Title
Digital signal processing and artificial intelligence for the automated
classification of food allergy
Author(s)
Twomey, Niall Joseph
Publication date
2013
Original citation
Twomey, N.J. 2013. Digital signal processing and artificial intelligence
for the automated classification of food allergy. PhD Thesis, University
College Cork.
Type of publication
Doctoral thesis
Rights
© 2013, Niall J. Twomey
http://creativecommons.org/licenses/by-nc-nd/3.0/
Embargo information No embargo required
Item downloaded
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http://hdl.handle.net/10468/1236
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Digital Signal Processing and Artificial
Intelligence for the Automated Classification
of Food Allergy
Niall Twomey
A thesis submitted to the National
University of Ireland in fulfillment
of the requirements for the Degree of
Doctor of Philosophy
Supervisor: Dr. William P. Marnane
Head of Department:
Prof. Nabeel Riza
Department of Electrical and Electronic Engineering,
National University of Ireland, Cork.
Abstract
A
S a by-product of the ‘information revolution’ which is currently unfolding, lifetimes of man (and indeed computer) hours are being allocated for the automated
and intelligent interpretation of data. This is particularly true in medical and clinical
settings, where research into machine-assisted diagnosis of physiological conditions gains
momentum daily. Of the conditions which have been addressed, however, automated
classification of allergy has not been investigated, even though the numbers of allergic
persons are rising, and undiagnosed allergies are most likely to elicit fatal consequences.
On the basis of the observations of allergists who conduct oral food challenges (OFCs),
activity-based analyses of allergy tests were performed. Algorithms were investigated
and validated by a pilot study which verified that accelerometer-based inquiry of human
movements is particularly well-suited for objective appraisal of activity. However, when
these analyses were applied to OFCs, accelerometer-based investigations were found
to provide very poor separation between allergic and non-allergic persons, and it was
concluded that the avenues explored in this thesis are inadequate for the classification
of allergy.
Heart rate variability (HRV) analysis is known to provide very significant diagnostic
information for many conditions. Owing to this, electrocardiograms (ECGs) were recorded
during OFCs for the purpose of assessing the effect that allergy induces on HRV features. It
was found that with appropriate analysis, excellent separation between allergic and nonallergic subjects can be obtained. These results were, however, obtained with manual QRS
annotations, and these are not a viable methodology for real-time diagnostic applications.
Even so, this was the first work which has categorically correlated changes in HRV features
to the onset of allergic events, and manual annotations yield undeniable affirmation of
this.
Niall Twomey
Chapter 0:
Fostered by the successful results which were obtained with manual classifications,
automatic QRS detection algorithms were investigated to facilitate the fully automated
classification of allergy.
The results which were obtained by this process are very
promising. Most importantly, the work that is presented in this thesis did not obtain any
false positive classifications. This is a most desirable result for OFC classification, as it
allows complete confidence to be attributed to classifications of allergy. Furthermore,
these results could be particularly advantageous in clinical settings, as machine-based
classification can detect the onset of allergy which can allow for early termination of OFCs.
Consequently, machine-based monitoring of OFCs has in this work been shown to possess
the capacity to significantly and safely advance the current state of clinical art of allergy
diagnosis.
ii
Acknowledgements
I must first and foremost thank and acknowledge my supervisor, Dr. Liam Marnane, for
taking me on in this project and for his advice during my incarceration in UCC. I would
also like to thank the Irish Research Council, the Tril Centre and Intel for funding this
research.
Next, I would like to thank my internal and external examiners, Dr. Bill Wright and Dr.
Fernando Schlindwein, for a surprisingly enjoyable viva.
I would like to thank Prof. Jonathan O’B Hourihane, Deirdre Daly and Claire Cullinane
from the Department of Paediatrics and Child Health for their patience and support
during my time recording the data which forms the basis of this thesis. Your feedback,
patient answers and advice was very much appreciated!
I would also like to extend my thanks to Stephen Faul and Andrey Temko for their
guidance in all aspects of signal processing and machine learning, and for hitting me over
the ear when I deserved it. I would like to also thank all of the postgraduate students
that I came to know during the course of my research; thank you all for your company,
friendship and especially for the caffeine and card game indulgences during the years;
they kept me relatively sane.
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Chapter 0:
Finally, I must also thank my friends and family for your constant support and throughout
this time, specifically Jen and Chengde. Sincerely, I appreciate your consistent encouragement, sporadic berating and occasional willingness to seem interested in my research more
than I can say.
iv
Statement of Originality
I hereby declare that this submission is my own work and that, to the best of my knowledge
and belief, it contains no material previously published or written by another person
nor material which to a substantial extent has been accepted for the award of any other
degree or diploma of a university or other institute of higher learning, except where due
acknowledgement is made in the text.
Niall Twomey
September, 2013
v
Contents
1 Allergy and Allergic Reactions
1
1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1
1.2 Allergy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2
1.2.1
1.2.2
1.2.3
Varieties and symptoms of allergy . . . . . . . . . . . . . . . . . . . .
2
1.2.1.1
Variety . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2
1.2.1.2
Symptoms . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3
Management and treatment of allergic reactions . . . . . . . . . . . .
4
1.2.2.1
Mild reactions
. . . . . . . . . . . . . . . . . . . . . . . . . .
5
1.2.2.2
Severe reactions . . . . . . . . . . . . . . . . . . . . . . . . . .
5
Risk factors, and quality of life . . . . . . . . . . . . . . . . . . . . . .
6
1.2.3.1
Risk and protection factors . . . . . . . . . . . . . . . . . . .
6
1.2.3.2
Quality of life . . . . . . . . . . . . . . . . . . . . . . . . . . .
7
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1.3 Requirement for clinical diagnosis of allergy . . . . . . . . . . . . . . . . . . .
8
1.4 Diagnosis of allergy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9
1.4.1
Blood testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
9
1.4.2
Skin testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
1.4.3
Challenge testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
1.4.3.1
Preliminary tests . . . . . . . . . . . . . . . . . . . . . . . . . 11
1.4.3.2
Checkup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
1.4.3.3
Observation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
1.4.3.4
Failure protocol . . . . . . . . . . . . . . . . . . . . . . . . . . 14
1.4.3.5
Supplementary stages . . . . . . . . . . . . . . . . . . . . . . 14
1.5 Challenge-testing clinical experience . . . . . . . . . . . . . . . . . . . . . . . 15
1.6 Machine-assisted classification of allergy . . . . . . . . . . . . . . . . . . . . . 16
1.7 Layout of this thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2 Algorithms, methods and data collection
19
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.2 Activity analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.2.1
Introduction to inertial measurement . . . . . . . . . . . . . . . . . . . 20
2.2.2
Applications of inertial sensing . . . . . . . . . . . . . . . . . . . . . . 22
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Chapter 0:
2.2.2.1
2.2.3
Activity recognition . . . . . . . . . . . . . . . . . . . . . . . 23
Activity-based analysis of OFC . . . . . . . . . . . . . . . . . . . . . . 25
2.3 Heart rate analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
2.3.1
History and introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 26
2.3.2
Applications of HRV analysis . . . . . . . . . . . . . . . . . . . . . . . 28
2.4 Machine learning and classification . . . . . . . . . . . . . . . . . . . . . . . . 30
2.4.1
Introduction to classification . . . . . . . . . . . . . . . . . . . . . . . . 30
2.4.2
Background to machine learning . . . . . . . . . . . . . . . . . . . . . 30
2.4.3
Novelty detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
2.4.4
2.4.3.1
One class SVMs and GMMs . . . . . . . . . . . . . . . . . . . 34
2.4.3.2
Example of novelty detection . . . . . . . . . . . . . . . . . . 35
Applications of novelty detection . . . . . . . . . . . . . . . . . . . . . 37
2.5 Food challenge data collection . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
2.5.1
Recording platform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
2.5.2
Integration with oral food challenge . . . . . . . . . . . . . . . . . . . 39
2.5.3
Other data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
2.5.4
Data recording . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41
2.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
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3 Accelerometer-based analysis of oral food challenges
44
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
3.2 Accelerometer-based activity analysis . . . . . . . . . . . . . . . . . . . . . . . 47
3.2.1
Activity metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
3.2.2
Energy expenditure estimation algorithms . . . . . . . . . . . . . . . . 49
3.2.2.1
A note on the energy expenditure estimation algorithms . . 50
3.2.2.2
Bouten et al . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
3.2.2.3
Chen et al . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
3.2.2.4
Crouter et al . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
3.3 Energy expenditure validation . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
3.3.1
Experimental setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
3.3.2
Pre-processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
3.3.2.1
Codeword conversion . . . . . . . . . . . . . . . . . . . . . . 59
3.3.2.2
Breath and acceleration synchronisation . . . . . . . . . . . . 62
3.3.2.3
Normalisation . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
3.3.3
Performance evaluation
. . . . . . . . . . . . . . . . . . . . . . . . . . 63
3.3.4
Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
3.3.5
Discussion on energy expenditure estimation algorithms . . . . . . . 66
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3.3.6
Chapter 0:
Conclusion on energy expenditure estimation . . . . . . . . . . . . . . 67
3.4 Accelerometer-based analysis during OFCs . . . . . . . . . . . . . . . . . . . . 67
3.5 Probability density functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
3.6 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
3.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
4 ECG-based analysis of OFCs
73
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
4.2 ECG and HRV . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
4.2.1
ECG recording . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
4.3 HRV feature extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
4.3.1
Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
4.3.2
Epochs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
4.3.3
Epoch overlap . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
4.4 Feature normalisation/calibration . . . . . . . . . . . . . . . . . . . . . . . . . 77
4.5 HRV feature categories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
4.5.1
Feature categories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
4.5.2
Frequency domain feature analysis . . . . . . . . . . . . . . . . . . . . 80
4.5.3
Resampling + FFT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
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4.5.4
Direct PSD estimation of HRV . . . . . . . . . . . . . . . . . . . . . . . 82
4.5.5
Comparison of HRV frequency analysis methods . . . . . . . . . . . . 85
4.6 Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
4.6.1
Notation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
4.6.2
Time domain features . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
4.6.2.1
Mean heart rate . . . . . . . . . . . . . . . . . . . . . . . . . . 87
4.6.2.2
Standard deviation . . . . . . . . . . . . . . . . . . . . . . . . 88
4.6.2.3
Coefficient of variation . . . . . . . . . . . . . . . . . . . . . . 88
4.6.2.4
RMSSD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
4.6.2.5
NN/PNN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
4.6.2.6
Histogram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
4.6.3
Sequential domain features . . . . . . . . . . . . . . . . . . . . . . . . 94
4.6.4
Poincaré features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95
4.6.5
Frequency domain features . . . . . . . . . . . . . . . . . . . . . . . . 98
4.7 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
5 Machine learning for allergy classification
102
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
5.2 Novelty detection for OFC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
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5.2.1
Choice of classification routine . . . . . . . . . . . . . . . . . . . . . . 103
5.2.2
Feature transformation . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
5.2.3
Gaussian mixture models . . . . . . . . . . . . . . . . . . . . . . . . . 106
5.2.4
5.2.3.1
k-means clustering . . . . . . . . . . . . . . . . . . . . . . . . 108
5.2.3.2
Expectation maximisation . . . . . . . . . . . . . . . . . . . . 109
Postprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112
5.3 Classification procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114
5.4 Classifier model selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
5.4.1
Performance evaluation
. . . . . . . . . . . . . . . . . . . . . . . . . . 116
5.4.2
Parameter selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
5.4.2.1
Search space
. . . . . . . . . . . . . . . . . . . . . . . . . . . 117
5.4.2.2
Cost function . . . . . . . . . . . . . . . . . . . . . . . . . . . 118
5.5 Classification metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119
5.5.1
Sensitivity/specificity . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119
5.5.2
Time gain parameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120
5.5.2.1
Time gain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120
5.5.2.2
Doses saved . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121
5.5.2.3
Activation percentage . . . . . . . . . . . . . . . . . . . . . . 121
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5.6 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122
5.6.1
A brief note on the structure of these results . . . . . . . . . . . . . . . 122
5.6.2
Results obtained at epoch length of 60 seconds . . . . . . . . . . . . . 123
5.6.3
Overall results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125
5.6.4
Inconsistent classification at different epoch lengths . . . . . . . . . . 125
5.6.5
5.6.4.1
Short-duration signatures of allergy . . . . . . . . . . . . . . 125
5.6.4.2
Longer signatures of allergy . . . . . . . . . . . . . . . . . . . 127
5.6.4.3
Tolerance to non-allergic variances . . . . . . . . . . . . . . . 127
Boosted allergy classification . . . . . . . . . . . . . . . . . . . . . . . 131
5.6.5.1
Sensitivity/specificity . . . . . . . . . . . . . . . . . . . . . . 131
5.6.5.2
Time gain parameters . . . . . . . . . . . . . . . . . . . . . . 133
5.7 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135
5.7.1
Specificity of OFC classification . . . . . . . . . . . . . . . . . . . . . . 135
5.7.2
Robust classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136
5.7.3
Parameter selection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138
5.7.4
5.7.3.1
Importance of correct parameter selection . . . . . . . . . . 138
5.7.3.2
Alternative parameter selection . . . . . . . . . . . . . . . . . 139
Role of classification in OFCs . . . . . . . . . . . . . . . . . . . . . . . 140
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6 Automatic QRS detection
142
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142
6.2 QRS detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142
6.2.1
QRS validation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143
6.2.2
Validation databases . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144
6.2.3
Sensitivity and positive predictivity
6.2.4
Good detection window . . . . . . . . . . . . . . . . . . . . . . . . . . 146
6.2.5
Feature accuracy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146
6.2.6
Box-plots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147
. . . . . . . . . . . . . . . . . . . 145
6.3 Choice of QRS detector . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149
6.4 Hilbert transform based QRS detection . . . . . . . . . . . . . . . . . . . . . . 150
6.4.1
Theory of Hilbert transform . . . . . . . . . . . . . . . . . . . . . . . . 150
6.4.2
Method of QRS detection with Hilbert transform . . . . . . . . . . . . 152
6.4.3
Beat identification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155
6.5 Filter-banks based QRS detection . . . . . . . . . . . . . . . . . . . . . . . . . 156
6.5.1
Theory of filter banks . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156
6.5.2
QRS detection with filter banks . . . . . . . . . . . . . . . . . . . . . . 157
6.5.2.1
Pre-processing . . . . . . . . . . . . . . . . . . . . . . . . . . 157
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6.5.2.2
Beat-classification logic . . . . . . . . . . . . . . . . . . . . . 158
6.5.2.3
Overall . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163
6.6 Results obtained on MIT-BIH database . . . . . . . . . . . . . . . . . . . . . . 164
6.6.1
Sensitivity and positive predictivity
. . . . . . . . . . . . . . . . . . . 164
6.6.2
Percentage RMS difference . . . . . . . . . . . . . . . . . . . . . . . . . 166
6.6.3
Conclusions on QRS detection on MIT-BIH database . . . . . . . . . . 167
6.7 Requirement for artefact detection . . . . . . . . . . . . . . . . . . . . . . . . 167
6.7.1
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167
6.7.2
Artefact detection algorithm . . . . . . . . . . . . . . . . . . . . . . . . 168
6.7.3
Demonstration of artefact detection
. . . . . . . . . . . . . . . . . . . 169
6.8 Results obtained on allergy database . . . . . . . . . . . . . . . . . . . . . . . 171
6.8.1
Artefact detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171
6.8.2
Sensitivity and positive predictivity
6.8.3
Percentage RMS difference . . . . . . . . . . . . . . . . . . . . . . . . . 175
. . . . . . . . . . . . . . . . . . . 173
6.9 Overall discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 176
7 Fully automated allergy detection
178
7.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 178
7.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179
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7.3 Unmatched classification results . . . . . . . . . . . . . . . . . . . . . . . . . . 181
7.3.1
Results of unmatched classification . . . . . . . . . . . . . . . . . . . . 181
7.3.2
Discussion on unmatched classification results . . . . . . . . . . . . . 182
7.4 Matched classification results . . . . . . . . . . . . . . . . . . . . . . . . . . . 183
7.4.1
Sensitivity and specificity . . . . . . . . . . . . . . . . . . . . . . . . . 183
7.4.2
Artefact detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185
7.4.3
Time gain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187
7.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 188
7.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189
8 Overall summary, final conclusions and future work
191
8.1 Summary of this thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191
8.2 Primary contribution of this thesis . . . . . . . . . . . . . . . . . . . . . . . . 194
8.3 Possible avenues of future work . . . . . . . . . . . . . . . . . . . . . . . . . . 195
8.3.1
Data collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195
8.3.2
Alternative novelty detectors . . . . . . . . . . . . . . . . . . . . . . . 196
8.3.3
Real-time and portable implementation . . . . . . . . . . . . . . . . . 197
8.3.4
Feature and epoch analysis . . . . . . . . . . . . . . . . . . . . . . . . . 197
8.4 Publications resulting from this work . . . . . . . . . . . . . . . . . . . . . . . 198
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Appendices
201
A Alternative parameter selection routines
201
A.1 Introduction and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201
A.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204
A.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 206
A.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 206
B Investigation into the importance of features
208
B.1 Introduction and methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 208
B.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 210
B.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 212
B.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 214
References
215
xvii
List of Tables
2.1 Tabulation of the characteristics of the subjects who were recorded for this
study. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42
3.1 MET activity and corrected MET activity values. . . . . . . . . . . . . . . . . 56
3.2 Tabulation of the physical characteristics of the subjects who participated
in the accelerometer-based energy expenditure validation test. . . . . . . . . 58
3.3 Table of PRD values computed between true energy expenditure and
the estimated energy expenditure values obtained from the algorithms
investigated. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
4.1 Table of HRV diagnostic frequency ranges for children. . . . . . . . . . . . . . 98
5.1 Classification results obtained with the novelty detection routine at epoch
length of 60 seconds. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124
5.2 Tabulation of the classification results of the allergic subjects where ‘1’
represents an allergic classification (TP) whereas ‘0’ represents a nonallergic classification (FN). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132
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5.3 Classification result, time gain, doses saved and activation percentages
obtained by the classification routine.
The results in this table were
obtained by fusing the results obtained for the individual epoch lengths
together. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133
6.1 Differences in reported and calculated sensitivity and positive predictivity. . 164
6.2 Sensitivity and positive predictivity of QRS detectors on allergy database. . . 173
6.3 Distribution of mean and standard deviation of the PRD values calculated
from automatically extracted QRS points. . . . . . . . . . . . . . . . . . . . . 176
7.1 Sensitivity and specificity of classification results obtained with the manual
classification models on the automatically extracted HRV features (i.e.
crossover classification results). . . . . . . . . . . . . . . . . . . . . . . . . . . 181
7.2 Sensitivity and specificity of classification results obtained by Afonso’s and
the Hilbert transform QRS detectors. . . . . . . . . . . . . . . . . . . . . . . . 183
7.3 Specific time gain metrics obtained from fully automatic allergy classification based on Afonso and Hilbert transform QRS detectors. . . . . . . . . . . 188
A.1 Tabulation of sensitivity, specificity, and the time gain metrics which were
obtained by selecting the mean, median and mode of the set of postprocessing parameters from the training data. In the case of the mean
method, imperfect specificity was obtained. . . . . . . . . . . . . . . . . . . . 205
B.1 Classification metrics which were obtained with the time–, frequency–,
Poincaré– and sequential-domain classification models ranked by order of
importance of the feature category in question. . . . . . . . . . . . . . . . . . 210
xix
List of Acronyms
ADC
analogue to digital converter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
ANS
autonomic nervous system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
BP
band-pass . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168
BPM
beats per minute . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
BW
bandwidth . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157
CPET
cardio pulmonary exercise testing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
CE
European conformity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
CSI
cardiac sympathetic index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
CVI
cardiac vagal index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
DFT
discrete Fourier transform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
DoF
degrees of freedom . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
DSP
digital signal processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
ECG
electrocardiogram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
EE
energy expenditure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
c
EE
energy expenditure estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
EEact
energy expenditure due to activity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
cact
EE
energy expenditure estimation due to activity . . . . . . . . . . . . . . . . . . . . 51
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Section LIST OF TABLES
EEtrue
true energy expenditure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
EEG
electroencephalogram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
EM
expectation maximisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109
Epi-pen
epinephrine pen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
FFT
fast Fourier transform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
FIR
finite impulse response . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152
FN
false negative . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120
FP
false positive . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120
FT
Fourier transform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
GMM
Gaussian mixture model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
HF
high frequency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
HR
heart rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
HRV
heart rate variability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
IAA
integral of absolute acceleration. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .47
IAAt
total integral of absolute acceleration . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
IAAx
integral of absolute acceleration in the x-axis . . . . . . . . . . . . . . . . . . . . . 47
IAAy
integral of absolute acceleration in the y-axis . . . . . . . . . . . . . . . . . . . . . 47
IAAz
integral of absolute acceleration in the z-axis . . . . . . . . . . . . . . . . . . . . . 47
IIR
infinite impulse response . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156
IQR
inter-quartile range . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148
KDE
kernel density estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
km/h
kilometers per hour . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
kNN
k-nearest neighbours . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
LF
low frequency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
LOO
leave-one-out . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
MEMS
micro-electro-mechanical systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
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MET
metabolic equivalents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
MIT-BIH
Massachusetts Institute of Technology Beth Israel Hospital . . . . . 145
nesC
network embedded systems C. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .38
OFC
oral food challenge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
PCA
principal component analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
PDF
probability density function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
+P
positive predictivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146
PRD
percentage root mean square difference . . . . . . . . . . . . . . . . . . . . . . . . . . 63
PSD
power spectral density . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
REE
resting energy expenditure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
RMR
resting metabolic rate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
RMS
root mean square . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
RMSE
root mean square error. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .63
RMSSD
root mean square of successive difference . . . . . . . . . . . . . . . . . . . . . . . . 90
SHIMMER device
sensing health with intelligence, modularity, mobility and
experimental reusability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
SNR
signal to noise ratio . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157
SVM
support vector machine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
TGS
specific time gain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120
TGT
total time gain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120
TN
true negative . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120
TP
true positive . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120
ULF
ultra-low frequency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
VLF
very low frequency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
xxii
List of Figures
1.1 Photograph of an adrenaline pen (theonlineallergist.com, 2013). . . . . . . .
6
1.2 Oral food challenge flowchart which presents the means by which allergy is
diagnosed in a clinical environment. . . . . . . . . . . . . . . . . . . . . . . . 12
2.1 Illustration of the approximate growth of microelectromechanical systems
accelerometers, gyroscopes and magnetometers in the research market over
the past decade. (Google Inc., 2013). . . . . . . . . . . . . . . . . . . . . . . . 21
2.2 Functional diagram of a MEMS accelerometer, identifying the mass and
spring. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21
2.3 The original galvanometer developed by Willem Einthoven to record the
ECG in the early 20th century. . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
2.4 Labelled ECG waveform (Dublin Institute of Technology, 2013). . . . . . . . 28
2.5 Example of how classification is obtained with two-dimensional data (top)
with SVM based classification (bottom left) and GMM based classification
(bottom right).
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
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2.6 Example of how novelty detection algorithms can be employed to determine
novel and normal data points. The upper image shows the distribution of
the labels, and the lower-right figure shows one-class SVM while the lowerleft figure shows the example of GMM-based novelty detection on the same
data.
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
2.7 Diagram of the SHIMMER device, with the various components annotated
(reproduced with permission from SHIMMER-research (2010)). . . . . . . . . 39
2.8 Modified oral food challenge flowchart which is employed to accommodate
the introduction of the SHIMMER monitoring device for data collection
during OFCs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
3.1 The x (anterio-posterior), y (medio-lateral), and z (vertical) acceleration
directions in relation to a body. . . . . . . . . . . . . . . . . . . . . . . . . . . 47
3.2 Demonstration of the conversion process from the raw digital codewords
obtained from the accelerometer (a) to a measurement of absolute gravity (b). 60
c and EEtrue values obtained for participant 2. The values obtained
3.3 The EE
are overlaid. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
3.4 Example histogram and probability density function of a feature. . . . . . . . 68
3.5 Illustration of the differences between PDFs that describe two separate
classes of data. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
3.6 Histograms plotting the normalised IAA values of the allergic and nonallergic subjects who were investigated.
. . . . . . . . . . . . . . . . . . . . . 70
4.1 Einthoven triangle configuration for ECG electrode placement (University
of Nottingham, 2013). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
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4.2 Illustration of relationship between the ECG and the epoch length for ECG
recorded in OFC. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
4.3 Illustration of relationship between the ECG, the epoch length and the
epoch overlap. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
4.4 Illustration of the raw HR (⇤) which is not periodically sampled, and the
HR re-sampled to 10 Hz via cubic spline interpolation. . . . . . . . . . . . . . 81
4.5 PDF of mean heart rate, generated from allergic and non-allergic subjects . . 88
4.6 PDF of standard deviation of the heart rate, generated from allergic and
non-allergic subjects. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
4.7 PDF of coefficient of variation of the heart rate, generated from allergic and
non-allergic subjects. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
4.8 PDF of RMSSD of the heart rate, generated from allergic and non-allergic
subjects. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
4.9 PDF of PNN50 of the heart rate, generated from allergic and non-allergic
subjects. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
4.10 Histogram of the relative times between successive QRS complexes. . . . . . 92
4.11 PDF of histogram of the heart rate, generated from allergic and non-allergic
subjects. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
4.12 Chart of the change between successive QRS complexes. . . . . . . . . . . . . 94
4.13 PDFs derived for the sequential domain features. . . . . . . . . . . . . . . . . 95
4.14 Original and rotated points plotted in a Poincaré Chart. . . . . . . . . . . . . 96
4.15 CSI and CVI PDF from Poincaré features. . . . . . . . . . . . . . . . . . . . . 97
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4.16 PDF of the frequency domain features. . . . . . . . . . . . . . . . . . . . . . . 99
5.1 An illustration of PCA in two-dimensional feature space (subplot a) and
two-dimensional component-space (subplot b). . . . . . . . . . . . . . . . . . 105
5.2 A mixture of three equally-weighted Gaussians (dashed lines) which combine to represent a multi-modal non-normal distribution (solid black line). . 107
5.3 A demonstration of the difference in clustering which is obtained by
k-means clustering and the expectation maximisation algorithm.
With
subfigures B and C, a line is drawn from each point to its associated cluster. . 110
5.4 Sample likelihood (subplot a) and histogram of the background likelihood
(subplot b) of Subject 23. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112
5.5 Flowchart of classification procedure involving the recording of ECG,
annotation of QRS complexes, feature extraction and the classification
procedure of OFC. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114
5.6 Illustration of the data segmentation and testing routines employed in the
allergy classification procedure. . . . . . . . . . . . . . . . . . . . . . . . . . . 115
5.7 The confusion matrix showing how sensitivity and specificity are obtained
with regard to the ground truth (diagnosis) and predicted (classification)
results. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119
5.8 A demonstration of early detection of allergy (with Subject 11).
The
segments at 45, 60 and 80 minutes which fall beneath the threshold were
classified as allergy. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123
5.9 Example demonstrating how the generated likelihood for Subject 2 satisfies
the allergy criteria at an epoch length of 60 seconds (subplot a) while failing
to do so for epoch lengths of 120, 180 and 300 seconds (subplots b — d). . . 126
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Section LIST OF FIGURES
5.10 Example demonstrating how the generated likelihood for Subject 13 does
not satisfy the allergy criteria at an epoch length of 60, 120 and 180 seconds
(subplot a — c) but the criteria are then met for the epoch length of and 300
seconds (subplots d). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128
5.11 Example demonstrating how the generated likelihood for Subject 16 surpasses the threshold, but does not satisfy the allergy criteria due to the
inclusion of the duration parameter and for all epoch lengths the subject
is correctly classified as non-allergic. . . . . . . . . . . . . . . . . . . . . . . . 130
5.12 Example of arrhythmia on the ECG trace (a) and the effect this has on the
heart rate (b) on Subject 20. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137
5.13 The likelihood series chosen for Subject 2 which does not diverge from the
background level significantly enough to classify allergy. PCA preserved
80% of the feature variance which was modelled with a GMM order of 32 at
an epoch length of 60 seconds. . . . . . . . . . . . . . . . . . . . . . . . . . . . 139
6.1 Relationship between a box-plot, and quartile ranges with a normal distribution. The locations marked Ql and Qu are the lower and upper quartiles
respectively, and the median is marked as m.
. . . . . . . . . . . . . . . . . . 148
6.2 The real and imaginary components resulting from the Hilbert Transform
of the ECG. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152
6.3 The flowchart for QRS detection from the Hilbert Transform. . . . . . . . . . 153
6.4 The stages employed by the Hilbert Transform QRS detection algorithm (the
ECG data was obtained from Patient 113 in the MIT-BIH Database). . . . . . 154
6.5 The generic filter banks flow chart incorporating both bandpass and synthesis filters. The # and " symbols represent down– and up-sampling
respectively. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156
xxvii
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Chapter 0:
6.6 The idealised filter response of the filter banks, with M equally-wide subbands. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 156
6.7 Overall simplified flowchart of Afonso’s QRS detection method. . . . . . . . 159
6.8 The effect of the various QRS validation levels from Afonso’s QRS detection
algorithm. In these charts, the ◦ symbols represent the candidate QRS
points. Charts b — d have been down-sampled.
. . . . . . . . . . . . . . . . 161
6.9 Incorrect QRS complex localisation (Patient 8 of MIT-BIH arrhythmia
database). Manual QRS annotations are marked with ⇥ and automatic
detections are marked with ⇤ (Hilbert transform algorithm) and ◦ (Afonso’s
algorithm). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165
6.10 Sensitivity and positive predictivity box-plots of QRS detection on the
MIT-BIH arrhythmia database. . . . . . . . . . . . . . . . . . . . . . . . . . . . 166
6.11 PRD box-plots of the mean (µ) and standard deviation (σ) of the heart rate
over all subjects in the MIT-BIH arrhythmia database. . . . . . . . . . . . . . 167
6.12 Normalised output of high-frequency (a,b) and low-frequency (c,d) energy
estimators for artefact detection (data from Subject 8 of allergy database). . . 170
6.13 The breakdown of the number of artefact events which were detected by the
artefact detection algorithm for each subject of the allergy database. . . . . . 172
6.14 Sensitivity and positive predictivity box-plots of QRS detection on the
allergy database. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174
6.15 Example of poor quality of the ECG signal after the application of denoising
filters which contributed to poor sensitivity and positive predictivity values
for Subject 3. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175
xxviii
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Section LIST OF FIGURES
6.16 Boxplots of the PRD of the mean and standard deviation of the heart rate
between the manual and automatic QRS points extracted. . . . . . . . . . . . 176
7.1 The flow of how the matched (right) and unmatched (left) classification
results are obtained.
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179
7.2 Likelihood plots of Subject 1 for manual and automatic models at epoch
lengths of 60 seconds. Subplots (a) — (c) show the likelihoods which were
obtained with manual, Afonso and Hilbert models respectively. In all cases
the threshold for allergy classification is off the scope of the figures. . . . . . 184
7.3 Likelihood plots of Subject 7.
Subplot (a) shows the threshold which
was computed without the aid of artefact detection and how allergy
classification does not classify allergy. Subplot (b) shows the threshold
which was computed when artefact detection was incorporated and how
allergy classification is successful with artefact-aware classification. . . . . . 186
A.1 The estimated distribution of duration and multiplicative post-processing
parameters which achieve 100% specificity and maximum sensitivity on the
training dataset. The image is limited to d and n parameters of 75. The
darker regions indicate a higher density of suitable parameters.
xxix
. . . . . . . 203
CHAPTER 1
Allergy and Allergic Reactions
1.1
A
Introduction
LLERGY is defined as an abnormally high acquired sensitivity towards certain
substances (Dorland, 1901). To the vast majority of the population allergens are
harmless and do not interfere with their day-to-day living. However, for those who suffer
from allergy, allergens can provoke acquired, predictable and rapid conditions which are
called allergic reactions, and in some cases, these can be fatal.
This chapter sets the background to the clinical knowledge of allergy and allergic reactions
and it also introduces the scope of this thesis. The chapter focuses on the varieties,
symptoms, diagnoses and treatment of food-based allergy. The possibility of automated
allergy detection for real-time clinical allergy testing environments is then explored and
the benefits which this type of analysis could provide are also introduced.
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1.2
Chapter 1:
Allergy
Approximately 25 — 30% of people believe that they suffer from allergy (Miles et al.,
2005), but it is estimated that only 6% of schoolchildren (Bock and Sampson, 1994) and
2.3% of adolescents (Pereira et al., 2005) suffer from food allergy. The reason for this
disparity between perceived and true numbers is due to a lack of public understanding
of allergy, and also because in most instances, suspicions of allergy are not confirmed
diagnostically.
1.2.1
Varieties and symptoms of allergy
While allergies can come in many varieties, it is only diagnosed if the symptoms present
in a predictable manner.
1.2.1.1
Variety
A range of different types of allergy exist, and the primary varieties are listed here.
1. Food allergy:
Food allergy is caused by an adverse immune response against a food-type. In
some cases, when a sufferer is very sensitive to the allergen, physical contact with
the food-type is sufficient to provoke the symptoms of an allergic reaction. The
majority of reactions occur after ingestion of the food. The quantity of food required
to provoke a reaction differs from person-to-person, and it can be thought of as a
specific threshold.
Typical food allergens include:
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Section 1.2: Allergy
• Milk
• Egg
• Wheat
• Peanut
• Soy
• Cooked egg
2. Environmental allergy:
Allergic reactions can be provoked by pets, poor hygiene and insects. Reactions to
environmental allergens can manifest due to the presence of the allergen in the air
(dust mites, mold spores) or by physical contact (rubbing cat/dog, insect stings etc).
The length of exposure required to provoke a reaction differs from person-to-person.
Environmental allergens include:
• Cats
• Dust mites
• Mould
• Dogs
• Pollen
• Insect sting/bites
3. Drug/medication allergy:
Allergic reactions can also be provoked by the administration of medication. The
reaction manifests when the drug enters the sufferer’s blood stream. These allergies
can be very dangerous if the sufferer or medical staff are unaware of them. Often,
allergies to medical drugs are due to dyes and stabilising agents found in the drug
packaging rather than the drug itself. However, due to international regulation,
colour coding is required to reduce counterfeiting.
Drug allergens include:
• Penicillin
1.2.1.2
• Anaesthetics
Symptoms
The severity of the symptoms that the allergy sufferers encounter depends on the
individual subject’s susceptibility to the allergen and on the amount of the allergen that
they have come into contact with. For subjects with severe sensitivities, an allergic reaction
may be provoked simply by contact between the subject’s skin and the allergen. For less
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Chapter 1:
sensitive subjects, oral consumption of the food type is required in order to provoke a
reaction.
The most dangerous of the symptoms of allergy involve restrictions of the airway
(wheezing, shortness of breath and asthma attacks) and anaphylaxis. Anaphylaxis is an
acute allergic reaction towards an allergen, and 3 — 15% of those who suffer from allergies
will be afflicted with an anaphylactic reaction at least once (Matasar and Neugut, 2003;
Yanishevsky and Hourihane, 2010; Järvinen et al., 2009), indicating that there is a very real
risk of a severe reaction amongst sufferers of allergy. Interestingly, there is no universally
accepted definition of anaphylaxis, and there is also disagreement about the criteria for
the diagnosis of anaphylactic events (Sampson et al., 2006). In order to protect against
reactions, sufferers of allergy must always be constantly vigilant and be equipped to treat
and manage allergic reactions.
Symptoms of allergy can include:
• Hives
• Red eyes
• Vomiting
• Rashes
• Sneezing
• Diarrhoea
• Swelling
• Bloating
• Wheezing
• Running nose
• Mood change
• Shortness of breath
• Sinus congestion
• Abdominal pain
• Asthma attacks
• Itchy eyes
• Ear pain
• Anaphylaxis
1.2.2
Management and treatment of allergic reactions
The best means of prevention of allergy is complete avoidance (Pereira et al., 2005; Sicherer
and Sampson, 2006). For the cases of animal allergy, subjects are strongly encouraged
not to have a pet in the home and to avoid them elsewhere. With some environmental
4
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Section 1.2: Allergy
allergens, such as dust mites, a high degree of hygiene and cleanliness can assist in limiting
the exposure to a subject, lowering their overall vulnerability to the allergen.
However, it is not possible to manage exposure to all environmental allergens, in the case
with pollen for example, and if a subject is allergic to these unavoidable environmental
allergens it is possible to self-administer precautionary antihistamines which will increase
tolerance towards these symptoms for a short period of time.
This precautionary treatment is only viable in the case of environmental allergens. With
food and chemical allergies, it is not appropriate to take medication on the chance of
coming into contact with the substances. In the case when a person reacts to a food type,
insect bite, etc, the means of countering the reaction depends of the extent of the reaction.
1.2.2.1
Mild reactions
For mild reactions (hives, rashes, sneezing, abdominal pain, vomiting, etc) off-the-shelf
antihistamines can be sufficient to stop the progression of a reaction. Zyrtec is commonly
used in home and hospital environments to halt mild allergic reactions. This is a secondgeneration antihistamine which does not pass the blood/brain barrier, so drowsiness is
not induced by consumption. Zyrtec can be taken in tablet or liquid form, and can begin
to take effect in 10 — 20 minutes. In 2008 Zyrtec was the highest-grossing non-food
product in the United States of America (Elliot, 2010), indicating the prevalence of allergy
internationally, and the chronic nature of the condition.
1.2.2.2
Severe reactions
For severe reactions (wheezing, shortness of breath, anaphylaxis) stronger rescue medications are required. Inhalers and epinephrine pens (Epi-pens) are used in these cases to
stop the reaction.
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Chapter 1:
Figure 1.1: Photograph of an adrenaline pen (theonlineallergist.com, 2013).
Epi-pens contain a dose of adrenaline⇤ in a sealed syringe-like container, see Figure 1.1.
If a subject requires adrenaline when in a state of anaphylaxis the top of the Epi-pen is
removed, revealing a needle. The needle is then inserted in the thigh of the subject for
ten seconds while the auto-injection mechanism releases the adrenaline into the subject’s
bloodstream. If the effects of the allergic reaction do not cease within ten minutes of
administration, a second dose should be administered. After use of the Epi-pen the subject
should go to the nearest hospital for a checkup and renewal of the Epi-pen prescriptions.
1.2.3
Risk factors, and quality of life
1.2.3.1
Risk and protection factors
Some researchers have demonstrated that a number of factors can increase or decrease
the likelihood of a person having allergic reactions. There are conflicting reports about
the effect of rural and urban localisation on extensivity of allergy. Some studies (BraunFahrlander et al., 1997; Kilpelinen et al., 2000; Radon et al., 2004; Almqvist et al., 2003)
report that children who have grown up on a farm have a lower frequency of allergy in
comparison with urban and less-rural children. In contrast with this, other researchers
were not able to find significant differences between rates of allergy based on rural and
urban localisation (Viinanen et al., 2007; Omenaas et al., 1994; Azpiri et al., 1999).
⇤ The terms adrenaline and epinephrine are synonyms for the same compound and are associated with
British and American English respectively. The term Epi-pen employs the American naming convention and
is adopted in this thesis because it is the most common term for the medical device.
6
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Section 1.2: Allergy
It has also been reported that breastfeeding (Rautava et al., 2002) and exposure to
household pets in a childhood home (Ahlbom et al., 1998) have been shown in some
studies to be protection factors against allergy, but other studies demonstrate otherwise
(Bergmann et al., 2002).
Allergy is therefore difficult to predict as a number of unknown dynamics determine a
person’s sensitivity to the allergens. It had been consistently reported, however, that
suffering from allergy is a risk factor towards other other diseases. Allergy is heavily
linked with asthma in childhood (Roberts et al., 2003; Black et al., 2000; Call et al., 1992),
eczema (Bryld et al., 2003), wheezing and bronchial hyperresponsiveness (Arshad et al.,
2005).
1.2.3.2
Quality of life
Allergy has a significant impact on the quality of life of its sufferers. In a quality of
life survey, young sufferers of allergy reported much greater fear and anxiety of their
condition in comparison to persons who suffer from other chronic diseases, such as
insulin dependant diabetes mellitus (Avery et al., 2003). Owing to the relative ease of
contamination, sufferers must live in a constant state of awareness towards their current
state of health, and towards everything they consume. They must also live at constant
risk of potential reactions and anaphylaxis, which increases stress and worry for the rest
of their lives. Indeed, sufferers of severe allergies are reminded of their affliction daily
because they must always carry Epi-pens in case of reactions.
Allergy affects the quality of life of more than just the sufferer and the quality of life
of their family members are also impaired. Limitations of family activities are reported
among suffering families (Sicherer et al., 2001), and in social situations the parents of
suffers report significantly more disruption than non-suffering families (Oude Elberink
et al., 2002). This is due to the persistent fear of the sudden death that follows sufferers of
allergy (Primeau et al., 2000).
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Chapter 1:
Adolescents are at the highest risk of death from anaphylaxis (Bock et al., 2007, 2001;
Sampson et al., 1992), but young children and adults are also in danger, with up to 1% of
these sufferers at mortal risk (Matasar and Neugut, 2003; Yanishevsky and Hourihane,
2010; Järvinen et al., 2009).
Without a confirmed diagnosis, a significant burden of anxiety is carried by suffering
families (Primeau et al., 2000), and remains until a diagnosis is verified. Positive and
negative diagnoses of allergy improve the quality of life of the family and individual
involved, as, even with diagnosis of allergy, the burden of uncertainty is removed, and
the family can adapt to living with the situation (DunnGalvin et al., 2010).
1.3
Requirement for clinical diagnosis of allergy
It is very important for a subject to know if they are allergic to a substance. Precautionary,
but unsubstantiated, avoidance of the substance may be considered a safe option, but it can
also leave a subject poorly guarded against allergic events. In Ireland, for example, schoolgoing children suspected as being allergic to a food-type are provided with a prescription
for four Epi-pens, even before the diagnosis of allergy has been confirmed. One pair of
Epi-pens is kept by the guardians of the subject, and the second pair is kept in the subject’s
school.
While great efforts may be taken to avoid an allergen in the home environment, it is in
the unregulated environments that the subject is in greatest danger. For example at school
lunchtime, cross-contamination (introduction of allergen from secondary source through
contact) of food is common. If a subject has not been diagnosed (or is not waiting to be
diagnosed), he/she will not be able to obtain the Epi-pens required to treat severe allergic
reactions. They are, therefore, at the highest risk of sudden death as they are without
appropriate preventative medicine should anaphylaxis arise.
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Section 1.4: Diagnosis of allergy
For this reason, it is very important that subjects who might be at risk of allergy have an
early clinical diagnosis. Yet, a very high percentage of adults who believe themselves to be
allergic are not, and may therefore be needlessly avoiding food substances and impairing
their own quality of life and that of their family.
1.4
Diagnosis of allergy
Proof of a subject’s susceptibility towards an allergen can be obtained clinically in
controlled environments. Three types of test exist to assess the vulnerability and these
are discussed in the subsequent sections.
1.4.1
Blood testing
Bio-chemically, symptoms of allergic reactions occur as a result of antibodies stimulating
cells in such a way that allergic reactions manifest in a physical manner. Studies have
shown that subjects with higher levels of these antibodies have a higher probability of
suffering from allergy. However, high levels of these antibodies are not sufficient for a
diagnosis of allergy, and low concentrations are not sufficient to rule out allergy.
Blood tests can be considered as an overall ‘likelihood of allergy’ test. With a blood sample,
the plasma can be stimulated with a serum which contains a solution of a specific allergen.
By analysing the concentrations of antibodies which are present before and after the serum
has been introduced to the blood plasma, the likelihood of existing allergy towards the
allergen can be assessed. These tests require specific laboratory equipment and trained
personnel.
9
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1.4.2
Chapter 1:
Skin testing
Skin testing — also referred to as ‘puncture’, ‘scratch’ or ‘prick’ testing in relevant
literature — is a quick means of assessing a subject’s allergic susceptibility towards an
allergen. A list of potential allergens is drawn up based on a subject’s history. Samples
of each are then obtained, and these are mixed with deionised water, producing a waterbased solution of the allergen. Needles are then placed in the solution and these are used to
scratch the skin of the subject. As well as scratching the skin with potential allergens, the
skin is also scratched with a needle that was exposed only to deionised water, providing a
true-negative scratch.
If a subject is allergic to one of the substances, a reaction can occur around the area of the
scratch mark. The symptoms of the reaction can range from reddening, to inflammation,
to an outbreak of hives on the skin. The extent of the reaction is then measured. The larger
the response to the scratches with regard to the true negative, the more likely it is that the
subject is allergic to the allergen. While allergic reactions will not present on the true
negative scratch, the scratching process alone can introduce reddening (and sometimes
inflammation) that must be accounted for in the measurement of the other test scratches.
Reaction of the skin to suspected allergens is an indication of the subject’s susceptibility
towards the allergen, but, in the same manner as the blood tests, skin tests are not
conclusive for a diagnosis of allergy, even in light of a strong reaction to the scratches
(Sampson, 1999).
1.4.3
Challenge testing
The only clinically validated means of diagnosing food allergy is the oral food challenge
(OFC) (Yanishevsky and Hourihane, 2010; Järvinen et al., 2009). During the challenge
the subject is required to consume one age-appropriate portion of a food-type. This has
the potential to act as a medical poison for some subjects. The portion (e.g. one egg, a
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Section 1.4: Diagnosis of allergy
glass of milk, eight peanuts etc) is divided up into five sub-portions doubling in size from
approximately
1
32
of a portion to
1
2
of a portion. The smallest dose is always administered
first. In the case of peanuts, a supplementary sub-portion is introduced where a peanut
is rubbed on the subject’s lower lip. Depending on the sensitivity of the subject towards
peanuts, the lip may swell after this contact, and this is sufficient for diagnosis of allergy.
The flowchart for the oral food challenge (OFC) is shown in Figure 1.2, and every stage of
this figure will be discussed in this section.
If a subject is allergic to a food-type they might be able to consume a small quantity of the
food-type without reacting. The amount they must consume to provoke allergic symptoms
can be thought of as a ‘reaction threshold,’ so to induce a reaction requires this threshold
to be surpassed. Reactions will be more severe with the consumption of a greater amount
of the food. For the comfort and safety of the subjects who react during the food challenge,
it is desirable for the subject to consume the smallest amount possible.
If the subject consumes the full portion of the food-type they are being tested against they
are said to have ‘passed’ the test and may introduce the food-type into their diet. If a
reaction to the food-type occurs during the challenge the subject is said to have ‘failed’ the
test and the subject must avoid the allergen.
1.4.3.1
Preliminary tests
The subject arrives with their parent/guardian and they are admitted to the day ward.
Labelled skin tests are performed before the OFC begins.
The subjects are left for
fifteen minutes and if the skin has reacted to these allergens in this time, the size of
the reaction is measured and recorded. Depending on the extent of the reactions to the
solutions, supplementary food challenges may be scheduled. The food-type the subject
is being tested against will always be included in the skin test before the OFC, but oral
consumption is still required, regardless of the extent of reactions which are obtained
from the skin tests.
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Chapter 1:
Subject
arrives at
Hospital
Preliminary
tests
Observe
for 10 —
20 minutes
Checkup
Fail
Pass
Administer
Dose
Yes
Dose
Remaining?
Subject
diagnosed
‘allergic’
Fail
No
2hrs
Observation
Challenge
over
Pass
Subject
diagnosed
‘non-allergic’
Figure 1.2: Oral food challenge flowchart which presents the means by which allergy is
diagnosed in a clinical environment.
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Section 1.4: Diagnosis of allergy
If the subject has been sick over the preceding two weeks, or if the subject suffered from an
allergic reaction over the same time-period, the challenge will not proceed as the subject’s
immune system might be compromised, and the results of the OFC may be inconclusive.
1.4.3.2
Checkup
The subject is then given his/her first checkup by the allergists. The symptoms of allergic
reactions can manifest in an outbreak of hives and rashes on the skin, so it is important
to know of any existing skin blemishes a priori so they are not mistaken as the physical
manifestations of an allergic reaction in later checkups. Indeed, as there is a definite
link between allergy and dermatological conditions such as eczema (Bryld et al., 2003),
the subjects in question can present with many non-allergy related skin conditions which
might be mistaken for allergic rashes later. Therefore, a survey of these is required for
accurate diagnosis.
The subject’s heart rate, blood pressure, blood oxygen saturation and respiration rate are
measured and logged by the allergist. The first sub-portion of the suspect allergen is then
administered to the subject.
1.4.3.3
Observation
After the dose is administered the subject is observed from a nearby station by the nursing
staff for 10 — 20 minutes.
If the subject is thought to be reacting to the allergen during this waiting period, the
subject is given a checkup by the nursing staff. If the subject fails the checkup the subject
is not required to consume any more food, and the failure protocol (see next subsection)
will be followed. If the subject passes this intermediate checkup, the remainder of the
observation time is allowed to pass.
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Chapter 1:
When the observation period has completed, the subject is given another checkup. If
the physiological signals recorded from the subject during the checkup are within the
normal range for the subject’s age group, and if no manifestations of an allergic reaction
have been observed, the next largest dose is administered, and the subject is observed
from the observation station for another 10 — 20 minutes. The ‘checkup’, ‘sub-portion
administration’ and ‘observation’ sequence repeats until all sub-portions have been fully
consumed, or until the subject reacts to the food type.
1.4.3.4
Failure protocol
If, at any stage during the OFCs, a subject reacts negatively to the food-type, rescue
medications (i.e. antihistamines) can be administered. At this stage the subject will be
diagnosed as being allergic to the food-type, and the guardians of the subject are informed
about how to avoid the allergen and shown how to use Epi-pens in case of emergencies.
Typically the reactions are not severe, and symptoms such as sneezing, itchy eyes and hives
will present. In these situations the administration of Zyrtec is sufficient to halt the effects
of the reaction. After this time period subjects are monitored for two supplementary hours
as further allergic reactions can lay dormant for this length of time after the final dose
of allergen has been consumed. Further antihistamines are administered to the subject
during this time period if necessary. Approximately 1 — 3% of OFCs will require the
administration of adrenaline even under the close supervision of the allergists.
1.4.3.5
Supplementary stages
Many of the symptoms the allergists look for are subjective and differ from subject to
subject. For a definitive diagnosis, the allergist must continue to administer the allergen
until a reaction occurs. If the allergist is unsure about the cause of a symptom (if the
subject seems distressed it may be a result of restlessness rather than the onset of a
reaction) they will wait for another 10 minutes or repeat the size of the previous subportion as the specific cases require.
14
Section 1.5: Challenge-testing clinical experience
Niall Twomey
If the subject consumes all sub-portions of food without reacting, they are deemed to have
passed the food challenge. However, these subjects are also monitored for a further two
hours as delayed reactions can still manifest after the challenge has finished. If the subject
reacts during this time the failure protocol is followed and the subject is diagnosed allergic.
If a subject does not react during the waiting period, they are deemed to have passed the
food challenge.
1.5
Challenge-testing clinical experience
Over 600 OFCs have been performed by the Department of Paediatrics and Child Health
in Cork University Hospital. These controlled OFCs are monitored by staff who have been
trained to recognise changes in behaviour and changes in physiological signals which
are typical of a subject who has reacted to the food type they are being tested against.
However, even with the close supervision provided by the staff there have been seven
cases where administration of adrenaline was required.
The allergists who conducted the OFCs identified two events which are suggestive of
oncoming allergic reactions (Bindslev-Jensen et al., 2002). These are:
Change in activity: There is a tendency for the subject to become quiet, introverted and
still before the onset of a reaction (Bindslev-Jensen et al., 2002). The desire to play
disappears. Biologically this is due to inflammatory mediators of the cardiovascular
system compensating for the effects of histamine and other mediates of allergic
response. At this stage, the magnitude of the reaction is not yet sufficient to present
perceivable symptoms. This quieting state does not always transpire for subjects who
fail a food challenge as reactions can occur before the subject begins to feel unwell
depending on the subject’s susceptibilities to the allergen in question.
Change in heart rate: There is a general tendency for the heart rate of subjects to increase
as a result of a reaction (Bindslev-Jensen et al., 2002). This was observed during the
15
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Chapter 1:
checkups that the allergists performed on the subjects during the OFCs. A change in
heart rate during a checkup is one of the factors that is used to determine if a subject
is allergic to the food-type. However, a change alone is not sufficient to diagnose
a subject as being allergic as it has not been definitively linked to the presence of
allergy. In the presence of a likely allergic reaction, the absence of a change in heart
rate is not sufficient to discount allergy as this is a subjective measure which is only
recorded at twenty minute intervals.
Blood pressure and blood oxygen saturation are also measured during checkups. These
signals will be the last to change as a result of a reaction, and other symptoms of allergic
reactions will be observable before they change. However, if these signals exceed the
normal range for the subject’s demographics the test will immediately be halted and the
failure protocol will be followed.
1.6
Machine-assisted classification of allergy
Based on the observations which were discussed in the previous section, it was therefore
proposed to investigate the effectiveness of machine-assisted diagnosis of food challenge
monitoring. In this, the activity and heart rate of the subjects are monitored in a noninvasive and remote manner, and these are later interrogated in order to enquire into the
existence of signatures of allergy in the signals.
This monitoring should be performed in an unobtrusive manner without interfering
with the subjects, staff or the usual progression of challenges. With the recorded data,
characterisation of activity and heart rate will be performed with the goal of detecting,
predicting and classifying a subject as being ‘allergic’ or ‘non-allergic’. The classification
is designed to complement the allergists diagnoses during OFCs as the allergists cannot be
replaced. Therefore, classification must be ‘tuned’ in an objective manner to ensure that
no false positive classifications occur as this would introduce an unnecessary violation to
the subject’s quality of life indefinitely.
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Section 1.7: Layout of this thesis
Monitoring of OFCs carries the potential to introduce many advances to the current state
of the art. If a correlation between the measurements and the onset of allergy is discovered,
automatic classification introduces the possibility of early-detection of allergy. With this,
challenges could be stopped earlier, antihistamines could be administered sooner, and the
stress on the subject and their family during these tests could be significantly reduced by
suppressing the extent of the reaction. Indeed, with machine-assisted monitoring, realtime and non-invasive visualisation of the heart rate throughout the challenges could be
obtained. This would also introduce high-resolution clinical information with which the
allergists can make their diagnoses.
1.7
Layout of this thesis
For the purposes of this investigation, the remainder of this thesis takes the following
form:
Chapter 2 discusses and reviews the in more detail the algorithms which are employed
in later chapters and also provides an overview of the subjects whose data were
recorded during the OFC.
Chapter 3 discusses the collection of accelerometer and energy-expenditure data for the
purpose of validation of the algorithms which were employed to assess OFCs. This
chapter also presents the results obtained when employing these algorithms for the
purpose of classification of allergy.
Chapter 4 introduces the heart rate variability features which are extracted from the
electrocardiogram (ECG) data which were recorded during OFCs. This chapter also
plots probability density functions which demonstrate how effective the individual
features were at discriminating between allergic and non-allergic subjects.
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Chapter 1:
Chapter 5 discusses the machine learning algorithms which are employed for the classification of allergy. This chapter also presents the results which are obtained when
classification of allergy is performed based on manual QRS annotations of the ECG.
Chapter 6 discusses the concepts of automatic QRS extraction.
Two QRS detection
algorithms are discussed and are validated against online ECG databases. The
effectiveness of the algorithms is then assessed with the ECG recorded during OFCs.
Chapter 7 presents the results which are obtained by combining the capabilities of the
topics discussed in Chapters 4, 5 and 6 for the fully automated assessment of allergy
classification.
Chapter 8 first summarises the work presented in the following chapters. The main
contributions of this thesis are then presented and a number of directions that should
be investigated in future work are also listed.
18
CHAPTER 2
Algorithms, methods and data
collection
2.1
T
Introduction
HIS chapter provides an overview of the analytic techniques which will be employed
later in this thesis. In the previous chapter, it was stated that the allergists, who
oversee oral food challenges, observed that subjects who react to the food-type have a
tendency to become quiet and still, and it was also stated that there is a tendency for the
heart rate of the subject to change before allergic reactions.
In later chapters, accelerometer-based activity, and heart rate variability analysis will be
employed to objectively assess the extent to which these changes occur. However, as this
is the first work to investigate these signals during allergic events it is not possible to cite
review results from other researchers for allergy classification. Therefore, the state of the
19
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Chapter 2:
art of each of these disciplines is discussed separately in this chapter. Furthermore, a core
aspect of this thesis is also in regard to the utilisation of machine learning algorithms for
automated decision making. Classification algorithms are also introduced and discussed
in this chapter.
As this chapter discusses four separate areas (activity, heart rate, machine learning and
data collection) which are — for the sake of clarity in this chapter — independent, these
topics are discussed in isolation. Later, if chapters require cooperation between these
individual methods, the means by which they engage will be discussed.
Finally, this chapter will discuss the hardware which was employed to record data during
the OFCs, and the means by which this was integrated into the oral food challenge for data
collection is outlined.
2.2
Activity analysis
2.2.1
Introduction to inertial measurement
Inertial sensors sense acceleration.
Recently inertial sensors have been miniaturised
with the invention of micro-electro-mechanical systems (MEMS) fabrication technologies.
Inertial sensors can accurately sense acceleration in a number of axes (or degrees of
freedom (DoF)), and they can be employed to monitor complicated applications with good
accuracy.
The most common types of inertial sensors are accelerometer–, gyroscope– and magnetometerbased. These measure acceleration, angular velocity and magnetic fields (i.e. cardinal
orientation with regard to Earth’s magnetic poles) respectively. Individually, these sensors
will typically measure up to three DoFs. However, when combined together in similar
planes on one board, a 9-DoF sensor is obtained and these are capable of assessing full
kinematic mobility.
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Section 2.2: Activity analysis
6,000
# new publications
5,000
Accelerometer
Gyroscope
Magnetometer
4,000
3,000
2,000
1,000
0
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
Publication year
Figure 2.1: Illustration of the approximate growth of microelectromechanical systems
accelerometers, gyroscopes and magnetometers in the research market over the past
decade. (Google Inc., 2013).
Fixed plates
Applied
Acceleration
Spring
Mass
(a) Accelerometer experiencing
no external acceleration.
(b) Accelerometer experiencing
external acceleration.
Figure 2.2: Functional diagram of a MEMS accelerometer, identifying the mass and spring.
21
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Chapter 2:
Accelerometers are the most popular of these sensors both in production volumes and
research markets. Figure 2.1 shows the trends of new research articles published over
the past 10-years containing the phrases ‘MEMS accelerometers’, ‘MEMS gyroscopes’
and ‘MEMS magnetometers’ on Google Scholar (Google Inc., 2013). This chart shows
steady growth of research for each inertial sensor, but, in particular, the popularity of
accelerometers has outgrown the other inertial sensors.
Figure 2.2 shows a high-level functional diagram which demonstrates how MEMS accelerometers sense changes in acceleration. These devices consist of fixed plates which
stay immobile relative to a device, and mobile plates that are held in position by ‘springs’.
Between the springs is a mass which, under the influence of applied acceleration, will
physically lag relative to the acceleration that is experienced. This causes the distance
between the fixed outer plates and the plate on the moveable mass to change. When these
plates are driven with a voltage, the change in distance generates a change in capacitance
between the plates.
The change in capacitance is then measured by supplementary
circuitry, and it is the output of this circuitry which provides the measurement of
acceleration.
Inertial sensors have a wide number of applications and have been applied to good effect
in a large number of areas, including automotive (Galvin et al., 2000), aerospace (Hayton
et al., 2001) and crash testing (Castro et al., 1997) to name a few. However, as the inertial
sensors are to be employed to monitor children undergoing OFCs, this section focuses on
the application of inertial sensors for human monitoring.
2.2.2
Applications of inertial sensing
The algorithms which are discussed in this section relate to monitoring of human bodies
for the purpose of estimating activity, energy expenditure and other metrics. In all cases
accelerometers are employed, but in some cases other inertial measurements are also
utilised.
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Section 2.2: Activity analysis
2.2.2.1
Activity recognition
Inertial sensing can be applied to recognition of postures, gait and activities (i.e. sitting,
standing, walking running, etc). This form of sensing has many applications. For example,
Minnen et al. (2005) inferred high-level behaviour from low-level gestures for the purpose
of the detection of behavioural syndromes such as autism, Asperger’s syndrome, etc. To
perform this, three microphones and two accelerometers were worn, and an on-body
computer logged the data which were recorded.
Simple classification routines were
utilised by the researchers, but to good effect. The suitability of the hardware for the
application was not discussed even though with three microphones, two accelerometers
and associated data logging hardware, this may not be appropriate for persons with
behavioural syndromes.
Pober et al. (2006) discussed the application of quadratic discriminant analysis to assess
activities. It was shown in this study that the classification of activities is non-trivial,
because for some (for example with walking uphill and on flat surfaces) the accelerations
which are obtained are very similar. It is possible that with measurement of more DoFs
that the angle of ascent could have been inferred to allow discrimination between flat and
tilted walks, but this is not discussed. However, with the classification routines which were
utilised, good results were obtained. The sample size which was used was also quite small
(n = 6). While this number of participants is suitable for the validation of pre-existing
algorithms, it is small for the generation of new algorithms. The authors also concede this
and indicate that there is also value in investigating alternative classification routines.
Staudenmayer et al. (2009) presented a routine where 48 volunteers performed light,
moderate and intense exercise for 10 minutes a piece. The classification effectiveness
that was achieved was very good. Indeed, Staudenmayer et al. (2009) also demonstrates
how energy expenditure can be estimated through accelerometer analysis, and when
compared to the ground truth, the results which were obtained were competitive with
other researchers, such as Crouter et al. (2006); Freedson et al. (1998); Swartz et al. (2000).
The results which were obtained involved activities requiring high degrees of energy, such
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Chapter 2:
as racquetball and basketball. It is shown elsewhere, however, that tests of this nature
should not exceed 20 minutes in length as the metabolic response of persons after this
time is unreliable (Winter et al., 2006). This is because as the body is exerted to intense
or lengthy physical exertion, different attributes will affect the metabolic response which
is experienced. Therefore, while the results obtained by Staudenmayer et al. (2009) were
accurate, there is the possibility that they may still have been corrupted by this factor.
Benbasat and Paradiso (2002) presented a generic motion recognition framework which
employed six DoFs to track movements. This platform was designed so that it could
be used by researchers with minimal requirement for knowledge of the underlying
algorithms. However, while the platform performed very well and adapted to many
situations appropriately, it occupied a volume of approximately 500 cm3 , which is not
practical for many applications, in particular in the medical setting.
Recently, with the integration of inertial sensors into mobile phones, activity analysis has
become readily accessible to smartphone users. Interestingly, it was found that by analysis
of a single accelerometer from within a smart phone in a user’s pocket, it was possible
to predict the identity of users (Kwapisz et al., 2010). Movement-based identification has
been performed before in image analysis (Kale et al., 2003) and with multi-sensor gait
analysis (Annadhorai et al., 2008) previously, but the novelty of Kwapisz et al. (2010) was
the first which obtained this from single sensors in the pocket.
Another active area of research is in the estimation of energy expenditure through
accelerometer analysis. In particular, with the popularity of mobile phones, and with
their integration with accelerometers, overall assessment of daily energy expenditure can
be achieved with accelerometer data alone. Indeed, this is a primary application of a
number of youth-based anti-obesity initiatives where, integrated with social networking
websites, daily activity can be posted as a means of encouraging exercise (LanninghamFoster et al., 2009). Research into activity-based intervention monitoring to combat obesity
is gaining momentum (Oude Luttikhuis et al., 2009) and these means of research are not
only applicable for countering obesity in youth (Lobstein et al., 2004; Wang and Lobstein,
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Section 2.2: Activity analysis
2006), but it also applicable to the adult generation which is also susceptible to this
epidemic (WHO, 2000).
Accelerometers can also be employed for the analysis of rehabilitation exercises (after a
fall, for example). This can be achieved because MEMS accelerometers can provide reliable
and objective measures of mobility, gait and balance (Culhane et al., 2005). Accelerometerbased rehabilitation has also been employed to measure the mobility of sufferers of strokes
by Uswatte et al. (2005, 2006). These studies have shown that accelerometers have been
shown to objectively quantify mobility scores which are within 10% of human-recorded
scores (Hester et al., 2006), which shows that accelerometry is an accurate measurement
for fully mobile and constrained applications.
2.2.3
Activity-based analysis of OFC
In the previous chapter it was stated that there is a tendency for subjects to become quiet
and that there is a tendency for them to play less in light of allergic reactions. This
effect can be objectively measured by the measurement of activity and by the estimation
of energy expenditure, and accelerometry is an appropriate tool for this measurement.
Indeed, it has been shown here that accelerometry has the capacity to analyse both activity
and energy expenditure in free living and constrained environments alike.
For accelerometer-based analysis of OFCs, the primary means of assessment will focus
on activity– (i.e. movement) and accelerometer-based energy expenditure. Postural and
activity classifications are not performed on this data because the subjects are required to
stay on a bed throughout the challenges, and will, therefore, typically be lying supine.
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Chapter 2:
Figure 2.3: The original galvanometer developed by Willem Einthoven to record the ECG
in the early 20th century.
2.3
Heart rate analysis
2.3.1
History and introduction
Clinically, ECG analysis is one of the most important area of research for the assessment
of cardiac health. The development of modern machines which can record the ECG was
pioneered by Willem Einthoven with the invention of the string galvanometer (Barold,
2003). A representation of this machine is shown in Figure 2.3 where the subject has
placed his hands and legs in buckets which contain a salt water solution. This is performed
to increase the conductivity of the electrical signals of the heart to the galvanometer.
The signals were then amplified by a large electromagnetic amplifier, and a string was
deflected based on the signals which are obtained. This deflection was transferred to paper
and provided a representation of the ECG.
26
Niall Twomey
Section 2.3: Heart rate analysis
The naming conventions of the ECG waveforms that are used today were initially coined
by Einthoven.
However, while the terminology is similar, the technology which is
employed in the modern recording of the ECG has changed dramatically in size, power
and proficiency. Indeed, with today’s technology, the ECG can be recorded with circuitry
no bigger than a coin for many hours (Burns et al., 2010), whereas with Einthoven’s
contraption, five assistants were required to operate the machine, and water was required
to cool the active mechanisms (Churchill, 2008).
Even at its primitive beginnings, the ECG was shown to be diagnostically valuable,
and doctors, such as Einthoven and later Thomas Lewis, pioneered the investigation of
the effects of disease on the ECG. Since this era, the development of cardiology has
evolved, and now 12-lead ECG is the state of the art for preliminary diagnosis of heart
disease. This type of analysis can not only detect irregularities in the ECG, but with
training, cardiologists can also localise the precise area of the heart which is subject to
the condition without having to perform more invasive tests (Fuchs et al., 1982), such as
contrast-enhanced ultrasound (Martegani et al., 2008; Furlow, 2009). However, upon such
diagnoses, more invasive procedures may be scheduled in order to obtain more detailed
information about the condition under investigation.
Due to the expertise required to apply and interpret 12-lead ECG, these analyses are
typically only performed by trained cardiologists when clinical diagnosis of heart disease
is required. The lengths of these recordings are typically quite short. For medium-to-long
term ECG recordings, 3– and 5-lead ECG are used, which can result in ECG traces similar
to that shown in Figure 2.4 (Dublin Institute of Technology, 2013) in which the segments
of the ECG are annotated as described by Einthoven. While these 3-lead recordings can
highlight cardiac arrhythmias and other heart conditions, their specificity is quite low, and
these recordings are often discarded after recording.
ECGs recorded with 3– and 5-leads are more typically used in the assessment of the
variability of the heart over time. Statistical measurements obtained between sets of RR intervals of the heart rhythm are extracted, and these are known as features (see Figure
27
Niall Twomey
Chapter 2:
Figure 2.4: Labelled ECG waveform (Dublin Institute of Technology, 2013).
2.4). The variation of these is computed mathematically to quantify the state of the heart.
This is known as heart rate variability (HRV) feature analysis.
2.3.2
Applications of HRV analysis
The ECG is the best way of measuring variation in the heart (Sabiston Jr, 1981). The ECG
has previously been shown to change due respiration (Baldzer et al., 1989; Yamamoto et al.,
1991; Jan et al., Nov; Kemp et al., 2010), exercise (Robinson et al., 1966; Tulppo et al., 1996;
Cole et al., 2000; Sandercock et al., 2005), stress (Falkner et al., 1979; Kostis et al., 1982;
Bernardi et al., 2000; Obrist et al., 2007; Riese et al., 2004; Bořil et al., 2012; Bailón et al.,
2010), hypo-tension (Hernando et al., 2011), heart disease (Dekker et al., 2000; Tsuji et al.,
1996; Antelmi et al., 2004; Nolan et al., 1998; Licht et al., 2008), anxiety (Licht et al., 2009),
asphyxia (Boardman et al., 2002) and later in this thesis, investigations are performed into
whether the heart rate variability due to allergic reactions.
The heart rate variability (HRV) features, and in particular the sympathetic and parasympathetic indices, are strong indications of the risk of sudden adult death (Bradley and
Floras, 2003; Lombardi et al., 2001; Seccareccia et al., 2001; Dekker et al., 1997; Huikuri
et al., 2001). This is a condition in which otherwise healthy adults die suddenly. Risk
factors for this include a number of well-known arrhythmia and other heart conditions
which can be measured with the ECG. Screening can be performed with ECG and persons
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Niall Twomey
Section 2.3: Heart rate analysis
can be alerted to this which will then allow for medication and exercise regimes to be used
by the patient so that the risk of death is reduced.
Much work has been performed into digital signal processing (DSP) related investigation
and conditioning of the ECG (Sörnmo and Laguna, 2005, 2006; Schlindwein et al., 2006).
For example, the effect of breathing can be ascertained from signal processing analysis of
the ECG (Bailón et al., 2007). Indeed, HRV analysis has been used in order to assess seizure
in neonatal (Doyle et al., 2010) and adult (Jeppesen et al., 2010) hospitalised patients.
The results which have been obtained with these correlations have shown that HRV-based
seizure detection is difficult to obtain in general, and that electroencephalogram (EEG)
analysis provides the best results. However, it is the case that some patient’s HRV features
react strongly to seizure, and for these reasons their analysis can offer good subjective
seizure appraisal when applied with appropriate persons (Doyle et al., 2010).
While the role of HRV has been stated as being capable of detecting abnormalities in
the ECG, it can also be used to characterise normality in adults (Nunan et al., 2010) and
children (Aziz et al., 2012) which can be employed to rule out certain diseases.
Heart monitoring can be used in many situations, even outside of medically diagnostic
applications. For example, HRV analysis can be employed in order to assess the anxiety
and stress of persons throughout their daily work commute and employ. With medical
professionals, for example, stress can impact their work, decision making and vigilance.
Therefore, with HRV analysis, times at which these personnel become stressed can be
assessed automatically (Jovanov et al., 2011), which leads to the possibility of alerting the
medic of their state of stress. If conditions allow, relaxation methods could be followed.
With car driver analysis, the heart rates can be detected and drivers who might be suffering
from road rage can be identified (Bořil et al., 2012; Healey and Picard, 2005). This can
provide an objective feedback mechanism for drivers and can contribute to obtaining
safer roads. In both of these examples, however, the participants must be compliant with
analysis and towards the feedback for benefits to be obtained from this.
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Chapter 2:
Therefore, the use of the HRV features for the detection of signatures of allergy will be
investigated in order to discriminate between allergic and non-allergic subjects. HRV
features have been shown to be of good clinical use for the assessment of many stressful
environments and many acquired and chronic medical conditions, and it is believed that
signatures of allergy will be uncovered by analysis of these features.
2.4
Machine learning and classification
2.4.1
Introduction to classification
Applications in which activity and HRV analyses have been employed have been described
in the previous sections. However, in order to use these analyses in an automated manner,
it is generally necessary to employ classification algorithms.
A number of machine
learning algorithms which would be appropriate for classification of OFC are described
here.
It is important to note that the algorithms which are employed are generally
independent of their applications and can be used to solve a very wide range of other
applications when deployed appropriately. For example, classification can be used for
many applications including email spam detection (Andersen et al., 2008), fraud detection
(Phua et al., 2004), vibration analysis of bridges, cars, engines, etc Oh et al. (2009), to name
a few. Machine learning has also applied to activity analyses (Ravi et al., 2005; Mannini
and Sabatini, 2010) and HRV (Kononenko, 2001) . The use of classification for these will
be discussed in a later section.
2.4.2
Background to machine learning
Classification is the process of automatically and intelligently assigning labels to data.
The label which is assigned is subject to the application which is being investigated. For
example, with accelerometer-based classification the task at hand might be to determine
30
Section 2.4: Machine learning and classification
Niall Twomey
the activity which is being performed, and possible labels might include walking, running,
jumping, etc. Likewise, for ECG analysis, the task might be to determine if a specific
event has occurred, and the labels might be normal rhythm, arrhythmia, ectopia, etc (the
meanings of these conditions are discussed later).
Two main branches of classification are available: supervised and unsupervised.
In
supervised learning, all the data which is employed is labelled according to the application, whereas in unsupervised learning, supplementary algorithms are first required
to automatically assign labels to the data, and then classification can be performed with
these labels. In this section (and indeed for the remainder of this thesis) the classification
routines which are discussed relate to supervised classification only.
Two independent stages are involved with supervised classification: training and testing.
The training stage involves employing data with known labels and ‘feeding’ this data into a
learning algorithm. Once this process has completed, other data (which are also labelled)
are used to test the classifier. By this process the performance of the classifier can be
assessed as selected classification labels will be compared to the ground truth. The process
where the training data and testing data are separated is the preferred routine which can
be employed as it can assess the generalisation error of the problem at hand (Vapnik and
Kotz, 1982).
The most common type of classifier is the discriminative classifier. This type of classifier
is trained on more than one data label. Then, by the algorithms within its framework,
the classifier can learn to discriminate between the classes it was trained with. It is
not guaranteed that the ground truth will be recovered by classifiers, however, and the
accuracy of classification is subject to the application in question and the data which are
available.
Many different types of classifier exist, including — but not limited to — support vector
machines (SVMs), Gaussian mixture models (GMMs), k-nearest neighbourss (kNNs), naive
Bayes, etc. When multiple learners cooperate together for tackle a machine learning
problem procedures are termed ensemble or boosting methods. Eventually, all of these
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Chapter 2:
Figure 2.5: Example of how classification is obtained with two-dimensional data (top)
with SVM based classification (bottom left) and GMM based classification (bottom right).
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Section 2.4: Machine learning and classification
algorithms reduce to determining which of a number of labels is the most likely, having
been trained on one dataset and being tested on new data.
Clearly, for successful
application of machine learning, the training dataset must contain a wide range of
examples that are representative of the problem under investigation. An example of
machine learning can be visualised in Figure 2.5, where SVM and Gaussian mixture
model (GMM) classifiers are employed in order to classify two classes (i.e. labels) of data
which are marked as ⇥ and ◦. Areas on the left hand side of the images (highlighted in
red) are the regions which have been selected as being more indicative of the ⇥ class, while
those highlighted on the right hand side (in blue) are more indicative of the ◦. It can be
seen that for this simple example, the classification routine attempts to split the feature
space in two regions, one for each class of data. An obstacle which is common to all
classifiers is the means in which data should be best modelled in order to minimise (and
ideally eliminate) uncertainty between the classes. This example shows that complicated
decision boundaries can be obtained for relatively well-behaved data.
With the example in Figure 2.5, a case where two classes have been analysed is presented.
It can sometimes occur that more than two classes require classification, for example
previously it was stated that activity classification might assign walking, running, and
standing labels. However, it can also be the case that knowledge of only one class is
available. In this case, the classification routine is termed ‘novelty’ or ‘abnormality’
detection.
2.4.3
Novelty detection
The task of novelty detection algorithms is to detect data which are not from the
distribution of the training data.
These are termed ‘novel’ data points.
This type
of classification is the means by which allergy must be classified because only one
class of labelled data is available, and this is the data which were recorded before the
administration of the first dose of the allergen. This is guaranteed to be non-allergic
because the allergen has not yet been administered. Classification of allergy, therefore,
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Chapter 2:
involves training classification routines on this non-allergic data and determining the
boundaries of novelty from this. Data found to be outside this boundary would then be
considered novel (i.e. allergic).
With the allergy data, one could hypothesise about the temporal labels during the OFC and
thereby obtain a discriminative problem (e.g. one could assume that features 10 minutes
prior to a reaction belongs to the allergic class, for example). However, these hypotheses
will only enable the classifier to learn this assumption rather than facilitate detection of the
true signatures of allergy, and this may obtain sub-optimal performance. Consequently,
novelty detection is the preferred method for allergy detection.
Novelty detection applications are not as popular as multi-class problems, but they have
been employed for many applications as expert labels are often difficult — and sometimes
impossible — to obtain. These algorithms are trained on the data which are labelled and
obtain ‘boundaries of novelty’ and the data within these boundaries are ‘normal’ while
those from without are novel. The two most popular types of novelty detection routines
involve support estimation by a one-class SVMs, and density estimation by GMMs.
2.4.3.1
One class SVMs and GMMs
The discriminative SVM was mentioned previously and an example of how it partitions the
data was shown in Figure 2.5. SVMs compute a maximum-margin hyperplane between the
distributions of the classes which are investigated (Flach, 2012). However, with one class
SVMs, only one class is available for training, and a maximum margin hyperplane cannot
be obtained. One-class SVMs therefore learn a hypersphere which ‘surrounds’ a specified
percentage of the training data, and the radius of this sphere is employed to estimate the
support of a distribution (Schölkopf et al., 2000, 2001). Then, data which lies within this
sphere are considered normal, and data which is outside the sphere are novel. The extent
of the novelty of this data can be calculated by computing the perpendicular distance from
that point to the hypersphere. In effect, the radius of this sphere is a threshold which is
selected during the learning process.
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Section 2.4: Machine learning and classification
Niall Twomey
The GMM classifier sets out to model a distribution with a mixture of Gaussians. With
this, the ‘probability’ that new data are normal can be assessed by ‘reading’ the value
off the learnt distribution. It is, however, necessary to apply thresholding for GMM-based
novelty detection after the distribution has been modelled. This is acceptable as this is also
performed by the one-class SVM, but this process is ‘hidden’ by the learning algorithm.
2.4.3.2
Example of novelty detection
Figure 2.6 shows an example of novelty detection algorithms being employed. Here, the
same data that was used in Figure 2.5 were employed, and the novelty detection routines
were trained on data from Class 1 only. In the images, the red areas are more indicative of
normal data, and blue areas are more novel. The upper image shows the decision surface
obtained from the one-class SVM. While this algorithm learns a hypersphere, the shape
of the separation is not circular because of the ‘kernel trick’ (Aizerman et al., 1964) that
was employed. This projects the original data into high-dimensional feature spaces, and it
is in these feature spaces where the sphere is obtained. While the examples in Figure 2.5
effectively separated the feature plane, novelty detection is more likely to isolate a small
region of the plane and define this as being ‘normal’.
With both examples here, the normal data are well modelled and the highest probabilities
of ‘normal’ data are found in the centres of the distribution. The further away from this
that the data points are found indicate more novel features. It is difficult to say which
routine is superior due to the fact that both have been employed very successfully in
many applications. In the example in Figure 2.6 the one class SVM learns the boundary of
normal data which provides a quick roll-off between ‘normal’ and ‘abnormal’ data. GMMbased modelling models the distribution, so it has a slower roll-off than the SVM-based
procedure.
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Chapter 2:
Figure 2.6: Example of how novelty detection algorithms can be employed to determine
novel and normal data points. The upper image shows the distribution of the labels, and
the lower-right figure shows one-class SVM while the lower-left figure shows the example
of GMM-based novelty detection on the same data.
36
Section 2.4: Machine learning and classification
2.4.4
Niall Twomey
Applications of novelty detection
GMM– and SVM-based novelty detection algorithms have been employed in a number of
machine learning applications to very good effect. This section will focus on physiologicalbased classification as this is the application which is under investigation in this thesis.
Novelty detection has been used for the automatic and online classification of epileptic
seizure with intracranial EEG (Gardner et al., 2006; Gardner, 2004). This classification
routine involved the use of one-class SVMs and obtained excellent sensitivity of classification. However, while excellent sensitivity was obtained, the authors did not report the
specificity of the routine. The methodology investigated also only used three energy-based
features to describe the EEG, which is a very low number of features in comparison to the
state of the art in seizure detection (Temko et al., 2009, 2011b; Thomas et al., 2009).
Roberts (1999, 2000), investigated the use of extreme value theory (the branch of statistics
which deals with abnormally high or low values in distributions) in conjunction with
GMMs in order to assess a number of different applications in the medical and image
processing fields.
It was shown that GMM-based novelty analysis of hand tremors,
epileptic seizure, vigilance (during repetitive, boring or long-term tasks) and anaesthesia
are applications with which novelty detection can be employed to classify medical
conditions.
These papers do not provide any metrics of the overall performance of
classification, but rather demonstrated the applicability of novelty detection for these
applications. However, they do show that GMM-based analysis is useful for movement
(i.e. accelerometer-based) applications, and that it is also appropriate in medical studies.
Speech recognition has also employed novelty detection (Markov and Nakamura, 2008)
for improved speaker identity, and up until very recently GMMs are the state of the art for
speaker identification (Fine et al., 2001; Faundez-Zanuy and Monte-Moreno, 2005).
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Chapter 2:
2.5
Food challenge data collection
2.5.1
Recording platform
The requirements for the recording platform are that the acceleration and ECG must be
recorded concurrently during OFCs. Then, DSP is employed to identify the applicability
of the acceleration and ECG data for classification of allergy.
The sensing health with intelligence, modularity, mobility and experimental reusability
(SHIMMER device) (SHIMMER research, 2010; Burns et al., 2010) is the wireless sensing
platform that was chosen for data collection during the OFCs, and is illustrated in
Figure 2.7.
This is a sensing device which features Bluetooth and 802.15.4 radios,
tri-axial accelerometer (Freescale MMA7260Q, with programmable sensitivity⇤ ) with
programmable axial sensitivity (selectable from 1.5 g, 2 g, and 4 g), a micro SD card slot
(up to 2 GB) and a ultra-low power microprocessor (MSP430). The microprocessor is
programmed with the network embedded systems C (nesC) event-driven programming
language, and is supported by the TinyOS component-based operating system.
The
SHIMMER device also supports ‘daughterboards’ which extend the functionality of the
platform, and the ECG daughterboard allows for ECG recordings. The SHIMMER device
offers a small form factor (53 mm ⇥ 32 mm ⇥ 25 mm), which is approximately 10 times
smaller than the platform that was previously discussed in this chapter by Benbasat
and Paradiso (2002).
This is a very important consideration for the monitoring of
children during OFC. The SHIMMER device has been awarded European conformity (CE)
certification mark (SHIMMER-research, 2010), signifying it fulfills European Union health
and safety requirements for clinical-based use, and it is therefore appropriate for data
collection during OFCs. The acceleration signals and the ECG traces were sampled at 256
Hz.
⇤ These accelerometers are sufficiently sensitive for gait– and energy expenditure-based analysis and have
been used for many such application domains.
38
Niall Twomey
Section 2.5: Food challenge data collection
(a) Front of SHIMMER.
(b) Back of SHIMMER.
Figure 2.7: Diagram of the SHIMMER device, with the various components annotated
(reproduced with permission from SHIMMER-research (2010)).
2.5.2
Integration with oral food challenge
The Department of Paediatrics and Child Health in Cork University Hospital conduct
OFCs on a regular basis. A collaboration between the Department of Electrical and
Electronic Engineering of University College Cork and the Department of Paediatrics
and Child Health of Cork University Hospital was organised, and ethical approval was
sought and obtained from the ethics board to collect accelerometer and ECG data from
subjects undergoing OFC with the SHIMMER device. Participation in data collection was
voluntary, and if the parents or the child did not wish to partake, the OFC progressed as
normal.
With the requirements for data collection defined, the procedure of the OFC was
modified slightly in order to accommodate data collection. Figure 2.8 presents a modified
flowchart in which two supplementary stages are introduced to facilitate the use of the
SHIMMER device for data collection.
The first supplementary step required is to obtain informed consent from the subject’s
guardians for monitoring the subject’s physiological signals during the food challenge
in accordance with the ethical approval. Allergists discuss with the guardian the datacollection procedure and provide them with literature they must read before signing
the consent forms. If consent was provided, a chest strap holding the SHIMMER device
39
Niall Twomey
Chapter 2:
Subject
arrives at
Hospital
Preliminary
tests
Consent.
SHIMMER
applied
Observe
for 10 —
20 minutes
Checkup
Fail
Pass
Administer
Dose
Yes
Dose
Remaining?
Subject
diagnosed
‘allergic’
Fail
No
2hrs
Observation
SHIMMER
removed
Pass
Subject
diagnosed
‘non-allergic’
Challenge
over
Figure 2.8: Modified oral food challenge flowchart which is employed to accommodate the
introduction of the SHIMMER monitoring device for data collection during OFCs.
40
Niall Twomey
Section 2.5: Food challenge data collection
was fastened around the trunk of the subject and the ECG electrodes are configured to
record data. The SHIMMER device then connects to a host computer over which data is
transmitted. The second supplementary stage involves removing the SHIMMER device
from the subjects after the test has concluded. The OFC process is unchanged at this stage,
and diagnoses are unaffected.
2.5.3
Other data
While the blood pressure and blood oxygen saturation levels are recorded periodically by
the allergists who conduct the OFCs, these were not recorded during the data collection
stage. The primary reason for this is because the eventual allergy classification framework
is envisioned to require minimal interaction by the allergists, and to also record the
physiological data in a non-invasive manner which will introduce as little discomfort as
possible to the subject.
It would not be in line with this methodology, therefore, to require the allergist to log with
blood pressure and blood oxygen saturation levels in a computer after every checkup.
Moreover, it is also not feasible to delegate equipment to record these (in particular the
blood pressure) as they are are invasive sensors that require compliance and subjectparticipation for meaningful readings.
It is also the case that blood pressure and oxygen saturation signals will be the final
physiological conditions which change under the influence of allergy, and these will only
change after perceivable symptoms have manifested.
2.5.4
Data recording
In total, the acceleration and ECG data of 24 subjects were recorded during OFCs. Of
these subjects 15 reacted to the food type during OFC and were diagnosed allergic. These
subjects are tabulated in Table 2.1. All subjects are ten years of age and under. The
41
Niall Twomey
Chapter 2:
Table 2.1: Tabulation of the characteristics of the subjects who were recorded for this
study.
Index
Gender
Age
Recording length
(minutes)
Allergen
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
male
male
male
male
male
female
male
male
female
male
female
female
female
male
female
male
male
female
male
female
female
male
female
male
1.5 years
6 years
9 years
12 months
8 years
9 years
6 years
5 years
8 years
3 years
6 years
5 years
3 years
8 years
9 months
6 years
10 years
4 years
6 years
1.5 years
7 months
12 months
4 years
2 years
14
95
90
100
120
33
57
100
50
82
85
40
105
125
96
69
91
125
33
110
57
81
58
90
wheat
peanut
egg
milk
peanut
peanut
soy
peanut
egg (cake)
milk
peanut
milk
milk
soy
wheat
egg
egg (cake)
soy
peanut
milk
milk
milk
wheat
peanut
Diagnosis
Allergic
Non-allergic
shortest test was obtained with Subject 1 whose OFC lasted for 14 minutes, and the longest
challenge lasted 130 minutes (Subject 19). While the lengths of the challenges should
be consistent, in particular for non-allergic subjects, the OFC is a dynamic test in which
delays can occur, and this explains the variances in challenge length.
42
Niall Twomey
Section 2.6: Conclusion
2.6
Conclusion
It has been shown in this chapter that inertial measurement and ECG analysis can
each be employed for the classification of many physiological and clinical applications.
In particular, changes in HRV have been associated with many chronic and acquired
medical conditions. It has, therefore, been reasoned here that, while these have not been
used for allergy detection, acceleration and ECG analyses are capable of assessing the
observations of the allergists in an objective and quantifiable manner. Later chapters
will now investigate the applicability of these algorithms and for machine learning based
classification of allergy. The work presented in the remainder of this thesis is original and
has not been investigated by other researchers before.
43
CHAPTER 3
Accelerometer-based analysis of
oral food challenges
3.1
T
Introduction
HIS chapter introduces the concept of accelerometer-based activity and energy
expenditure estimation. This is investigated because allergists have reported that
there is a tendency for the activity and energy levels of subjects to change before an allergic
reaction presents, and these can be measured with accelerometers.
Energy can be measured in a number of ways. Physically, the gold-standard of energy
expenditure measurement is calorimetry. This measures the heat that radiates from a
body and, in combination with metrics such as the size and mass of that body, calorimetry
calculates true energy expenditure. This process is feasible for the measurement of energy
44
Niall Twomey
Section 3.1: Introduction
from small immobile objects, but it is not feasible to use this process to measure the true
energy expenditure of people in a non-invasive and comfortable manner.
Therefore, indirect calorimetry was devised. This exploits the fact that in order to expend
heat, humans (and indeed all animals) must breathe in oxygen and expel carbon dioxide
and other gases. By measuring the volumes of O2 and CO2 entering and leaving a subject
as they breathe, and by combining this with standard physiological characteristics (such
as mass, height, etc), the true energy expenditure of a subject can be inferred. This process
is called indirect calorimetry as energy is not directly measured. Two indirect calorimetry
processes exist for the measurement of energy expenditure in humans. Both of these
methods measure the same data with similar accuracy.
The first method utilises a mask that must be worn by a subject who breaths through this
into a tube. The oxygen and carbon dioxide content of the subject’s breath is analysed by
specialised and calibrated hardware (Cosmet, 2013). This mechanism is carefully designed
to ensure that that minimal difficulties are introduced to a subject’s respiratory process by
the applied hardware. The system, based on the gas levels recorded, then produces a
report of energy expended every time a subject has completed a respiratory cycle, i.e. the
subject must both inspire and expire before energy expenditure readings are obtained as
both processes provide metrics that are required for the analysis. The regression process
was described in detail by Ferrannini (1988).
The second method that can be used is called the doubly labelled water method (Schoeller
et al., 1988). This process employs isotopes of hydrogen (deuterium) and oxygen (oxygen18). These isotopes are not radioactive or dangerous, and can be found naturally in the
atmosphere. Here, they are used as chemical markers. The doubly labelled water method
is conducted within a sealed room where the air and temperature conditions are monitored
and controlled by trained technicians. The isotopes are introduced into the atmosphere of
the room in known and controlled volumes. By monitoring the gases expressed by the
respiration process which are marked by these isotopes, and by monitoring the change
45
Niall Twomey
Chapter 3:
in deuterium and oxygen-18 in the room, true energy expenditure can be computed with
similar equations that were employed with the first method.
Both of these methods measure true energy expenditure, but the doubly labelled water
method is the lesser used of the two as it is more expensive to operate and requires a
dedicated room with sophisticated controls and experienced personnel to correctly operate
the protocol. It is not practical to employ indirect calorimetry in every situation, however,
and it is not suited for recording data during oral food challenges where subjects are young
and often become sick and require medical assistance and the comfort of their parents.
Therefore, a recent area of research has been in the estimation of true energy expenditure
based on the monitoring of signals which can be obtained in a non-invasive, low-power
and low-cost manner.
Signals obtained from uni-axial (single-dimensional) and tri-
axial (three-dimensional) accelerometers have been shown to be highly correlated with
true energy expenditure. Figure 3.1 shows a diagram of a body and the three axes of
acceleration that are monitored by tri-axial accelerometers. Here, anterio-posterior is the
x-axis, mediolateral is the y-axis and vertical is the z-axis. Indeed, accelerometry can
provide a number of advantages over indirect calorimetry and Chapter 2 demonstrated
how acceleration signals can be employed for activity analysis too. In this chapter, a
distinction is drawn between activity and energy expenditure analysis — activity is the
measure of movement and mobility, whereas energy expenditure employs activity analysis
to estimate energy.
This chapter compares a number of accelerometer-based activity and energy expenditure
estimation algorithms. These algorithms are validated with independent data recorded
during new experiments in order to ensure that the values which result from these algorithms are accurate. The accelerations of subjects undergoing oral food challenges which
were recorded are then assessed yielding activity and energy expenditure estimation
readings. Based on the results of these, the applicability of accelerometer-based allergy
detection is discussed.
46
Section 3.2: Accelerometer-based activity analysis
Niall Twomey
Figure 3.1: The x (anterio-posterior), y (medio-lateral), and z (vertical) acceleration
directions in relation to a body.
3.2
Accelerometer-based activity analysis
3.2.1
Activity metrics
A number of metrics can be calculated on raw acceleration values over a time-window of
length Te seconds. The most common activity analysis metrics which form the basis of all
of the algorithms discussed in this chapter are described here.
• The integral of absolute acceleration (IAA) computes the absolute value of acceleration. The integral of absolute acceleration in the x-axis (IAAx ), integral of absolute
acceleration in the y-axis (IAAy ) and integral of absolute acceleration in the z-axis
47
Niall Twomey
Chapter 3:
(IAAz ) can be computed for tri-axial accelerometers and these are defined by
Z
IAAx (t) =
t=⌧+Te
|ax (t)| dt,
(3.1)
##a (t)### dt,
y
(3.2)
|az (t)| dt,
(3.3)
t=⌧
Z t=⌧+Te #
IAAy (t) =
t=⌧
Z t=⌧+Te
IAAz (t) =
t=⌧
where ax , ay and az are the acceleration values in the x, y and z directions. These
are in g-units (i.e. multiples of 9.81ms −2 ). In subsequent sections, when a number is
followed by the letter g (e.g. 1 g), this identifies multiples of the unit.
• The total integral of absolute acceleration (IAAt ) combines the three IAA values
together. This value is sometimes referred to as activity and acceleration counts
(Chen and Bassett Jr, 2005) and is defined by
Z
IAAt (t) =
t=⌧+Te ⇣
t=⌧
⌘
##
##
|ax (t)| + #ay (t)# + |az (t)| dt
= IAAx (t) + IAAy (t) + IAAz (t).
(3.4)
(3.5)
• The magnitude of acceleration is the square root of the sum of the squares of the
raw accelerations. This metric provides an indication of overall acceleration but
the directionality of the signal is removed by the squaring and summation. This is
computed by
Z
t=⌧+Te q
ax (t)2 + ay (t)2 + az (t)2 dt.
IAV(t) =
t=⌧
(3.6)
• Instantaneous velocities in x, y and z directions can be computed by
Z
vx (t) =
vy (t) =
vz (t) =
t=⌧
t=0
Z t=⌧
t=0
Z t=⌧
t=0
ax (t) dt + vx (t = 0),
(3.7)
ay (t) dt + vy (t = 0),
(3.8)
az (t) dt + vz (t = 0).
(3.9)
48
Section 3.2: Accelerometer-based activity analysis
Niall Twomey
However, these values must be integrated from t = 0 as acceleration is the continuous
derivative of velocity.
• The mean kinetic energy can be computed by
m
KEtot (t) =
2Te
Z
⌘
vx (t)2 + vy (t)2 + vz (t)2 dt,
t=⌧+Te ⇣
t=⌧
(3.10)
where m is the mass of the body on which acceleration is being measured, and vx , vy
and vz are the velocities in the x, y and z directions respectively.
• The mean power can be computed by
m
P=
2Te
Z
t=⌧+Te ##
t=⌧
#
## d ⇣v (t)2 + v (t)2 + v (t)2 ⌘### dt.
y
z
# dt x
#
(3.11)
Equations 3.1 — 3.11 are fundamental equations which are employed by researchers
for activity analysis and energy expenditure estimation algorithms (Bouten et al., 1994,
1997a,b; Crouter et al., 2006; Chen and Sun, 1997). These equations are presented in the
continuous domain, but are employed in the discrete domain for the work here. Replacing
the time variable, t, with the sample index, k, yields the discrete-domain equivalent of
these equations. The continuous equations were presented in this section as they are more
intuitively understood than the discrete equivalents.
3.2.2
Energy expenditure estimation algorithms
The goal of accelerometer-based energy expenditure (EE) is to obtain an energy expenc based on activity analysis which is as close as possible to true
diture estimation (EE)
energy expenditure (EEtrue ). Four different algorithms are described here which have been
designed to accomplish this.
The algorithms here are selected for investigation because they encompass a number of
different aspects of data modelling. Firstly, Bouten et al. (1994) provided algorithms which
49
Niall Twomey
Chapter 3:
performed an overall assessment of energy expenditure based on one regression equation.
Chen and Sun (1997) developed regression equations which were linear and non-linear
in nature, and these equations are designed to adapt to the physiological features of
the subjects being investigated. Finally, Crouter et al. (2006) employed a ‘decision tree’like algorithm to estimate energy expenditure based on the extent of activity which was
detected in the preceding minute.
3.2.2.1
A note on the energy expenditure estimation algorithms
In the next three subsections, a number of algorithms are described which can be employed
for the estimation of expended energy by persons wearing accelerometers. This thesis does
not cover the background on how to obtain such models, and for information on these
processes, and for justification of the regression units, the original publications that are
cited in the subsequent subsections should be reviewed.
For every algorithm presented below, the models are parameterised with values that were
found to minimise a specific cost function (e.g. the squared difference between the true
and estimated energy expenditure). Each model is therefore non-trivially parameterised
by seemingly arbitrary constants, but it should be noted that these were obtained by
employing regression techniques to learn the relationship between acceleration signals
and true energy expenditure.
These parameters are therefore, by definition of the
regression techniques, of appropriate units to convert the input values to the units of the
target variables (Bishop et al., 2006; Flach, 2012). Therefore, the specific parameters that
were obtained from the training process will not be discussed, but are presented here to
facilitate speedy reproduction of these algorithms.
3.2.2.2
Bouten et al
Bouten et al. (1994), provided regression equations for human-based energy expenditure
estimation with a tri-axial accelerometer.
50
Analysis of 30 seconds of accelerometer
Section 3.2: Accelerometer-based activity analysis
Niall Twomey
data is required for this estimation. Based on the data which were available, linear
relationships were discovered between acceleration counts and energy expenditure values.
Two regression equations were generated to estimated energy expenditure estimation due
c act ), and are defined by
to activity (EE
c act,x = −0.176 + 0.0851 ⇥ IAAx ,
EE
(3.12)
c act,t = 0.104 + 0.023 ⇥ IAAt ,
EE
(3.13)
c act,x and EE
c act,t are the energy expenditure due to activity (EEact ) estimates
where EE
based on IAAx and IAAt respectively.
As these equations model EEact , the resting
energy expenditure (REE) must be added to the results obtained to achieve total energy
expenditure. REE is calculated by Equations (3.14) and (3.15) for male and female subjects
respectively (Hemokinetics, 1993).
REEm =
REEf =
0
1
BB 215 ⇥ weight (kg)CC
BB
CC
BB
CC
BB
CC
BB + 12
⇥ height (m)CCC
BB
CC
BB
CC
BB
CC
BB − 513
(years)
⇥
age
CC
BB
CC
BB
CC
B@
A
+ 4687
100, 000
0
1
BB 150
CC
(kg)
⇥
weight
BB
CC
BB
CC
BB
CC
BB + 9
⇥ height (m)CCC
BB
CC
BB
CC
BB
CC
BB − 353
(years)
⇥
age
CC
BB
CC
BB
CC
B@
A
+ 49854
100, 000
51
(3.14)
(3.15)
Niall Twomey
Chapter 3:
Therefore, the total energy expenditure estimation is given by
c = EE
c act + REE.
EE
(3.16)
Bouten et al. (1994) generated Equations (3.12) and (3.13) by recruiting subjects who
walked on a treadmill between 3 and 7 kilometers per hour (km/h).
An indirect
calorimeter was worn by the participants and the acceleration was recorded at the same
time. The parameters of these equations were obtained by a least squares analysis of the
c
parameters which minimised the squared difference between EEtrue and EE.
Equation (3.12) estimates the energy expenditure with recorded accelerations from the
IAAx direction only. This is because the majority of the effort required for movement is
stated in this publication as being expended in this direction.
However, Equation (3.13) estimates energy expenditure based on IAAt . This allows the
regression to produce estimates which are representative of whole-body movement. In
this work, Equation (3.13) is used over Equation (3.12) because it was stated as providing
better results, and because it is intolerant to orientation errors as the overall acceleration
is measured (see later).
3.2.2.3
Chen et al
While Bouten et al. produced two generalised equations estimating energy expenditure,
Chen and Sun (1997), allowed for specialisation of their models based on the age, gender,
mass and height of the subjects whose acceleration was recorded.
The doubly labelled water method was employed here to obtain the reference energy
levels. The participants who were recruited were within the controlled room for two 24
hour periods, and in total over 6000 hours of data was recorded. The range of activities
52
Section 3.2: Accelerometer-based activity analysis
Niall Twomey
which the participants performed during their stay included sedentary, light, moderate
and vigorous activities.
The horizontal and vertical acceleration vectors were separated, as it was reasoned by
these researchers that different effort would be expended in vertical and horizontal
directions (Chen and Sun, 1997). The horizontal and vertical accelerations are computed
by Equations (3.17) and (3.19) respectively.
H(k) =
q
ax (k)2 + ay (k)2
q
V (k) =
az (k)2
= |az (k)|
(3.17)
(3.18)
(3.19)
Linear and non-linear algorithms were computed by Chen and Sun (1997), and these both
allow targeted specialisation of the algorithm towards specific physical characteristics (i.e.
age, height, weight, etc) in order to obtain the minimum error of estimation.
• Linear algorithm
The linear algorithm assumes the form of
c act (k) =aL ⇥ H(k) + bL ⇥ V (k),
EE
(3.20)
where H(k) and V (k) are calculated by Equations (3.17) and (3.19) respectively.
53
Niall Twomey
Chapter 3:
c
The parameters aL and bL were computed by minimising the difference between EE
and EEtrue , and the parameters which were found to achieve this on the data available
are calculated by
aL =
0
1
BB 5.76
⇥ weight (kg)CCC
BB
CC
BB
CC
BB
BB + 11.95 ⇥ height (cm)CCC
BB
CC
BB
CC
BB
CC
BB + 6.89
CC
(years)
⇥
age
BB
CC
BB
CC
B@
CA
− 2, 001
1000
bL =
,
5.96 ⇥ mass(kg) + 349.5
.
1000
(3.21)
(3.22)
• Non-linear algorithm
The relationship between acceleration and energy expenditure measurements is
not necessarily linear (Chen and Sun, 1997). In a similar manner to the linear
algorithm, the horizontal and vertical accelerations were also separated for nonlinear equations. The non-linear regression equations take the following form
c act (k) = aN ⇥ H(k)p1 + bN ⇥ V (k)p2 .
EE
(3.23)
The optimal scaling and power parameters (i.e. aN , p1, bN and p2) were computed
and were given by
p1 =
2.66 ⇥ mass(kg) + 146.72
,
1000
54
(3.24)
Niall Twomey
Section 3.2: Accelerometer-based activity analysis
p2 =
−3.85 ⇥ mass(kg) + 968.28
,
1000
(3.25)
aN =
12.81 ⇥ mass(kg) + 843.22
,
1000
(3.26)
bN =
0
1
BB 38.90 ⇥ weight (kg)CC
BB
CC
BB
CC
BB
CC
BB − 682.44
⇥ genderCCC
BB
CC
BB
CC
B@
A
+ 692.44
1000
,
(3.27)
where the ‘gender’ parameter of Equation (3.27) is 1 for male subjects, and 2 for
female subjects.
3.2.2.4
Crouter et al
Crouter et al. (2006), regressed acceleration readings to a measurement of energy expenditure termed metabolic equivalents (MET). 1 MET is the metabolic rate and energy
expended by person in rest (Ainsworth et al., 1993).The ratio of the energy expended
performing a task (walking, reading, etc) to that expended in a resting state is the
metabolic equivalent of the task, and thus the measurement is dimensionless.
Ainsworth et al. defined the rates of metabolic exercise and some of these are reproduced
in Table 3.1. Recently, a number of corrections have been made to these figures, (Ainsworth
et al., 2000, 2011), and the corrected MET values for normal– and over-weight persons are
also shown in Table 3.1. The correction factor between the original and revised MET values
is calculable and it is given by Equation (3.28). It is a function of the Harris-Benedict
resting metabolic rate (RMR) values which are calculated with Equations (3.29) and (3.30)
for female and male subjects respectively.
55
Niall Twomey
Chapter 3:
Table 3.1: MET activity and corrected MET activity values.
Activity
Rope jumping
Running
Bicycling
Pushing stroller
Calisthenics
Shopping
Watching TV
Original
12.3
9.8
7.5
4.0
3.5
2.3
1.3
Corrected (female)
Corrected (male)
<77kg
≥77kg
<91kg
≥91kg
13.5
10.7
8.2
4.4
3.8
2.5
1.4
16.5
13.1
10.0
5.4
4.7
3.1
1.7
12.9
10.3
7.9
4.2
3.7
2.4
1.4
15.4
12.3
9.4
5.0
4.4
2.9
1.6
Corrected MET value = MET ⇥
3.5
RMR
Rating
Vigorous
Moderate
Light
(3.28)
RMRf = 5.0 ⇥ Height(cm) +
13.7 ⇥ Weight(kg) −
(3.29)
4.7 ⇥ Age(years) + 66.5
RMRm = 1.8 ⇥ Height(cm) +
9.6 ⇥ Weight(kg) −
(3.30)
4.7 ⇥ Age(years) + 655.1
The algorithm of Crouter et al. (2006) is a function of the acceleration counts that were
calculated over the preceding minute and the coefficient of variation of the counts over
the preceding ten seconds. Models were generated to predict the energy expenditures
under these conditions. Listing 3.1 details the process of the algorithm (Crouter et al.,
2006). The algorithm is selects different regression equations depending on activity that
56
Niall Twomey
Section 3.3: Energy expenditure validation
Listing 3.1: Crouter et al.’s energy expenditure estimation algorithm.
1
2 i f ( A c c e l e r a t i o n C o u n t s min 50 ) {
3
EEMET = 1 . 0 ;
4
5 } else {
6
7
i f ( CV( A c c e l e r a t i o n C o u n t s 10s ) 10 ) {
8
EEMET = 2.379833 ⇥ exp { 0.00013529 ⇥ A c c e l e r a t i o n C o u n t s min } ;
9
10
} else {
11
EEMET = 2.330519 + 0.001646 ⇥ ( A c c e l e r a t i o n C o u n t s min )
12
− ( 1 . 2 0 1 7 ⇥10 −7
⇥ ( A c c e l e r a t i o n C o u n t s min ) 2 )
13
+ ( 3 . 3 7 7 9 ⇥10 −12
⇥ ( A c c e l e r a t i o n C o u n t s min ) 3 ) ;
14
}
15 }
16
was recorded over previous time windows. By this process the algorithm is unique to
the energy expenditure field as its formulation concedes that it is beneficial to generate
multiple regression equations for different operating points. It was tested over a large
set of light to vigorous activities (including those tabulated in Table 3.1) and the authors
stated that it was the most accurate algorithm in comparison to algorithms by Freedson
et al., 1998; Swartz et al., 2000; Hendelman et al., 2000.
3.3
Energy expenditure validation
The algorithms which have have been discussed have been stated to perform well by the
authors. However, these algorithms must be replicated and validated as, when these
algorithms are employed to assess OFCs, no true energy expenditure data are available.
Therefore the accelerometer-based estimates are the only energy expenditure metrics
which will be available from these recordings, and validation of the algorithms and
validation of the implementation of the algorithms is required.
In this section, the validation experiment is discussed. Acceleration and EEtrue metrics
c estimation algorithms are
were recorded on a small set of subjects (n = 5), and the EE
57
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Chapter 3:
Table 3.2: Tabulation of the physical characteristics of the subjects who participated in the
accelerometer-based energy expenditure validation test.
ID
Age
(years)
Height
(m)
Weight
(kg)
1
2
3
4
5
23
25
22
24
30
1.75
1.83
1.87
1.79
1.70
77.5
81.1
86.7
87
88
µ±σ
24.8 ± 3.1
1.79 ± 0.07
84.2 ± 4.45
employed on the acceleration data, and these results are compared to the EEtrue data. The
physical characteristics of each subject were recorded and are presented in Table 3.2.
3.3.1
Experimental setup
A treadmill (Powerjog GX100) was set up to operate from 3 — 7 km/h at 1 km/h
increments. The treadmill ran at each speed for four minutes at a gradient of 1%, which
has previously been stated as emulating walking on a flat surface (Jones and Doust,
1996). The recruited participants walked on this while EEtrue was recorded by a cardio
pulmonary exercise testing (CPET) indirect calorimeter (Cosmet, 2013). The CPET was
calibrated before each recording. A gas mask was worn by the subject and it was secured
around the subject’s nose and mouth. Care was taken to ensure that the volunteers were
comfortable with the mask and that no difficulty in breathing was introduced by the
equipment. A hose leaving the mask is attached directly to the gas analysers in the CPET
calorimeter that was used. To ensure that no gas entered or exited the seal about the
subject’s face, the subject was asked to momentarily block the outlet of their gas mask.
If they were unable to express air during this brief time the mask was deemed securely
fastened and the test continued.
The time for which the subject walked at each speed was chosen at four minutes due to
works published by Chatagnon and Busso (2006), and Winter et al. (2006). Chatagnon and
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Section 3.3: Energy expenditure validation
Busso (2006) provides the upper bound of the time that an exercise must be performed for
a body to have reached a steady metabolic state as being between 2 and 3 minutes, subject
to the fitness of the individual. Winter et al. (2006) states that metabolic tests of this nature
should not exceed 20 minutes in length as reliable metabolic response after this time is
unreliable because as the body is exerted to more physical exertion, different attributes of
the body will affect the metabolic rates of subjects. Employing the treadmill speeds with
consideration to Chatagnon and Busso (2006), and Winter et al. (2006) dictates that each
speed should be recorded for four minutes.
3.3.2
Pre-processing
3.3.2.1
Codeword conversion
The accelerometer on the SHIMMER device produces analogue voltages which the analogue
to digital converter (ADC) samples at 12-bits of resolution (allowing for 4096 possible
values). Therefore, the data which is obtained by the microprocessor is in the form of
12-bit digital codewords, and these codewords must be converted to measurements of
acceleration which are measured in g’s.
At rest, the absolute value of the three axes of a tri-axial accelerometer will sum to 1 g, due
to the orthogonal orientation of the axes of the sensor. The absolute value of acceleration
is obtained in the discrete domain by
aabs (k) =
q
ax (k)2 + ay (k)2 + az (k)2 .
(3.31)
Figure 3.2a shows the raw codewords (cx , cy , and cz ) which were recorded by the
microprocessor when the SHIMMER device was orientated on its six faces for equal
periods of time. It should be noted that this data was spliced so that each face of the
SHIMMER device provides equal portions of data which ensures that no axis dominates
59
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Chapter 3:
Codeword
3,000
2,500
cx
cy
cz
2,000
0
10
20
30
40
50
60
70
Time
80
90
100
110
120
100
110
120
(a) Accelerometer codewords on X, Y and Z axes
Gravity (g)
1.4
1.2
1
0.8
0.6
0
10
20
30
40
50
60
70
Time (s)
80
90
(b) Combination of raw accelerometer data obtaining the absolute acceleration
Figure 3.2: Demonstration of the conversion process from the raw digital codewords
obtained from the accelerometer (a) to a measurement of absolute gravity (b).
60
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Section 3.3: Energy expenditure validation
the calibration process.
The signals associated with movements between faces were
removed.
On a perfectly flat and still surface, when the SHIMMER device rests on a given face, the
axis experiencing gravity will read either 1 g or -1 g (depending on the orientation of
the sensor). The remaining axes should read 0 g. In order to convert from codewords to
gravitational units, each codeword must have the 0 g offset removed (cx,0g , cy,0g , and cz,0g )
and must then be scaled by the 1 g value (cx,1g , cy,1g , and cz,1g ). The absolute acceleration
in g’s is then computed with reference to these codewords by
s
aabs (k) =
cx (k) − cx,0g
cx,1g
!2
+
cy (k) − cy,0g
cy,1g
!2
+
cz (k) − cz,0g
cz,1g
!2
.
(3.32)
The optimal values of the converting codewords which can transform raw readings to
gravitational units can be discovered by searching for c0g and c1g in the range [0, 4095] for
each acceleration axis. The values which would be selected are those which minimise the
standard deviation of the resulting vector of aabs . This process results in a search space of
40963 bits per transforming metric, and a brute force search is not feasible to make this
discovery. Therefore, a binary search algorithm was implemented to select these optimal
values in an efficient manner with guaranteed convergence obtained in 12 iterations.
The result of the search yields the acceleration vector shown in Figure 3.2b which has
obtained average acceleration of 1 ± 0.0082 g (µ ± σ) with the values shown in Figure
3.2a as the input. This process provides codeword conversion values which are tolerant to
small orientation errors because the searching routine is performed on the absolute value
of acceleration. The robustness of this algorithm can be seen in Figure 3.2b where the
orientations of the SHIMMER device were not perfectly orthogonal but the original signal
variance is absent from Figure 3.2b.
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3.3.2.2
Chapter 3:
Breath and acceleration synchronisation
The EEtrue levels captured by the CPET were recorded on a breath-by-breath basis, and the
time at which each energy expenditure value is logged is rounded to the nearest second
by the hardware. The breathing rate of a human is non-periodic and non-stationary,
in particular when the subject is exercising (Chon et al., 2009). Therefore, the EEtrue
recordings and the acceleration values computed are not sampled at a common rate, and
the two datasets must be synchronised in time together to allow for comparisons between
c
EEtrue and EE.
The selected synchronisation method involved averaging the EEtrue values which occurred
within the previous 10 seconds to the current EEtrue value. The 10 second window was
chosen as the minimum respiratory rate for healthy adults typically requires 10 seconds
per breath (Lindh et al., 2009), and this rate will increase with exercise. Therefore, a 10
second time window allows for minimum latency and phase difference between EEtrue
values obtained while allowing for the maximum range of respiratory rates.
Consider the (unlikely) situation where a subject does not breathe for 20 seconds, for
example. With the time-window averaging algorithm, no EEtrue signals will be logged
over this time. This is an intuitive consequence to the absence of breathing as without
respiratory effort, the CPET will not log any new energy expenditure data and the
averaging method follows this trend. As a result of holding their breath, when the subject
next breathes, a higher concentration of CO2 will be expressed and a higher volume of O2
will be inspired which is recorded by the CPET and will yield higher EEtrue . By using the
time-window averaging method for synchronisation, the dynamics of EEtrue values will be
accurately reflected in the synchronised array, while with others, for example cubic spline
interpolation, the true dynamics will not be followed.
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Section 3.3: Energy expenditure validation
3.3.2.3
Normalisation
Crouter’s algorithm regresses to METs, which was previously described. As neither Chen
nor Bouten regressed to this unit of energy it was necessary to scale the reference and the
regressed energy values to a common standard so that all algorithms can be compared on
the same grounding. This was accomplished by
fb =
f − min (f )
,
max (f − min (f ))
(3.33)
where f is the signal which is normalised to fb, and the min and max functions select
the minimum and maximum values of the data series respectively. Equation (3.33) is
c signals and scales the range to between 0 and 1.
employed on both the EEtrue and EE
3.3.3
Performance evaluation
The root mean square error (RMSE) can be used to quantify the difference between EEtrue
c mathematically, and it is defined by
and EE
v
t
RMSE =
0
! 1
BB EE
c − EEtrue 2 CC
CC,
B
mean BB@
CA
EEtrue
(3.34)
where mean computes the average of the data array. The RMSE can be converted into
the percentage root mean square difference (PRD) which yields a percentage difference
representation of the two signals by
PRD = RMSE ⇥ 100%.
63
(3.35)
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Chapter 3:
Table 3.3: Table of PRD values computed between true energy expenditure and the
estimated energy expenditure values obtained from the algorithms investigated.
PRD (%)
Algorithm
Bouten
Chen linear
Chen non-linear
Crouter
µ
σ
7.54
8.07
6.57
13.50
1.93
1.32
0.33
1.41
PRD values of 0 indicate that the arrays are identical, and increasing PRD values indicate
that two arrays are becoming more dissimilar.
3.3.4
Results
Table 3.3 tabulates the PRD results which were obtained for each energy expenditure
estimation algorithm in the order in which the algorithms were presented earlier. Chen’s
non-linear algorithm performed best of all of the algorithms investigated over all the
subjects yielding an average PRD of approximately 6.6% while also obtaining the lowest
c are
standard deviation of PRD. Figure 3.3c shows an example where EEtrue and EE
overlaid. It can be seen that the values which were obtained follow the trend in the changes
of EEtrue .
It is not entirely surprising that Chen’s linear and nonlinear algorithms performed
well because the algorithms were designed to automatically specialise for persons of a
known weight, gender, height etc, and because the algorithms were also generated with
reference to the largest amount of data of all the algorithms considered. The fact that the
nonlinear algorithm performance is best suggests accelerometer based energy expenditure
regression is not a linear process averaged over all participants. However, the differences
are small.
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1
1
0.8
0.8
Normalised EE
Normalised EE
Section 3.3: Energy expenditure validation
0.6
0.4
0.2
0.6
0.4
0.2
EEtrue
c
EE
0
0
(a) Bouten.
(b) Chen (linear).
1
1
0.8
0.8
Normalised EE
Normalised EE
EEtrue
c
EE
0.6
0.4
0.2
0.6
0.4
0.2
EEtrue
c
EE
0
EEtrue
c
EE
0
(c) Chen (non-linear).
(d) Crouter.
c and EEtrue values obtained for participant 2. The values obtained are
Figure 3.3: The EE
overlaid.
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Chapter 3:
Crouter’s algorithm (Figure 3.3d) consistently overestimated the energy expenditure
which contributed heavily to it performing poorest out of the algorithms investigated,
yielding a mean PRD of 13.50%. However, the energy trace was smooth and did follow the
trend of the metabolic response, see Figure 3.3d. The smooth nature of this signal trace is
due to how the algorithm considers not only recent data, but also data from the preceding
minute. This introduces low-pass filtering behaviour to the algorithm.
3.3.5
Discussion on energy expenditure estimation algorithms
It is very clear from the PRD values and figures presented that accelerometers are a well
suited instrument for estimating energy expenditure in the situations investigated here.
That Chen’s algorithms provide excellent estimations of energy expenditure is a very
strong indication that subject tailored regression was one of the strongest factors for
accurate regression to energy expenditure values. Chen’s non-linear algorithm resulted in
the lowest overall PRD. This result supports the argument that accelerometry and energy
expenditure are non-linearly related.
The algorithms of Bouten and Crouter do not target the individual but rather were
developed to accommodate the standard user. This carries a convenient ‘plug and play’
feature to the algorithms (i.e. no setup or customisation is required for their use) but in
counterpoint the accuracy of energy expenditure estimation is traded, which can be seen
by high PRD values for Crouter’s algorithm in Table 3.2.
The reported accuracy of all of the algorithms is subject to the reference that their results
are compared against. As the reference energy expenditure levels are not periodic and
the time stamps are rounded to the nearest second, data synchronisation was performed
on the original reference data to coordinate its values to those which were estimated by
c algorithms. If the synchronisation algorithm is not chosen carefully, this process
the EE
can introduce undesirable artefacts to the reference data. The method employed here
66
Section 3.4: Accelerometer-based analysis during OFCs
Niall Twomey
involved averaging the breath data over time window, which achieves a low pass filtering
effect which improves the reliability of the synchronised EEtrue .
Indeed, the reference energy levels can themselves be corrupted before any analysis
is performed.
This can happen on the treadmill if the subject yawns, coughs or
talks. Participants were requested to refrain from doing this, but it is neither possible
or reasonable to stop a subject from coughing during these recordings if the subject
must. These artefacts will accentuate, exaggerate or otherwise affect the accuracy of the
reference values and the times they are measured. As low-pass filtering is achieved by
synchronisation, these artefacts will be reduced in the EEtrue reference data.
3.3.6
Conclusion on energy expenditure estimation
This study shows that accelerometer-based energy expenditure estimation algorithms can
achieve very accurate estimates of true energy expenditure. The SHIMMER device was
also found to be an ideal platform with which to measure the acceleration data. As the
results obtained here were very good and followed the dynamics of the changes of EEtrue ,
the analysis of accelerometer-based metrics for classification of allergy during oral food
challenges will be discussed in the next section.
3.4
Accelerometer-based analysis during OFCs
For the assessment of activity and energy expenditure based analysis of OFCs, the
SHIMMER device was applied to the torso and dominant wrist before the test began, and
then streamed tri-axial accelerometer data to a nearby computer. At this stage, activity
analysis and energy expenditure estimation algorithms which were previously validated
were applied to this new data.
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Niall Twomey
Chapter 3:
Histogram
PDF
Probability
0.2
0.1
0.08
0
−4
−3
−2
−1
0
Feature Value
1
1.5
2
3
4
Figure 3.4: Example histogram and probability density function of a feature.
3.5
Probability density functions
Figure 3.4 shows a sample histogram (blocks) and associated probability density function
(PDF) (solid curve) for a randomly generated normal distribution with a zero-mean and
a unity standard deviation. In general the histograms are normalised so that the area
under the curve sums to 100%. This diagram is indicative of what the distribution of
accelerometer metrics might look like. The probability of achieving an arbitrary x-value
(within a finite range) can be determined visually by projecting vertically upwards from
the selected point until the PDF curve is intersected. The probability of a feature of this
value occurring is then determined from the y-value at the intersection. This is illustrated
by the dotted arrow in Figure 3.4 for a feature value of x = 1.5. Here, kernel density
estimation (KDE) (Sheather and Jones, 1991) was employed in all cases in this thesis to
compute the histograms. This is a procedure which will produce ‘smooth’ histograms
that can be more representative of the true distribution of the process than centred
histogramming (Sheather and Jones, 1991).
This example shows the overall distribution of a feature. By computing PDFs on two
subsets of the data (first the allergic and second the non-allergic subjects, for example)
the characteristic differences between the two classes can be visualised. Figure 3.5 shows
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Niall Twomey
Section 3.6: Results
0.15
PDF Class 1
PDF Class 2
Probability
0.11
0.1
0.05
0.04
0
−5
−4
−3
−2
−1 −0.5 0
0.5
1
2
3
4
5
Feature Value
Figure 3.5: Illustration of the differences between PDFs that describe two separate classes
of data.
how PDFs of the two classes might differ. Two PDFs are shown in this image, representing
the allergic and non-allergic classes. The means of these PDFs are centred at +1 and −1.
Large differences between the means of the two curves indicate that the feature might
be a suitable candidate for separation between allergic and non-allergic subjects. This
is because with a smaller overlap there is a smaller probabilistic uncertainty about the
class of the data (i.e. does the data originate from an allergic or non-allergic subject?). In
contrast to this, if a large overlap exists between the PDFs the metric might not (by itself)
facilitate good class separation. In Figure 3.5 the probability densities for the two classes
are shown for an x = −0.5 where it can be seen that a higher probability of PDF Class 2 is
obtained in comparison to Class 1. However, at x = 0.5 the probabilities obtained for each
class are reversed.
3.6
Results
Section 3.2.2 presented the regression equations which were employed when computing
c for the algorithms being considered, and IAA was used by all of these.
activity and EE
It can be seen that all of these equations consider IAAx , IAAy and IAAz , and Bouten and
Crouter’s algorithms also consider IAAt . PDFs were generated to show the expected values
69
Probability
Niall Twomey
Chapter 3:
Non-allergic subjects
Allergic subjects
0.6
0.4
0.2
0
−5
−4
−3
−2
0
−1
1
2
3
4
5
Probability
(a) Normalised IAAx
Non-allergic subjects
Allergic subjects
0.6
0.4
0.2
0
−5
−4
−3
−2
0
−1
1
2
3
4
5
Probability
(b) Normalised IAAy
Non-allergic subjects
Allergic subjects
0.6
0.4
0.2
0
−5
−4
−3
−2
0
−1
1
2
3
4
5
Probability
(c) Normalised IAAz
Non-allergic subjects
Allergic subjects
0.6
0.4
0.2
0
−5
−4
−3
−2
0
−1
1
2
3
4
5
(d) Normalised IAAt
Figure 3.6: Histograms plotting the normalised IAA values of the allergic and non-allergic
subjects who were investigated.
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Niall Twomey
Section 3.7: Conclusion
of these during OFCs, and Figure 3.6 shows the set of histograms of the IAA metrics that
was obtained.
In every case, a very strong overlap can be seen between the allergic and non-allergic PDFs.
In order for these metrics to be employed for classification, separation between the allergic
and non-allergic curves must be apparent, but these PDFs show very similar distributions.
The set of PDFs show a very high correlation between the allergic and non-allergic classes,
c values which were computed. The EE
c values which were
and this is also reflected in the EE
obtained also offered no distinguishable difference in metrics in a similar manner to the
IAA metrics which are shown in Figure 3.6. Therefore, as the PDFs which are shown in
Figures 3.6a — 3.6d present as overlapped normal distributions, and because functions of
normal distributions are themselves normal distributions (Leon-Garcia and Leon-Garcia,
c estimates offered no better separability than the IAA metrics.
2009), the EE
It should be stated that there will be differences between acceleration obtained from adults
and children. The algorithms employed were tested on adults and worked well, but when
tested on children they worked poorly. However, PDFs that were displayed in Figure 3.6
show activity-based metrics, which are the basis of the activity and energy expenditure
estimation equations. With these data no separability is obtained, and this also occurs
with the energy-expenditure based algorithms. Therefore, it is believed that the poor
performance is not due to age-based discrepancies, but due to the inadequacy of the
accelerometer-based approach for allergy classification.
3.7
Conclusion
The PDFs did not, in any case, present with a mean shift or any exploitable anomaly
between the allergic and non-allergic classes. This indicates that the subjects cannot be
separated by activity analysis by the methods applied here, and therefore classification of
allergy cannot be resolved by these means. Therefore, accelerometer-based analysis is not
71
Niall Twomey
Chapter 3:
an appropriate means of classifying allergy, and will not be employed in the remainder of
this thesis.
72
CHAPTER 4
ECG-based analysis of OFCs
4.1
C
Introduction
HAPTER 3 discussed the use of accelerometer-based activity and energy expenditure metrics for use in the classification of oral food challenges. It was shown
previously that the analysis of these achieved very poor separability between the allergic
and non-allergic classes, and as such are insufficient measures for the classification of
allergy.
During the oral food challenges the SHIMMER device recorded the ECG of the subjects
who underwent these challenges. While it has been observed by the allergists who conduct
the OFCs that there is a tendency for the heart rate of subjects to change before the onset
of an allergic reaction, the effect of allergy on the heart has not been definitively quantified
and this chapter investigates this.
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Niall Twomey
Chapter 4:
Figure 4.1: Einthoven triangle configuration for ECG electrode placement (University of
Nottingham, 2013).
4.2
ECG and HRV
4.2.1
ECG recording
As 12-lead ECG is generally only performed for diagnosis of cardiac disease and stress
tests, for general heart monitoring (i.e. in a hospital ward) 3-lead ECG is recorded. This
employs the limb electrodes which are arranged in the Einthoven Triangle configuration
(Wilson et al., 1946) shown in Figure 4.1 (University of Nottingham, 2013).
Figure 2.4 shows the P–, Q–, R–, S– and T-waves which characterise the ECG (Dublin
Institute of Technology, 2013). While all of the waves can yield diagnostically relevant
information, the QRS complex is the principal feature of the ECG which is utilised for
the identification of heart beats. The intervals between sets of R-R intervals (as shown in
Figure 2.4) are employed to described the heart rate variability mathematically.
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Section 4.3: HRV feature extraction
Epoch Span
Figure 4.2: Illustration of relationship between the ECG and the epoch length for ECG
recorded in OFC.
4.3
HRV feature extraction
4.3.1
Overview
By considering HRV features which are extracted from allergic and non-allergic subjects
independently, the characteristic differences between the two classes can be assessed. This
can be employed to provide meaningful descriptors between allergic and non-allergic
data. In this chapter these differences are analysed and quantified to determine if HRVbased classification of allergy is worthy of investigation.
4.3.2
Epochs
HRV feature extraction is performed based on the times of QRS complexes which were
found within time-windows (known as epochs) of ECG data. This is illustrated by Figure
4.2, where the QRS points found within the shaded region are employed by the feature
extraction for this epoch.
75
Niall Twomey
Chapter 4:
Longer epochs will naturally consider a greater number of QRS points. These will measure
longer term variation of the HRV, whereas shorter epochs will obtain information about
shorter term characteristics of the heart. Long– and short-term epochs are diagnostically
interesting measurements and are both considered in this work.
Various lengths of epoch were investigated for this research. The European Society of
Cardiology and the North American Society of Pacing and Electrophysiology stated that
epochs between 120 and 300 seconds should be considered when extracting HRV features
on adults (Rawenwaaij-Arts et al., 1993). The subjects recorded during OFCs are children,
some of which have resting heart rates (HRs) exceeding 160 beats per minute (BPM)
(Giddens and Kitney, 1985) which is approximately twice the heart rate of the average
resting adult. Therefore, 60 second epochs are also considered for this work. The full
set of epoch lengths which are investigated here are {60, 120, 180, 300} seconds. This set
of epoch lengths was chosen because the effect of allergy on the HRV features has not
been characterised. Therefore, analysis of all of these sets of epochs will qualify whether
signatures of allergy are better obtained with longer– or shorter-duration epoch lengths.
4.3.3
Epoch overlap
Figure 4.3 illustrates the relationship between the ECG, the epoch length and the epoch
overlap. The epochs in this illustration are 9 seconds in length, with one second increments
for illustrative purposes only as as this epoch length is insufficient for meaningful feature
extraction (van Ravenswaaij-Arts et al., 1993). Feature extraction performed on the QRS
points found within the bounds of Epoch 1 quantify the behaviour of the heart between 1
and 10 seconds, while features extracted from QRS points found within the boundaries of
Epoch 2 characterise the heart rhythms between 2 and 11 seconds.
One second increments in time were utilised between epochs, which represents approximately 98% overlap with epoch lengths of 60 seconds. This was selected in order to
increase the number of data points which are available from the OFC. Subject 1 presented
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Section 4.4: Feature normalisation/calibration
Epoch 2
Epoch 1
Figure 4.3: Illustration of relationship between the ECG, the epoch length and the epoch
overlap.
with the shortest challenge which lasted approximately 3 minutes after the first dose of
the allergen was administered. Without any epoch overlap, only three features would be
extracted for this subject after administration of the first dose. However, with 1 second
epoch increments, 180 points describe this period. Subject 19 presented with the longest
OFC recording. Without epoch overlaps, this entire challenge would be described by
approximately 130 data points, but with the overlap, approximately 9000 points will be
used.
Indeed, for classification purposes, due to the short and varied length of the OFCs,
classification algorithms would not have sufficient quantities of data with which to
perform classification without epoch overlap (Duda et al., 1995; Bishop et al., 2006;
Cherkassky and Mulier, 2007; Catal and Diri, 2009), and therefore, as well as employing
one second increments these for distribution analysis in this chapter, small epoch
increments are also requirement for later chapters.
4.4
Feature normalisation/calibration
Chapter 2 presented Table 2.1 which tabulated the subjects investigated in this study.
From this table, it can be seen that the ages of the subjects varied from 7 months to 10
77
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Chapter 4:
years. Infants under one year of age will typically have a HR of ⇡ 120 BPM (with a range
of 80 — 160 BPM) while that of a ten year-old child will typically have a HR of ⇡ 90
BPM (with a range of 70 — 110 BPM) (O’Brien et al., 1986; Tanaka et al., 2001; Kliegman
et al., 2007; Aziz et al., 2012). Because of the age-related differences in the baseline resting
heart rate it is not appropriate to directly compare the features extracted from different
subjects. Therefore, a calibration procedure is performed in order to allow indirect and
valid comparisons between the features extracted from the different subjects.
Typically feature data is normalised by subtracting the mean of the data and dividing
by its standard deviation.
In the case of OFCs this is not appropriate because this
process will force the features obtained from the allergic and non-allergic subjects to have
similar statistical properties and could reduce and possibly eliminate — any characteristic
signatures of allergy in the HRV.
Therefore, the calibration process which is employed here computes the mean and
standard deviation of the features before the problem foods were administered to the
subjects. The entirety of the recording is then normalised by these values. This process
guarantees that the features are normalised by non-allergic HRV data as no allergen would
have been consumed by subjects. Therefore, if features deviate from this distribution as a
result of allergy, this will be observed in the PDFs which were generated for the allergic
subjects (see Chapter 3). Likewise, if, for non-allergic subjects, the features obtained for
the remainder of the challenge should not change. The time before the first dose of the
allergen is administered to the subjects is termed the ‘background’ or ‘baseline’ region
henceforth, and it is guaranteed to present non-allergic HRV features. To define this period
concretely, it is the time after the skin tests have concluded, but before the administration
of the first dose of the allergen.
Normalisation is then achieved by
78
Niall Twomey
Section 4.5: HRV feature categories
f − µb
b
f=
,
σb
(4.1)
where f is the non-normalised feature vector, b
f is the normalised feature, µb and σb
compute the mean and standard deviation of the background segment of feature f . All
background lengths recorded are approximately ten minutes in length, and all PDFs
presented in the following sections are computed from normalised features.
While the normalised background data is characterised with zero mean and unity
variance, non-background data will not preserve these traits. OFCs will induce stresses
to the recruited subjects and consequently the HRV features will tend to deviate from this
background baseline. As a result, the PDFs that are presented later in this chapter will
demonstrate significant probability mass approximately three times beyond the normal
ranges for a normal distribution (i.e. far beyond 5σ). This is explained because the units
of the x-axis are of the units of σbackground and not the overall standard deviation, and
larger multiples of this indicate features that are less similar to this background.
4.5
HRV feature categories
4.5.1
Feature categories
It should be stated here that HRV features extracted over an epoch are employed in order
to measure the characteristics of the distribution. Therefore, much of the information
that is extracted from the data relates to the statistical properties of such distributions,
and the extractions of time domain features such as the mean, standard deviation, etc, is
performed.
79
Niall Twomey
Chapter 4:
However, other feature categories are also extracted. For example, sequential domain
features measure the relative ‘acceleration’ and ‘deceleration’ that the heart rate has
experienced over an epoch length, whereas Poincaré features are employed to assess nonlinear dynamics of the heart rate, and this has been shown to have a close relationship to
the sympathetic indices of the autonomic nervous system (ANS). These features are also
used for many other applications in different fields (Cogdell and Piatetski-Shapiro, 1990).
Other feature types can be extracted which are a measure of the frequency-domain
characteristics of the heart, which have been correlated to the ANS too. Frequency domain
analyses are popular methodologies employed in many engineering and scientific fields.
4.5.2
Frequency domain feature analysis
When computing the frequency spectrum of the heart rate, care must be taken because the
heart rate does not beat periodically (Moody, 1993; Badilini and Blanche, 1996). Therefore,
direct application of the Fourier transform (FT) cannot be employed on the heart rate
data (Moody, 1993; Ebden, 2002; Clifford and Tarassenko, 2005), and the spectrum must
be estimated. Two popular means of extracting the frequency power spectrum from the
irregular data series exist in HRV literature and are discussed below.
4.5.3
Resampling + FFT
With this method, the heart rate series is re-sampled at a periodic frequency to provide
a uniformly sampled data series (Clifford and Tarassenko, 2005, Laguna et al., 1998). A
variety of re-sampling methods exist in literature including nearest-neighbor, linear, cubic
spline and piecewise cubic Hermite techniques (Srikanth et al., 1998). Figure 4.4 shows
the raw HR signal and the interpolated signal re-sampled after performing cubic spline
interpolation. Once the data series has been re-sampled, the FT can be used to compute
the power spectrum from this data series.
80
Niall Twomey
Section 4.5: HRV feature categories
104
Heart rate (BPM)
Original HR
Resampled HR
102
100
98
96
498
500
502
504
Time (s)
506
508
510
Figure 4.4: Illustration of the raw HR (⇤) which is not periodically sampled, and the HR
re-sampled to 10 Hz via cubic spline interpolation.
The FT is defined by Equation (4.2) and allows translation of a real, continuous timedomain signal, x(t), to a frequency-domain representation, X(!).
Z
+1
X (!) =
x(t)e−j!t dt
(4.2)
−1
The discrete Fourier transform (DFT) is used to compute the FT of data segments of
finite length N in the discrete domain. The data segments which are analysed must be
periodically sampled at a frequency f s , and the DFT is described for functions of multiples
of the sampling frequency, and yields powers at frequencies which are multiples of this.
X (!n ) =
N
X
x(tk )e−j!n tk
k=1
81
(4.3)
Niall Twomey
Chapter 4:
The periodogram of the DFT is the estimate of the power spectral density (PSD) of a signal
which is defined by
##
##2
N
##X
#
##
x(tk )e−j!n tk ###
# k=1
#
20
12 0 N
12 3
N
CC BBX
CC 777
1 6666BBBX
x (tk ) cos(!n tk )CCCA + BBB@
x(tk ) sin(!n tk )CCCA 777 .
= 66BB@
5
N4
1
Px (!n ) =
N
k=1
(4.4)
(4.5)
k=1
Using complexity analysis (Lewis and Papadimitriou, 1997) computation of the DFT
can be shown to scale exponentially with regard to N (i.e.
O(N 2 )).
Today, the
DFT is rarely computed from first principles, and complexity optimisation methods
(such as butterflying, memoisation and look-up tables) can be employed to reduce the
computational cost of the algorithm dramatically to O(N log N ) iterations as is the case
with the fast Fourier transform (FFT) (Cooley and Tukey, 1965; Frigo and Johnson, 1998).
4.5.4
Direct PSD estimation of HRV
The Lomb periodogram (Lomb, 1976; Flannery et al., 1992) is a least-squares optimisation
technique which minimises the squared error between a signal, x(tk ), and a reference
signal, s(tk ; !) directly without resampling. Whereas the FT estimation weighs the results
based on the time interval, the Lomb periodogram weighs the data on a per-point basis
(Biala et al., 2010). This is achieved by computing the square error between a signal and
the least squares estimation by
N ✓
◆2
X
✏=
x(tk ) − s(tk ; !) ,
k=1
82
(4.6)
Niall Twomey
Section 4.5: HRV feature categories
where ✏ is the square error value, and s(tk ; !) is a set of reference sinusoids and is defined
by
s(tk ; !) = a1 cos(!tk ) + a2 sin(!tk ),
(4.7)
where a1 and a2 are the amplitudes of the constituent components of the sinusoid. The
full error equation is written as follows.
✏(a1 , a2 ) =
N ✓
X
◆2
x(tk ) − a1 cos(!tk ) − a2 sin(!tk )
(4.8)
k=1
The least squares algorithm optimises the amplitudes, a1 and a2 , to achieve the minimum
square error against the reference. This is achieved by computing two partial derivatives
of Equation (4.8) with regard to the amplitudes of the reference sinusoids, and equating
the results to 0, i.e.
N
✓
◆
δ✏ X
=
−2 cos(!tk ) x(tk ) − a1 cos(!tk ) − a2 sin(!tk ) = 0,
δa1
(4.9)
N
✓
◆
δ✏ X
=
−2 sin(!tk ) x(tk ) − a1 cos(!tk ) − a2 sin(!tk ) = 0.
δa2
(4.10)
k=1
k=1
It is desirable to have
83
Niall Twomey
Chapter 4:
N
X
cos(!tk ) sin(!tk ) = 0
(4.11)
k=1
for computational complexity and orthogonality reasons, as Equations (4.9) and (4.10) can
be rewritten in matrix form by
0N
1 0
1
N
N
X
X
BBX
CC BB
CC
BB
x(tk ) cos(!tk )CCC BBB
cos2 (!tk )
cos(!tk ) sin(!tk )CCC 0B 1C
BB
C
CC BBa1 CC
B
BB k=1
CC BB
CC BB CC
k=1
BBB N
CCC = BBB N k=1
CC BB CC .
N
X
BB X
CC BBX
CC @a A
BB
C
CC 2
B
x(tk ) sin(!tk ) CCA BB@
cos(!tk ) sin(!tk )
sin2 (!tk )
B@
CA
k=1
k=1
(4.12)
k=1
Therefore, when the condition of Equation (4.11) is true, Equation (4.12) becomes
orthogonal. To obtain this condition, a time delay factor, ⌧, is introduced, and ⌧ must
satisfy the criteria that
N
X
cos(!(tk − ⌧)) sin(!(tk − ⌧)) = 0.
(4.13)
k=1
Solving Equation (4.13) for ⌧ yields
0 N
1
BB X
CC
BB
sin(!tk ) CCCC
BB
BB
CC
1
B k=1
CC
⌧=
arctan BBB
CC .
N
CC
2!
BBB X
C
BB
cos(!tk ) CCCA
B@
k=1
84
(4.14)
Niall Twomey
Section 4.5: HRV feature categories
Now, all sinusoidal components are subject to the ⌧ correction factor, and applying this to
Equation (4.12) provides
1
1 0X
0N
N
CC
CC BBB
BBX
2
CC 0 1
C
BB
B
cos
(!(t
−
⌧))
0
x(t
)
cos(!(t
−
⌧))
C
k
B
CC B C
k
k
CC BB
BB
CC BBa1 CC
CC BB k=1
BB k=1
CC BB CC .
CC = BB
BB
CC BB CC
N
CC BB
BB X
N
X
CC @ A
CC BB
2
BBB
C
0
sin (!(tk − ⌧))CCC a2
x(tk ) sin(!(tk − ⌧)) CA BB@
B@
A
(4.15)
k=q
k=1
The optimal amplitudes of a1 and a2 can be computed and the power at a specific
frequency can be estimated by
0N
1
CC
1 BBBX 2
P(!) = BB@
x (tk ) − ✏CCCA .
2
(4.16)
k=1
The full Lomb periodogram is then written by
00
0N
1 1
1
N
✓
◆C2 CC
✓
◆C2 BX
BB BBX
B
C
C
BB
BBB BBB
x(tk ) sin !(tk − ⌧) CCCA CCCC
x(tk ) cos !(tk − ⌧) CCCA
B@
BB @
CC
1 BB k=1
CC
k=1
CC .
P(!) = BBBB
+
CC
N
N
✓
◆
✓
◆
2 BB
X
X
CC
BB
2
2
CC
cos !(tk − ⌧)
sin !(tk − ⌧)
BB
CA
@
k=1
4.5.5
(4.17)
k=1
Comparison of HRV frequency analysis methods
As discussed in Sections 4.5.3 and 4.5.4, there are multiple means by which the PSD of
the heart rate can be computed. A number of open-source HRV tools are available for
free from software repositories which allow users to choose the PSD estimation method
85
Niall Twomey
Chapter 4:
(de Carvalho et al., 2002; Hamilton, 2002; Parvin et al., 2002; McSharry and Cifford, 2004).
Investigations into the benefits of the direct and indirect PSD estimates indicates that the
Lomb periodogram is preferable over the re-sampling methods (Moody, 1993, Laguna
et al., 1998, Chang et al., 2001, Clifford and Tarassenko, 2005). Indeed, McSharry and
Cifford, 2004, designed an open-source ECG generation model where the true spectrum
of the heart signal is set by the user before generating the ECG signals. Comparing the
results of Lomb and resampling techniques with the knowledge of the true underlying
dynamics of the PSD, it was stated that the Lomb periodogram performed in a superior
fashion.
For these reasons, the frequency domain features which are described later were extracted
with the Lomb periodogram spectral estimation method.
4.6
Features
The QRS points which were employed in the generation of the PDFs were manually
annotated. This was performed so that the PDFs which were generated were representative
of the true dynamics of the cardiovascular system for the allergic and non-allergic subjects.
Two PDFs are shown in each chart in this section. The PDF which relates to the allergic
subjects can contain a significant portion of non-allergic HRV features. This is because
it is not known when the allergy begins to affect the ECG. Indeed, obtaining this is the
principal focus of this thesis.
In all subsequent PDF charts, the more densely filled (green) curves represent the allergic
PDFs and those less-densely filled (blue) are the PDFs which were generated for nonallergic subjects.
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Niall Twomey
Section 4.6: Features
4.6.1
Notation
For the description of features, all data is considered to be in the discrete domain. A
subject’s OFC is represented by M feature vectors, with one vector per epoch. A specific
epoch is identified by the subscript j, and the time difference between the QRS points
within the j th epoch are defined as the vector RRj . The i th element of RRj is accessed by
RRj (i). Each epoch contains N QRS points, and the value of N is not necessarily similar
for all epochs. The elements of RR are in the units of seconds.
4.6.2
Time domain features
4.6.2.1
Mean heart rate
The mean heart rate (HR) measures the average heart rate over a given epoch. It is defined
by
HR =
60
,
µ
(4.18)
where µ is the average time between subsequent heart beats in a given epoch, and is
defined by Equation (4.19)
µ=
N
1X
RRj (i).
N
i=1
87
(4.19)
Niall Twomey
Chapter 4:
Non-allergic subjects
Allergic subjects
Probability
0.2
0.1
0
−10
−8
−6
−4
−2
0
2
6
4
Normalised feature value
8
10
12
14
Figure 4.5: PDF of mean heart rate, generated from allergic and non-allergic subjects
4.6.2.2
Standard deviation
The standard deviation of the R-R intervals measures the variability and diversity of the
QRS complexes found within a given epoch. It is defined by
v
u
t
σ=
N
⌘2
1 X⇣
RRj (i) − µ ,
N
(4.20)
i=1
where µ is defined by Equation (4.19). A higher standard deviation of the heart rate
indicates high variance of heart beats within an epoch, and conversely a low standard
deviation indicates consistency in the heart rhythm. The PDF of the standard deviation is
shown in Figure 4.6.
4.6.2.3
Coefficient of variation
The coefficient of variation is a normalised measure of the variance of a series of data. It
is calculated by dividing the standard deviation of the heart rate by the mean of the heart
rate and it is defined by
88
Niall Twomey
Section 4.6: Features
Probability
0.4
Non-allergic subjects
Allergic subjects
0.3
0.2
0.1
0
−4
−2
0
2
6
8
4
Normalised feature value
10
12
14
Figure 4.6: PDF of standard deviation of the heart rate, generated from allergic and nonallergic subjects.
Non-allergic subjects
Allergic subjects
Probability
0.3
0.2
0.1
0
−4
−2
0
2
4
Normalised feature value
6
8
10
Figure 4.7: PDF of coefficient of variation of the heart rate, generated from allergic and
non-allergic subjects.
Coefficient of Variation =
σ
.
µ
(4.21)
The effect of this normalisation procedure on the PDFs of the mean and standard deviation
features (Figures 4.5 and 4.6) is shown in Figure 4.7. The distributions of the standard
deviation and coefficient of variation features are similar, but the coefficient of variation
appears to provide better separation than µ and σ alone.
89
Niall Twomey
Chapter 4:
Non-allergic subjects
Allergic subjects
Probability
1
0.5
0
−2
−1
0
1
2
3
5
4
Normalised feature value
6
7
8
Figure 4.8: PDF of RMSSD of the heart rate, generated from allergic and non-allergic
subjects.
4.6.2.4
RMSSD
The root mean square of successive difference (RMSSD) feature measures the root mean
square (RMS) of the successive differences between the times of the QRS complex. It is
defined by Equation (4.22).
v
u
t
RMSSD =
N −1
⌘2
1 X⇣
RRj (i) − RRj (i − 1)
N −2
(4.22)
i=2
Figure 4.8 shows the PDF of this feature. A high correlation between the allergic and nonallergic subject data can be seen in this figure. However, the probability of the allergic
category is greater towards the more positive values of the normalised feature. The shape
of this feature is not normal. It is uncertain why this shape occurred, but as the QRS points
are manually annotated there is confidence that this distribution is accurate.
90
Niall Twomey
Section 4.6: Features
Listing 4.1: Calculation of NNx .
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
/ / Input
//
RR :
//
N:
//
x:
parameters
p o i n t e r t o t h e RR a r r a y
L e n g t h o f RR a r r a y
Difference threshold
/ / Returns
//
nn :
The number o f QRS p o i n t s t h a t d i f f e r
//
by o v e r xms i n t h e g i v e n e p o c h
i n t nn ( double *RR, i n t N, f l o a t x ) {
int n = 0;
f o r ( i n t i =1; i <N; i ++ )
i f ( RR[ i ] −RR[ i −1] ≥ x )
n++;
return n ;
}
Listing 4.2: Calculation of pNNx .
1
2
3
4
5
6
7
8
9
10
11
12
/ / Input
//
RR :
//
N:
//
x:
parameters
p o i n t e r t o t h e RR a r r a y
L e n g t h o f RR a r r a y
Difference threshold
/ / Returns
//
pnn : The p e r c e n t a g e o f QRS p o i n t s t h a t d i f f e r
//
by o v e r xms i n t h e g i v e n e p o c h
double pnn ( double *RR, i n t N, f l o a t x ) {
return ( double ) nn ( RR , N, x ) / ( double ) ( N − 2 ) ;
}
4.6.2.5
NN/PNN
The NNx and PNNx features describe the number of successive QRS points within an
epoch that differ by the time x. Their calculation is described in 0-indexed C code shown
in Listings 4.1 and 4.2.
Typically values of 25 ms and 50 ms are used. Figure 4.9 shows the allergic and nonallergic distributions of PNN50 .
91
Niall Twomey
Chapter 4:
Non-allergic subjects
Allergic subjects
Probability
0.15
0.1
0.05
0
2
6
−14 −12 −10 −8 −6 −4 −2 0
4
Normalised feature value
8
10 12 14
# of occurrences
Figure 4.9: PDF of PNN50 of the heart rate, generated from allergic and non-allergic
subjects.
60
40
20
0
0.36 0.38 0.4 0.42 0.44 0.46 0.48 0.5 0.52 0.54 0.56 0.58 0.6
R-R Interval (s)
Figure 4.10: Histogram of the relative times between successive QRS complexes.
92
Niall Twomey
Probability
Section 4.6: Features
Non-allergic subjects
Allergic subjects
0.2
0.1
0
−12 −10 −8
−6
2
6
−4 −2 0
4
Normalised feature value
8
10
12
14
Figure 4.11: PDF of histogram of the heart rate, generated from allergic and non-allergic
subjects.
4.6.2.6
Histogram
Features can also be calculated through generation of a histogram of the R-R intervals
from within an epoch. Figure 4.10 shows a typical histogram of R-R intervals which was
constructed with 10 bins. The feature value is then calculated by
hist =
h
,
w
(4.23)
where w is the width of the histogram, i.e. the difference between the maximum and
minimum values on the x-axis, and the h is the height of the most frequently occurring
bin.
Smaller values of this feature indicate a wide dispersal of R-R intervals over the epoch
owing to the smaller h and larger w values, while larger values of this feature indicate
consistency between R-R intervals over an epoch. This is reflected in the PDF of this feature
shown in Figure 4.11 where a higher probability of allergy can be seen with lower values
of this feature. Indeed, this feature shows the best difference between the allergic and
non-allergic subjects of all the features which were discussed up until now.
93
Niall Twomey
Chapter 4:
0.15
0.1
∆RRj+1 (s)
PP
0.05
0
NN
−0.05
−0.1
−0.1
−0.08 −0.06 −0.04 −0.02
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
∆RRj (s)
Figure 4.12: Chart of the change between successive QRS complexes.
4.6.3
Sequential domain features
Features extracted from the sequential domain compute the inter-beat variability of the
QRS complexes found within an epoch. The relative increases and decreases of the heart
rate are computed by differentiating the vector of R-R intervals between each successive
QRS complex (effectively obtaining the relative proportions of ‘acceleration’ of and
‘deceleration’ of the heart rate over an epoch (Schechtman et al., 1992)). Quantification
of this can be visualised by plotting the ith result of the difference of the R-R intervals
against the (1 + i)th . Figure 4.12 shows this plot and the graph is segmented into four
quadrants.
The shaded region in the upper right hand quadrant of Figure 4.12 indicates that two
consecutive increments in time between successive QRS complexes occurred, which is
representative of a slowing heart rate. This region is referred to as the PP quadrant. The
shaded region in the lower left hand quadrant of Figure 4.12 indicates the presence of two
consecutive R-R intervals with a decreasing interval, which indicates a speeding heart rate.
This quadrant is referred to as NN quadrant.
94
Niall Twomey
Section 4.6: Features
Probability
0.2
Non-allergic subjects
Allergic subjects
0.15
0.1
0.05
0
−20
−15
−10
−5
0
5
Normalised feature value
10
15
Probability
(a) PDF of PP feature of the heart rate, generated from allergic and non-allergic subjects from the
sequential domain.
Non-allergic subjects
Allergic subjects
0.15
0.1
0.05
0
−14 −12 −10 −8
−6
2
6
−4 −2 0
4
Normalised feature value
8
10
12
14
(b) PDF of NN feature of the heart rate, generated from allergic and non-allergic subjects from the
sequential domain.
Figure 4.13: PDFs derived for the sequential domain features.
The sequential trend features are calculated by counting the number of occurrences of
points within the PP and NN quadrants. These are then divided by the total number of
points in the chart. The PDF of the features are shown in Figures 4.13a and 4.13b. A
significant amount of similarity exists between these two features. However, in both cases,
lower values of the feature are more indicative of allergy.
4.6.4
Poincaré features
Poincaré features are another means of assessing the beat-to-beat variability and nonlinear dynamics of R-R intervals. Poincaré features are obtained from a Poincaré chart,
95
Niall Twomey
Chapter 4:
0.6
Original
Rotated
0.2
SD1
RRn +1 (s)
0.4
0
SD2
−0.2
0
0.2
0.4
RRn (s)
0.6
0.8
Figure 4.14: Original and rotated points plotted in a Poincaré Chart.
which plots the current R-R interval against the next R-R interval, as is shown by the
cluster of ⇥’s in Figure 4.14. This cluster will be dispersed about a line orientated at 45◦ ,
which plotted as the solid line in Figure 4.14.
Features are extracted based on the extent of horizontal and vertical distribution of the
⇥’s about the solid line, i.e. perpendicular and parallel to the 45◦ line. Computer-based
computation of these features is simplified if each point is rotated clockwise by 45◦ about
the origin, which can be achieved by the rotation matrix defined in Equation (4.24), with
✓ = − ⇡4 . This results in the cluster of ◦’s in Figure 4.14 which are dispersed about y = 0 on
the x-axis.
3
2
> 66cos(✓) − sin(✓)77
77
6
77
y 0 = x y ⇥ 6666
7
4sin(✓)
cos(✓)5
>
x0
96
(4.24)
Niall Twomey
Section 4.6: Features
Non-allergic subjects
Allergic subjects
Probability
0.2
0.1
0
2
6
−14 −12 −10 −8 −6 −4 −2 0
4
Normalised feature value
8
10
12
14
(a) PDF of CSI of the heart rate, generated from allergic and non-allergic subjects.
Non-allergic subjects
Allergic subjects
Probability
0.15
0.1
0.05
0
−20
−15
−10
−5
0
5
10
Normalised feature value
15
20
25
(b) PDF of CVI of the heart rate, generated from allergic and non-allergic subjects.
Figure 4.15: CSI and CVI PDF from Poincaré features.
With the rotated cluster, two measurements from the plot (SD1 and SD2) can easily be
computed. SD1 is the standard deviation of the set of rotated x-values, and SD2 is the
standard deviation of the set of rotated y-values. Points close to the line of identity indicate
a similarity between consecutive beats, and conversely points which are further from the
line of identity indicate that a change in the heart rhythm has occurred.
The cardiac sympathetic index (CSI) and the cardiac vagal index (CVI) are computed from
SD1 and SD2, and they are defined by Equations (4.25) and (4.26) respectively.
97
Niall Twomey
Chapter 4:
CSI =
SD2
SD1
(4.25)
CVI = log (SD2 ⇥ SD1)
(4.26)
Figures 4.15b and 4.15a show the PDFs of the CSI and CVI features for the allergic and
non-allergic subjects. The CVI feature shows a positive mean-shift between allergic and
non-allergic subjects, with higher probabilities of allergic subjects at the higher positive
side of the chart. The CSI feature for allergic subjects presents a wider distribution of the
feature but with a similar mean between the allergic and non-allergic subjects.
4.6.5
Frequency domain features
The frequency spectrum was computed with the Lomb periodogram. The total powers
in the very low frequency (VLF), low frequency (LF), and high frequency (HF) bands
were extracted from the PSD estimates from the Lomb periodogram. Gombarska and
Horicka, 2012, presented Table 4.1 in which the boundaries for VLF, LF, HF and ultra-low
frequency (ULF) are presented. These frequency ranges are indicative of physiological and
cardiac events which are listed alongside the frequency ranges in Table 4.1.
The ULF frequency band was not considered in this work because it is stated by researchers
(Tulppo and Huikuri, 2004; Rajendra Acharya et al., 2006) that meaningful determinations
of the associated powers can violate the rules governing PSD determinations. This concern
Table 4.1: Table of HRV diagnostic frequency ranges for children.
Type
Frequency (Hz)
Origin
HF
LF
VLF
ULF
0.15 – 0.4
0.04 – 0.15
0.0033 – 0.04
< 0.0033
Parasympathetic, respiratory sinus arrhythmia
Sympathetic + parasympathetic
Sympathetic, chemo-receptors, thermoregulation, endocrine
Circadian rhythms
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Probability
0.2
Non-allergic subjects
Allergic subjects
0.15
0.15
0.1
0.1
0.05
0.05
0
Non-allergic subjects
Allergic subjects
0.2
−20
−10
0
0
−5
0
5
10 15 20
Normalised feature value
Normalised feature value
(a) PDF of the power from VLF frequency band of the
heart rate, generated from allergic and non-allergic
subjects.
Probability
0.25
Non-allergic subjects
Allergic subjects
(b) PDF of the power from LF frequency band of the
heart rate, generated from allergic and non-allergic
subjects.
0.25
0.2
0.2
0.15
0.15
0.1
0.1
0.05
0.05
0
−5
0
5
10
15
20
Normalised feature value
0
−5
Non-allergic subjects
Allergic subjects
0
5
10
15
20
Normalised feature value
(c) PDF of the power from HF frequency band of the (d) PDF of the power from LF/HF frequency band
heart rate, generated from allergic and non-allergic of the heart rate, generated from allergic and nonsubjects.
allergic subjects.
Figure 4.16: PDF of the frequency domain features.
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is also valid for the VLF frequency band, but it has been clinically reported to be strongly
related to the parasympathetic response, even in short duration recordings, (Carney et al.,
2001; Kleiger et al., 2005). The ratio between the HF and LF frequency bands was
computed as another feature, and it is a measure of the radio between the sympathetic
and parasympathetic response of the ANS.
The PDFs of the frequency-domain HRV features were extracted and are shown in Figures
4.16a — 4.16d. The non-allergic and allergic PDFs overlap significantly, but in all cases
there is tendency towards a higher probability of allergy towards the positive and negative
extremities of the independent axis. It is expected that these features will perform well in
allergy classification because when allergy occurs the ANS should react to combat allergic
reactions.
4.7
Discussion
This is the first work which has quantified the effect of allergy on HRV features, and it has
shown that in a number of cases a clear separation can be obtained between the allergic and
non-allergic classes. In the case of the CVI feature, for example, a mean-shift and wider
variance of approximately 3σbackground was obtained. It is interesting to note that with
the accelerometer features no deviation beyond the background distribution was observed
while for the HRV features this is not the case. This trait is one that when identified can be
exploited by classification platforms. This is a desirable trait for classification purposes as
it indicates that separability and classification are obtainable. This is in counterpoint to the
accelerometer-based features that were obtained in the previous chapter, and it indicates
that HRV-based classification exposes a viable avenue for automated allergy classification.
The sequential domain features also demonstrated significant differences in probability
mass and bimodalities were introduced to the allergic PDFs. This modality-distortion also
occurred (but to a lesser extent) with the histogram and PNN features. It is interesting
to see that the frequency-based histograms are not well-partitioned, with the mean and
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Section 4.7: Discussion
variance of the distributions being approximately equal for the allergic and non-allergic
classes. These features are well documented by other researchers (Aziz et al., 2012) as
being highly correlated to the activity of the ANS.
It should be stated that the figures in this chapter presented histograms in a single dimension. In later chapters it will be shown that employing high dimensional multivariate
data modelling can be exploited to yield superior separability. Such high dimensional
representations, however, cannot be visualised on the pages of this thesis, and true
separability of the classes can only be obtained analytically. The focus of the remaining
chapters of this thesis lies in the means of representing and assessing these distributions.
The PDFs shown here also show that a high degree of overlap exists between the
allergic and the non-allergic subjects.
This is to be expected because OFCs are not
temporally annotated, which indicates that supervised class separability is not possible.
However, this further supports the argument that machine-based classification of allergy
through analysis of HRV features is worthwhile, because even with single dimensional
representations of features, class separability has been obtained in many cases.
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CHAPTER 5
Machine learning for allergy
classification
5.1
I
Introduction
N the previous chapter, the eighteen HRV features which are used to quantify the
variability of the heart during OFCs were described and these assess whether machine
learning algorithms might be utilised for automatic classification of food allergy.
The only temporal label available is that obtained from the ECG data recorded before
allergens were administered. Therefore, non-discriminative classification is required for
allergy detection as the allergic events are not available. By this process, models are
trained on the normalised background heart rate variability features, and allergy will be
classified if features anomalous to this distribution are detected. The background data
is recorded before any food was ingested by the subjects, so is guaranteed to represent
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Section 5.2: Novelty detection for OFC
the normal, non-allergic state. This classification routine is called novelty or abnormality
detection.
As the QRS points used here were annotated, this chapter will determine the effectiveness
of HRV-based allergy detection, and the manual aspect of QRS identification provides
affirmation of this. The results obtained here will also provide the upper-bound of the
estimated efficacy of machine-based allergy classification.
5.2
Novelty detection for OFC
5.2.1
Choice of classification routine
Chapter 2 discussed the one-class SVM and GMM classifiers.
For the classification
work of this thesis, GMMs were chosen over one-class SVMs and other novelty detection
algorithms for a number of reasons. GMMs model the distribution of the training data and
have been shown to be robust in a number of classification applications. Effectively, this
means that the GMMs generate multi-dimensional PDFs which can be used to ascertain
the ‘probability’ (or more formally, the likelihood, see later) that new data belong to the
background class. GMMs also allow for subject-adaptive procedures, which is important
for the classification routine which is employed, and this will be discussed later. Oneclass SVMs belong to the boundary-of-novelty estimate. This is useful for very high input
dimensionality when not many data points are available. Therefore, on the basis of these
arguments, one-class GMMs are preferable over other options for OFC classification.
5.2.2
Feature transformation
For the classification of allergy, principal component analysis (PCA) (Pearson, 1901) is
first performed on the normalised training data. The PCA transformation was performed
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in order to de-correlate the feature set. This is a requirement for allergy classification as
the allergy database is insufficiently sized to accurately train GMMs with full covariance
matrices.
Using diagonal covariance matrices dramatically reduces the quantity of
samples required for training of the classification models. It is not always possible to
assign interpretation to these new components obtained with the PCA transform and this
is particularly true in cases where normalisation has been performed on the initial training
data (Webb et al., 2011).
For high-dimensional data, the PCA transform is equivalent to minimising the scatter
matrix, Si , over N dimensions, by
N
X
(xj − µ)(xj − µ)T ,
Si =
(5.1)
j=1
where
N
1X
xj .
µ=
N
(5.2)
j=1
The PCA transformation matrix is obtained from the eigenvectors of St , and these can
be obtained singular value decomposition (De Lathauwer et al., 2000) or other similar
procedures.
Figure 5.1 illustrates a two-dimensional example of what the PCA transform accomplishes.
Figure 5.1a shows two-dimensional data plotted in feature space.
In viewing the
histograms associated with each axis it can be seen that the range and variance of feature
values are approximately equal in the x– and y-directions. However, once the PCA
transform has been performed, the feature data is transformed to the distributions shown
in Figure 5.1b which is plotted in ‘component’ space. This is equivalent to viewing the
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Section 5.2: Novelty detection for OFC
(a) Two-dimensional feature-space, showing the contours lines of the axis of the
first principal component. Histograms show the distribution of features along
the x– and y-axes with equal bin ranges.
(b) Two-dimensional component-space after the PCA transformation. Histograms show the distribution of features along the x– and y-axes with a much
greater variance range with the x-axis.
Figure 5.1: An illustration of PCA in two-dimensional feature space (subplot a) and twodimensional component-space (subplot b).
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original data along axes shown as the contour lines in Figure 5.1a. In Figure 5.1b, the
ranges of the feature data are not equal. Indeed, the computed variance of the first
component accounts for 97% of the total variance of the transformed feature data, while
in the original distribution the importance of both features is approximately equal.
For higher dimensional cases, if the first C (C < N ) principal components account for
the majority of the variance, they may be used to describe the variance profile of the
data accurately. If the remainder of the matrix is discarded in analysis, dimensionality
reduction is achieved. It has been shown by other researchers that employing the PCA
transform to select a subset of features in a subject-independent manner can improve the
accuracy of classification (Thomas, 2010).
5.2.3
Gaussian mixture models
The Gaussian classifier is a density estimation algorithm which is commonly used for
modelling data distributions. For classification purposes features are often extracted from
the data to obtain more information about the data itself. While the original data may be
normally distributed, the extracted features may not preserve this trait. This is a problem
for the Gaussian classifier because multi-modal distributions would be poorly modelled
by a single Gaussian distribution.
Gaussian mixture models (GMMs) were devised to solve this problem. These employ
weighted mixtures of several Gaussian distributions to model arbitrary feature distributions. In a similar manner to how the FFT decomposes a data sequence into a weighted
sum of complex exponentials and computes the frequency spectrum, GMMs (through the
expectation maximisation algorithm, see later) can be thought of as decomposing a data
sequence to a basis of Gaussian distributions and obtaining a model of the density of the
data. The mixture of Gaussians, therefore, allows arbitrary and non-trivial distributions
to be modelled, provided the algorithm is correctly parameterised.
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Probability density
0.2
A
B
C
Total
0.15
0.1
0.05
0
−6
−5
−4
−3
−2
−1
0
Value
1
2
3
4
5
6
Figure 5.2: A mixture of three equally-weighted Gaussians (dashed lines) which combine
to represent a multi-modal non-normal distribution (solid black line).
Figure 5.2 demonstrates how three equally-weighted Gaussian distributions, A, B and C,
can be combined in order to represent the non-Gaussian distribution, shown by the solid
line in Figure 5.2. The number of Gaussians that are required to represent the distribution
depends strongly on the data which are to be represented, and is specific to the application
in question.
For GMMs, conditional density is modelled by
p(x|µi , Σ i ) =
m
X
!i N (x|µi , Σ i ),
i=1
where m is the number of Gaussians, the weights, !i , satisfy
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(5.3)
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Chapter 5:
m
X
!i = 1 and
(5.4)
!i ≥ 0, 8i,
(5.5)
i=1
and N (x|µi , Σ i ) is the multivariate Gaussian function defined by
N (x|µi , Σ i ) =
1
1
exp{− (x − µ)T Σ −1 (x − µ)},
2
(2⇡) |Σ|
d
2
1
2
(5.6)
where µ and Σ (which are collectively termed the mixture parameters and given the
shorthand symbol θ for conciseness) are mean and variance matrices, and d is the
dimensionality of the data, x. Σ is a symmetric and positive semi-definite matrix and
|Σ| is its determinant. The task of training the GMM involves selecting the appropriate
mixture parameters to represent the training data accurately. This is performed through
the use of k-means clustering and the expectation maximisation algorithm.
5.2.3.1
k-means clustering
For the GMM algorithm, m Gaussians are required to model the training data set, and
the value of m is termed the GMM order. The k-means clustering algorithm (Hartigan
and Wong, 1979) can be employed to automatically partition the training data set into k
data clusters and discover the mean (or centroid) of each of these clusters. The k-means
algorithm is iterative in nature, and will generally have indeterminable computation time
(Lewis and Papadimitriou, 1997; Aloise et al., 2009). The following steps outline the
procedure of the k-means algorithms:
1. Initialise k initial cluster centroids randomly in feature space.
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Section 5.2: Novelty detection for OFC
2. Calculate the Euclidean distance (Deza and Deza, 2009) between all data points and
the k cluster centroids.
3. Assign membership of each data point to the closest centroid.
4. Compute the new cluster centroids for the k clusters based on the members of each
cluster.
5. If no change in centroid coordinates have occurred between steps 2 and 4, the process
is terminated. Otherwise repeat step 2 using the new cluster centroid positions that
were calculated in step 4.
The k-means algorithm will always segment the data into k clusters there is no guarantee
that good partitioning will be obtained. Therefore value of k (which is also termed
the Gaussian order, m) must be chosen carefully as the data might be automatically
clustered into an inappropriate and unrepresentative number of clusters. However, kmeans clustering partitions data without consideration to the density of the data points
which are under investigation. To partition data in this manner, expectation maximisation
(EM) is required.
5.2.3.2
Expectation maximisation
EM takes into consideration not only the means of the clusters, but also the weights and
covariance between features and it can provide superior data segmentation than k-means
alone (Figueiredo and Jain, 2000). EM is typically initialised by the k-means clustering
algorithms, and seeks to select the mixture parameters so as to model the training data
accurately. This is achieved with a maximum-likelihood algorithm, and is therefore
iterative in nature as no closed form solution exists (Bishop et al., 2006).
The likelihood is maximised by EM in order to ensure that the distribution of the training
data is modelled accurately. In order to simplify computation, and for computer-based
floating point precision, the log-likelihood is taken and this requires N floating point
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Chapter 5:
5
Feature 2
0
−5
−10
−15
−10
−8
−6
−4
−2
0
Feature 1
2
4
6
8
(a) The true relationship between the two Gaussian distributions which make up the nonnormal distribution.
5
Feature 2
0
−5
−10
−15
−10
−8
−6
−4
−2
0
Feature 1
2
4
6
8
(b) The partitioning of the classes obtained by k-means clustering (k = 2).
5
Feature 2
0
−5
−10
−15
−10
−8
−6
−4
−2
0
Feature 1
2
4
6
8
(c) The partitioning of the classes obtained by the expectation maximisation clustering
(m = 2).
Figure 5.3: A demonstration of the difference in clustering which is obtained by k-means
clustering and the expectation maximisation algorithm. With subfigures B and C, a line is
drawn from each point to its associated cluster.
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Section 5.2: Novelty detection for OFC
additions rather than N floating point multiplications (where N is the number of examples
of the data).
L(Xi |Θi ) = log
N
Y
p(x j |Θi )
(5.7)
j=1
=
N
X
log
j=1
m
X
!l p(x j |θj )
(5.8)
l=1
Like any optimisation algorithm, EM selects parameters so as to minimise a loss function.
This process repeats until convergence has been reached or until a pre-specified number of
iterations have been performed. EM, therefore, iteratively updates the mixture parameters
simultaneously until termination criteria are satisfied. EM is a two-step procedure where
an expectation calculation step (E) is first performed. The expectation is the product
between the true data distribution and the model of the distribution. EM can therefore be
thought of as selecting the mixture parameters so as to maximise the correlation between
the true data distribution and the model of the data distribution. The maximisation (M)
step selects the next arguments for Equation (5.8) based on the outcome of the previous
expectation stages. EM maximises the likelihood because even though the parameters
which maximise the likelihood are initially unknown, the knowledge of the distribution
of the training data can be used to assess the parameters which were selected on every
iteration, and the iterative process guides the algorithm to convergence.
Figure 5.3 shows a distribution which was generated with two Gaussian distributions
centred at (−2, −2) (marked with ⇤) and (3, 2) (marked with 4), with standard deviations
of 3 and 1 respectively. While the two distributions are presented in Figure 5.3 as two
classes, this is to illustrate the difference between cluster membership of k-means (Figure
5.3b) and EM (Figure 5.3c). It can be seen that k-means partitions the distribution poorly
and mis-categorises a number of data points (obtaining accuracy of less than 90%). The
EM partitioning algorithm captures much information from the original distribution, and
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Chapter 5:
·10−2
10−1
Likelihood
8
10−2
6
Background
Checkups
Likelihood
10−3
0
11
20
40
Time (minutes)
Background
µ
µ±σ
4
60
(a) Likelihood series.
0
10
20
30
40
# of occurrences
50
(b) Sample histogram of background likelihood
data series.
Figure 5.4: Sample likelihood (subplot a) and histogram of the background likelihood
(subplot b) of Subject 23.
allows for better modelling of the true data (obtaining over 98% accuracy). While the
partitioning is not perfect with either method, EM models the data in a superior manner
because of the density-based analysis.
5.2.4
Postprocessing
After learning the mixture parameters, GMMs produce likelihoods for features extracted
from new epochs of normalised HRV data. The likelihoods computed are in the range of
0 and 1. The closer the likelihood is to 1 the more likely it is that the data belongs to the
background class, and conversely the smaller the likelihood is the less likely it is that the
feature vector belongs to the background class. The likelihood will never reach 0, but will
become infinitesimally smaller as the data becomes less similar to the background models.
A sample likelihood (for Subject 23) is shown in Figure 5.4a. In this figure, the region
highlighted between 0 and 11 minutes represents the background time (before any
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Section 5.2: Novelty detection for OFC
allergen was introduced to the OFC). The remaining time segments which are highlighted
represent checkup times during which the allergist performed checkups on the subject.
The solid trace is the likelihood series computed for the subject. This figure is plotted in a
log-scale.
With the goal of machine-based allergy detection, the criterion for novel data (which is
classified allergic) is defined as follows: the likelihood must fall below a specific threshold,
th, for a specific duration, d. This is defined thusly because allergy should present
with non-background features (see Chapter 4), and the more unlike the background
new features are, the smaller the computed likelihood will become. However, the PDFs
in Chapter 4 also show a significant amount of overlap between the allergic and nonallergic subjects. Therefore, by incorporating the duration parameter, rejection of spurious
deviations will be achieved, which will enable superior classification.
The specific values of d and n are obtained via subject independent cross validation.
The n and d parameters are henceforth referred to as the ‘multiplicative’ and ‘duration’
parameters individually, and collectively are termed the ‘decision making’ parameters.
Figure 5.4b shows the histogram of likelihood values during the initial background state of
Subject 23 during OFC, and it can be seen from this example that the values of likelihood
follow an approximately normal distribution. Therefore the threshold equation is defined
as a function of the mean (µ) and standard deviation (σ) of this background data by
th = µ − nσ,
(5.9)
where n is a multiplicative factor for the standard deviation. This was chosen to make the
modelling and decision making subject-adaptive, as it is a function of the subject’s own
background distribution. The larger the n parameter the smaller the likelihood must be to
surpass the threshold, i.e. the less similar to the background data.
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Chapter 5:
Decision making
ECG
QRS
HRV
Classification
Postprocessing
Result
Figure 5.5: Flowchart of classification procedure involving the recording of ECG,
annotation of QRS complexes, feature extraction and the classification procedure of OFC.
The second parameter for an allergic decision is d, which is the time for which the
likelihood must remain below the threshold th for an allergic decision to be reached. The
purpose of this parameter is to reduce the effect of spurious irregularities in the likelihood
series which might not be due to allergy, but could be due to the natural variation of the
heart. However, the duration parameter will also allow for less extreme threshold values
which will facilitate in obtaining better classification results. The signature of abnormality
which will be detected by this classification routine can be defined as being a substantiated
and sustained departure from the background HRV levels.
5.3
Classification procedures
Figure 5.5 shows a high-level overview of the allergy classification procedure. The ECG
is first recorded. As mentioned in the introduction, analyses in this chapter focus on
manually annotated QRS points in order to confirm the presence of allergic signatures
in HRV features. The next step in this process is manual annotation of the QRS points.
The features described in Chapter 4 are then extracted and background models are learnt.
Following this, post-processing and decision making is performed in order to classify the
subject in question as being allergic to the allergen they are being tested against. The
specifics of the classification and post-processing stages will be discussed in subsequent
sections.
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Section 5.3: Classification procedures
Model
Selection
Training data
Data
selector
PCA Model
Perform
PCA
GMM Model
Generate
Likelihood
n, d
Decision
making
Machinebased
result
Testing routine
Figure 5.6: Illustration of the data segmentation and testing routines employed in the
allergy classification procedure.
For the remainder of this thesis, machine-based detection of allergy is termed classification of allergy, while the result of the OFC is termed diagnosis of allergy. When a subject
is stated as being ‘classified allergic’, there is, therefore, an inherent implication that it
was with the statistical modelling and post-processing processes that the classification
was made.
The classification and post-processing blocks of Figure 5.5 can be expanded to what is
shown in Figure 5.6. In this Figure, model training and testing data are separated. From
the training data, PCA and GMM models are selected by the parameter selection routine.
The selected multiplicative and duration post-processing parameters are also obtained
from the training data and are used by the system to classify allergy on the test subject.
This figure shows how the testing and training sections of this classification routine are
completely independent, and that the testing data bears no influence in the model and
decision making parameter selection routines.
This system only classifies allergy, i.e. it detects abnormal HRV features only. If only
normal features are detected tests cannot terminate and will continue as normal.
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5.4
Classifier model selection
5.4.1
Performance evaluation
There are various performance assessment routines proposed in the literature (Kohavi
et al., 1995) such as bootstrapping, split-sample, etc.
Their effect on the reported
performance for neonatal seizure detection has been compared in previous studies (Temko
et al., 2011b). The split-sample method where one fixed partition of the available data is
allocated for training and the rest is used as a testing set has several major disadvantages
as such a division results in a potentially large bias. Over-optimistic and indeed overpessimistic results can be obtained depending on what seems an arbitrary partition of the
data yielding a ‘good’ or ‘bad’ split.
In this work, leave-one-out (LOO) is used to assess the performance of the developed
allergy detector. With this all but one subject are used for training and the remaining
subject is used for testing. The process is repeated until every subject was tested, and the
average performance is reported. LOO is known to be an almost unbiased estimation of
true generalisation error (Vapnik and Kotz, 1982). Additionally, in contrast to randomised
re-sampling routines (bootstrapping), the LOO eliminates any subjectivity from the testing
protocol, hence it can be repeated and exactly the same results will be obtained (Thomas
et al., 2013).
What is examined with the LOO procedure is not a particular model, but the methodology
used to obtain such a model. This means that a good modelling system is obtained by this
methodology, and the parameters are not fixed for all subjects. Here, 24 data splits of 23
vs. 1 are made by the LOO method formed the performance assessment routine, and this
means that 24 unique GMM models are obtained by this procedure.
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Section 5.4: Classifier model selection
5.4.2
Parameter selection
5.4.2.1
Search space
In each of these 24 splits, nested cross-validation model selection on the training 23
subjects’ data was performed to choose suitable model parameters. Those include:
• Percentage of information retained by PCA for feature set reduction:
The following set of values was searched: { 80%, 90%, 95%, 99%, 99.9%, 100% }
This range was selected because of the complicated nature of diagnostic HRV, and
because preliminary analyses showed that the allergic condition was better identified
by preserving more than 80% of the feature variance.
• The number of Gaussians in the GMM model:
The following set of values was searched: { 1, 2, 4, 8, 16, 32, 64 }
This was in order to facilitate modelling the data distributions with simple and
complicated models depending on the complexity of the data used in training. This
search space has also been successfully employed for EEG and speech processing
applications (Reynolds and Rose, 1995; Thomas, 2010).
• The multiplicative factor (n) in decision making:
The integer-rounded values logarithmically distributed over the maximum range
were searched.
This range was selected in order to accommodate the entire range of likelihoods
which are required. With the logarithmically distributed range more precision is
obtained at the lower ranges without affecting the limits of classification.
• The duration parameter (d) in decision making:
The integer-rounded values logarithmically distributed over the maximum range
were searched.
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5.4.2.2
Chapter 5:
Cost function
Fully automated machine learning cannot replace allergists who conduct the OFCs. This is
due to the fact that the allergists are required to administer the doses of the problem food
to the subjects throughout the challenge, and, should allergy occur, they will be required to
administer antihistamines. Therefore, the classification routine which is discussed here is
designed as a diagnostic assistance tool, and should complement the diagnosis of allergy
in a way that improves diagnoses. The cost function which is defined was designed in
collaboration with the clinicians in order to best suit their diagnostic needs.
The consequence of false positive classifications would yield unacceptable effects on the
quality of life of the subjects (Chapter 1). With this consideration, the parameters found
within the search space which achieve 100% specificity in the nested cross validation (i.e.
models which correctly classify all non-allergic training subjects) are initially selected.
This ensures that the parameters were selected based on obtaining no false positive
classification in training. From this reduced set, the parameters which lead to the highest
sensitivity are selected. If there are more than a single set of parameters that satisfy
these conditions, the parameters which achieve the maximum total time gain are used.
Sensitivity, specificity and time gain are defined in subsequent sections.
The search space was searched in a targeted, but exhaustive manner. This was because
only a small portion of the decision making parameters satisfy the criteria mentioned
above. Therefore, it was possible to avoid computing the majority of the parameters in the
exhaustive enquiry. Other parameter selection routines, such as receiver operating curves,
would require the entire search space to be investigated. However, with consideration to
the previously defined cost function it is possible to reduce the computation significantly
and target the exhaustive search to only the range of values that satisfy the decision making
criteria.
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Section 5.5: Classification metrics
Diagnosis
Classification
p
n
p
True
positive
False
negative
n
False
positive
True
negative
Figure 5.7: The confusion matrix showing how sensitivity and specificity are obtained
with regard to the ground truth (diagnosis) and predicted (classification) results.
5.5
Classification metrics
5.5.1
Sensitivity/specificity
The measurements of sensitivity (Se) and specificity (Sp) were computed in order to
measure the overall accuracy of the allergy detection framework. These are commonly
referred to as the true positive and true negative rates respectively. They are both bounded
between 0 and 100%, and higher values indicated better accuracy. Both sensitivity and
specificity measure classification accuracy against the ground truth diagnosis and are
defined by
Se =
TP
⇥ 100%,
TP + FN
(5.10)
Sp =
TN
⇥ 100%,
TN + FP
(5.11)
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where true positive (TP) are the number of allergic subjects who were classified allergic,
false negative (FN) are the number allergic subjects who were misclassified as non-allergic,
true negative (TN) are the number non-allergic subjects classified as non-allergic, and false
positive (FP) are non-allergic subjects who were classified as allergic. A confusion matrix
can be employed to visualise the means by which these metrics are computed, and is shown
in Figure 5.7. In this figure, p is a positive result (i.e. allergic) and n is a negative result (i.e.
non-allergic). Sensitivity is an assessment of the two upper quadrants while specificity is
represented in the lower two quadrants.
5.5.2
Time gain parameters
Other metrics which provides an insight into the algorithmic performance are related to
the time gain. Three time gain metrics are calculated for the allergic subset of subjects
only, as no time gain can be obtained for the non-allergic subjects.
5.5.2.1
Time gain
Time gain measures the difference in time between termination of the OFC by the allergists
and classification of allergy. In effect it demonstrates whether it is possible to conclude
OFC earlier and reduce the overall risk of anaphylaxis and other strong reactions due to
early administration of antihistamines.
The average time gain factor is reported in two ways: first as the sum of time gains divided
by the number of allergic subjects (always 15 in this study), and second as the total time
gain divided by the number of subjects whose allergy was detected by the framework.
These two metrics will be equal if perfect sensitivity is obtained, and are termed total
time gain (TGT) and specific time gain (TGS) respectively. The remainder of the time gain
parameters have associated total and specific results.
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The reason for calculating these two time gain metrics is because this allergy detection
platform is envisioned as an diagnosis assistance tool. Therefore it is appropriate to
quantify its effectiveness with the entire set of allergic subjects, but also its effectiveness
on the subset of correctly classified subjects is also of interest.
5.5.2.2
Doses saved
The time gain factor can be converted to another metric which measures the number of
doses of the allergen which would not need to be administered if the allergy classification
was introduced and the OFC was halted when automatic detection diagnosed allergy. This
metric is termed doses saved henceforth.
This doses saved metric is a measure of the risk reduction that can be achieved when
allergy detection is employed, as with fewer doses administered there is a smaller
likelihood of allergic reactions presenting.
5.5.2.3
Activation percentage
An additional time gain metric is also calculated which determines the percentage of the
allergen which was required to be consumed for abnormal HRV-features to be detected
by the classification routine. This metric is related to the doses saved metric in a nonlinear manner, and, therefore, also gauges the risk reduction that is gained by automatic
classification.
For example, if a subject reacts after consuming five doses of an allergen, but the novelty
detection framework detects allergy after three doses, it can be stated retrospectively that
only 22.5% of the consumed allergen was required to induce signatures of allergy on the
HRV features. This percentage is termed the activation percentage. With N total doses
administered to a subject, and s doses saved by the classification, the activation percentage
is calculated by
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N
−s
X
Activation percentage =
i=1
N
X
2i−1
⇥ 100%,
(5.12)
2i−1
i=1
which, for the previously stated example results in
1+2+4
⇥ 100%
1 + 2 + 4 + 8 + 16
7
⇥ 100%
=
31
Activation percentage =
= 22.5%.
5.6
Results
5.6.1
A brief note on the structure of these results
This section presents the results which were obtained with an epoch length of 60 seconds.
While epoch lengths of 120, 180, and 300 seconds were also investigated, the results which
were obtained at an epoch length of 60 seconds will first be demonstrated. Later, results
will be presented which show what occurs at the different epoch lengths, before finally
discussing a more optimal classification routine which is employed for the remainder of
this thesis. The purpose of separating the results is to first introduce the methodology of
performance assessment before finally discussing a more optimal routine.
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Likelihood
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Checkups
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Fail times
10−3
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0
10
20
30
50
40
Time (minutes)
60
70
80
Figure 5.8: A demonstration of early detection of allergy (with Subject 11). The segments
at 45, 60 and 80 minutes which fall beneath the threshold were classified as allergy.
5.6.2
Results obtained at epoch length of 60 seconds
The overall results which were obtained at an epoch length of 60 seconds are presented
in Table 5.1. In this table, the first 15 subjects were diagnosed as allergic, and the final
9 subjects were diagnosed as non-allergic. The diagnosis column shows the diagnosis
of allergy for the subject, and 1 indicates allergic, and 0 indicates non-allergic. The
prediction column presents the results which were obtained by the classification routine,
and 1 indicates a classification of allergic and 0 indicates a non-allergic classification. The
time gain column presents the time gain results which were obtained. The cells which are
marked as ‘—’ did not achieve any time gain. The time gain metric is only applicable for
subjects who were diagnosed allergic.
It can be seen from Table 5.1 that all of the non-allergic subjects were classified as nonallergic obtaining 100% specificity. Subjects 1, 3, and 13 were not classified as allergic,
however, and this yields sensitivity of 80%.
Figure 5.8 shows how allergy was detected before the OFC was terminated. In this Figure
the shaded regions represent the background and checkup times. The signal trace is the
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Table 5.1: Classification results obtained with the novelty detection routine at epoch
length of 60 seconds.
Subject ID
Diagnosis
Classification
Time gain
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
0
0
0
0
0
0
0
0
0
0
1
0
1
1
1
1
1
1
1
1
1
0
1
1
0
0
0
0
0
0
0
0
0
—
35.0
—
16.0
34.0
12.0
29.0
12.0
25.0
5.0
40.0
19.0
—
94.0
13.0
—
—
—
—
—
—
—
—
—
likelihood which was calculated and the segments of the likelihood which satisfied the
allergic criteria are indicated with ⇥ markers. The likelihoods obtained during checkup
times are not considered as allergic. The start of the final checkup period is when the
allergist concluded the OFC.
The challenge shown in Figure 5.8 was concluded by the allergist at approximately the 85th
minute when symptoms of allergy manifested. It can be seen from the example in Figure
5.8 that the subject is classified allergic by the system developed here approximately 40
minutes sooner than the challenge was concluded by the allergist.
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When considering the time gain metrics for the entire database, this achieved TGT of 22.26
minutes, TGS of 27.83 minutes, two of five doses were saved which yields an activation
percentage of 22.5%.
5.6.3
Overall results
For all epoch lengths, 100% specificity was obtained. Sensitivities of 80% were obtained at
epoch lengths of 60 and 180 seconds, and 73% sensitivity was obtained with epoch lengths
of 120 and 300 seconds. It should be noted here that every subject is classified by different
modelling parameters and this is due to the LOO procedure which was incorporated. It
should also be noted that different modelling and decision making parameters are utilised
by the different epoch lengths for the same reason.
5.6.4
Inconsistent classification at different epoch lengths
Subjects 2, 3, 13, 14 and 15 were not consistently classified for different epoch lengths.
5.6.4.1
Short-duration signatures of allergy
Figures 5.9a — 5.9d demonstrate this inconsistency with Subject 2. The highlighted
regions in these figures represent the background and checkup periods, and the solid trace
is the likelihood which was calculated over the OFC. Satisfaction of the allergy criteria
occurred once in these figures and is marked with an arrow in Figure 5.9a at approximately
65 minutes. It can be seen here that the departure which classified Subject 2 as allergic in
Figure 5.9a is reduced in significance with longer epochs, until in Figures 5.9c and 5.9d,
the anomaly is indistinguishable from the likelihood trace.
With the longer epochs, the extent of this departure is averaged with ‘regular’ features,
and as such the likelihoods at these times are less pronounced which reduces the extent
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Likelihood
(a) Epoch length of 60 seconds.
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Likelihood
(b) Epoch length of 120 seconds.
10−1
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70
Likelihood
(c) Epoch length of 180 seconds.
10−1
10−3
0
10
20
30
40
50
60
Time (s)
70
(d) Epoch length of 300 seconds.
Figure 5.9: Example demonstrating how the generated likelihood for Subject 2 satisfies
the allergy criteria at an epoch length of 60 seconds (subplot a) while failing to do so for
epoch lengths of 120, 180 and 300 seconds (subplots b — d).
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of the departure at these feature lengths. In effect longer epochs will act as a higher-order
moving average filter which reduce the significance of these deviations with background
HRV metrics, while the shorter epochs allow for classification of the segment due to higher
relative importance of novel HRV features.
5.6.4.2
Longer signatures of allergy
The opposite of this phenomenon can also occur where longer feature lengths enhance
deviations from the background. Figure 5.10 shows how Subject 13 is misclassified as
non-allergic at epoch lengths of 60, 120 and 180 seconds. However, it can be seen
that with the longer epochs certain departures from the background likelihood become
more pronounced (e.g. at approximately 70 minutes). With an epoch length of 300
seconds Subject 13 is correctly classified as allergic at approximately 35 minutes, which
is approximately 70 minutes before the challenge was terminated. This is not an isolated
departure from the background levels either, and at 70, 90 and 100 minutes allergy is also
detected in the likelihood trace.
5.6.4.3
Tolerance to non-allergic variances
The likelihood trace in Figures 5.9 and 5.10 all surpass the threshold at some instance in
time, e.g. at the very end of every trace with Subject 2, and at approximately 90 minutes
with Subject 13. Yet, it is only at certain epoch lengths that the allergic criteria are satisfied.
This is due to the inclusion of the duration parameter in decision making. Without this
parameter, a lower threshold (i.e. a larger departure from the background) would be
required to classify allergy in order to reject deviations from the non-allergic subjects.
Figure 5.11 shows the likelihood trace for Subject 16, who was diagnosed non-allergic
and also classified as non-allergic at all epoch lengths. In this Figure it can be seen that
with longer epoch lengths the baseline of the likelihoods is nearly continuously departed
from the background levels of the first checkup. At all epoch lengths (in particular at 50
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100
(a) Epoch length of 60 seconds.
10−1
10−3
0
10
20
30
40
50
60
70
(b) Epoch length of 120 seconds.
10−1
10−3
0
10
20
30
40
50
60
70
(c) Epoch length of 180 seconds.
10−1
10−3
0
10
20
30
40
50
60
70
(d) Epoch length of 300 seconds.
Figure 5.10: Example demonstrating how the generated likelihood for Subject 13 does not
satisfy the allergy criteria at an epoch length of 60, 120 and 180 seconds (subplot a — c)
but the criteria are then met for the epoch length of and 300 seconds (subplots d).
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Section 5.6: Results
minutes with an epoch length of 120 seconds in Figure 5.11b) the likelihood surpasses the
threshold at multiple occasions. However, with the inclusion of the duration parameter,
none of these points are flagged as allergic, and the subject is correctly classified as nonallergic by the classification routine.
It can be seen in Figure 5.11 that with longer epoch lengths, the baseline of the likelihood
traces becomes less similar to the background recordings. This is because Subject 16
became agitated during the OFC. This agitation was fueled by the periodic checkups, and
this can be seen directly after the administration of the first sub-portion at approximately
10 minutes. This agitation was noted when the ECG was recorded during the OFC. This
agitation demanded a number of extra checkups during the challenge as the allergists were
required to verify that the agitation was not as a result of allergy.
It is believed that the cause for the agitation was psychological: the subjects who are tested
for allergy in this manner have been repeatedly told before the OFC that consuming the
allergen may cause them to become sick. Subject 16, for example, was tested against
egg which was in the form of a cake. Cakes are instantly recognisable to a six-year-old
child, and repeated subjection to the food they live in fear of (Chapter 1) resulted in
the subject being continuously agitated which resulted in almost all of the likelihoods
calculated during the OFC being departed from ‘normal’ background. Even though the
subject did not react to the food, there is an underlying fear that they might, and this fear
is not inhibited by the fact that the challenge is closely monitored by allergists. Without
the inclusion of the duration parameter in the classification routine, these non-allergic
subjects would increase the value of the multiplicative parameter of the threshold equation
in order to satisfy the 100% specificity criteria in training. This would consequently
require a larger deviation from the background for allergy classification and might not
facilitate as high sensitivities as were obtained.
Figure 5.11a may appear to display a substantiated departure from the background
recording, so it is interesting to note that even with this variance, the classification routine
did not once classify allergy for this subject, but in all cases correctly classified them
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60
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100
120
Likelihood
(a) Epoch length of 60 seconds.
10−1
10−3
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20
40
60
80
Likelihood
(b) Epoch length of 120 seconds.
10−1
10−3
0
20
40
60
80
Likelihood
(c) Epoch length of 180 seconds.
10−1
10−3
0
20
40
60
80
(d) Epoch length of 300 seconds.
Figure 5.11: Example demonstrating how the generated likelihood for Subject 16
surpasses the threshold, but does not satisfy the allergy criteria due to the inclusion of
the duration parameter and for all epoch lengths the subject is correctly classified as nonallergic.
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Section 5.6: Results
as non-allergic. Even with the presence of ‘agitation’, it is demonstrated that all nonallergic subjects can be classified correctly, which allows justification to the claim that
the allergy detection platform demonstrated here is robust in classification, and robust in
discrimination between allergy and allergy-like HRV features.
Subject 1 was not classified correctly at any epoch length. This is because this challenge
was concluded very abruptly three minutes after the challenge began and because a
signature of allergy which was detectable by the means discussed here did not present
in the ECG of this subject. Therefore, it is believed that perfect sensitivity is not possible
to achieve perfect sensitivity on the ECG of the allergy database.
5.6.5
Boosted allergy classification
Here, ensemble-based result fusion is performed which will be shown to obtain more
optimal classification performance.
5.6.5.1
Sensitivity/specificity
Based on the inconsistent classification results which were obtained, it appears that
the detection of abnormal signatures on the HRV is a dynamic process which is not
suited to one particular epoch length. Therefore, to obtain more optimal results, the
remainder of this chapter and thesis will employ classifier fusion based logically OR-ing
the results which were obtained for the individual epoch lengths. This is employed in
EEG classification applications where individual channels are OR-ed together. The ORing process is justified because it does not violate the subject independent nature of model
and post-processing parameter selection.
Table 5.2 shows the tabulation of allergy classification with regard to the epoch length.
In this table, only Subjects 1 — 15 (i.e.
the allergic subjects) are tabulated.
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Table 5.2: Tabulation of the classification results of the allergic subjects where ‘1’
represents an allergic classification (TP) whereas ‘0’ represents a non-allergic classification
(FN).
Epoch length
Subject ID
Logical OR
60
120
180
300
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
0
1
0
1
1
1
1
1
1
1
1
1
0
1
1
0
0
0
1
1
1
1
1
1
1
1
1
1
0
1
0
0
1
1
1
1
1
1
1
1
1
1
1
0
1
0
0
1
1
1
1
1
1
1
1
1
1
1
0
0
0
1
1
1
1
1
1
1
1
1
1
1
1
1
1
Sensitivity
Specificity
80.00%
100.0%
73.33%
100.0%
80.00%
100.0%
73.33%
100.0%
93.33%
100.0%
elements signified by ‘1’ indicate that the subject been correctly classified as allergic and
‘0’ represents false negative classification.
The fifth classification result column which is emphasised by the emboldened characters
show the results which are obtained by logically OR-ing the classification results obtained
at each of the epoch lengths. In the cases of the individual epoch lengths the highest
sensitivity obtained was 80% (12/15 correct classification) which were obtained for 60
and 180 seconds, but by considering the logical OR-ing process, 93.33% sensitivity (14/15
correct classifications) is obtained, and only one false-negative classification was achieved.
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Table 5.3: Classification result, time gain, doses saved and activation percentages obtained
by the classification routine. The results in this table were obtained by fusing the results
obtained for the individual epoch lengths together.
5.6.5.2
Subject ID
Classification
Time gain
Doses saved
Activation
percentage
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
0
1
1
1
1
1
1
1
1
1
1
1
1
1
1
—
35
55
17
39
20
30
48
27
32
42
22
73
94
14
—
2
3
0
2
0
1
2
1
2
2
1
4
4
1
—
22.58
9.68
100.0
22.58
100.0
42.86
22.58
33.33
14.29
22.58
33.33
3.23
3.23
48.39
µ
σ
µ
σ
93.33%
—
93.33%
—
36.53
23.91
39.15
22.50
1.66
1.30
1.78
1.25
38.58
34.26
34.18
30.88
Time gain parameters
Table 5.3 presents the set of time gain results which were obtained by the automatic classification of allergy when employing classifier fusion. As no false positive classifications
were obtained here, this table only presents the allergic subjects. This table presents the
time gain, doses saved and activation percentage values which were obtained.
5.6.5.2.1
TGT and TGS
Column three of Table 5.3 tabulates the set of time gain
results which were obtained for the allergic subjects. In this column the ‘—’ symbol is
used to mark when the classification routine failed to classify allergy. The mean and
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standard deviation of the total and specific time gains (TGT and TGS respectively) are
also presented.
The logical OR-ing classification process benefits from selecting the best time gain metrics
from the set of time gains which were for every epoch length. Subjects 4 — 12 are classified
as allergic at all epoch lengths, and of these, Subjects 4, 5, 7, 9, 11 and 12 allergy were
classified at approximately the same time in every instance. However, with Subjects 6,
8 and 10 the OR-ing process adds an additional 60 minutes time gain in comparison to
the results obtained from the individual epoch length cases, which contribute overall time
gain metrics which are approximately 60 minutes greater than the largest individual epoch
length based procedure. For example, between 60 second epoch results and the merged
results, both TGT and TGS increased by approximately 12 minutes. The significance of
obtaining high time gain metrics is that emergency rescue medication could be quickly
administered which could introduce the possibility of reaction-free OFCs for subjects who
would suffer from a reaction with the current OFC.
5.6.5.2.2
Portions saved Column four in Table 5.3 presents the number of portions
saved which would have been obtained had allergy classification been employed during
the OFC recordings.
Approximately 1.8 portions are saved when allergy is classified. In three cases, however,
with Subjects 1, 4 and 6, no portions were saved.
With Subjects 4 and 6 allergy
was classified, but owing to the fact that no additional portions of the allergens were
administered between the classification of allergy and the diagnosis of allergy, the full
amount of the food was required for the detection.
5.6.5.2.3
Activation percentage The final time gain parameter which is calculated
is the activation percentage, and this is shown in the fifth column of Table 5.3. The
activation percentage is the percentage of the allergen which was required to be consumed
before signatures of allergy in HRV features were detected by the allergy classification
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framework. In Table 5.3, values of 100% indicate that the entire dose of the allergen was
required.
For Subjects 1, 4 and 6, as no doses were saved from consumption, the activation
percentage of 100% was achieved. However, in the case of Subjects 4 and 6, time gains
of 17 and 20 minutes were still obtained.
Overall, approximately 40% of the doses administered to the subjects are required for
allergic classification based on the HRV features. This figure is reduced to approximately
30% when considering only the subjects who were correctly classified allergic. This value
indicates that when machine-based allergy classification is achieved, consumption of less
than one third of the dose required for a diagnosis of allergy is required for classification
of allergy (or with a 70% reduction in exposure to the problem food).
5.7
Discussion
A close inspection of several subjects is required to provide additional insight into the
robust nature of the allergy classification system’s behaviour.
5.7.1
Specificity of OFC classification
The importance of obtaining very high specificity was discussed previously. The significance of mis-classifying non-allergic subjects would have a negative effect on their quality
of life indefinitely (Sicherer et al., 2006; Cox et al., 2008). This would be an unacceptable
consequence, so parameters were selected to not misclassify allergy on the training data.
This characteristic was preserved with the unseen testing subjects. It is very significant
that this was obtained as the testing data remains unavailable during parameter selection
(see Figure 5.6). Indeed, because perfect specificity was obtained in this section, it can be
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stated that the classification of allergy is equivalent to the diagnosis of allergy, due to the
subject-independent nature of the means in which the parameters were selected.
5.7.2
Robust classification
The heart rate of Subject 20 presented with frequent isolated arrhythmia events. This is
a condition where the heart rate changes from its resting rate to a much higher value, the
heart rate will then relax to its resting value (Clarke et al., 1976). Figure 5.12 shows an
example of the arrhythmia for this subject. Figure 5.12a shows the raw ECG signal, with
the QRS complexes identified with ⇥, while Figure 5.12b depicts the beat-to-beat heart
rate which was derived from the QRS points.
The resting heart rate can be seen to be approximately 100 BPM before the arrhythmia
incidents at 2,758 — 2,760 seconds.
The heart rate then reduces by over half to
approximately 34 BPM at 2,760 seconds as no QRS complexes have occurred, before rising
to approximately 250 BPM soon after. The arrhythmia incidents occur between 2,758 and
2,762 seconds. Subject 20 experienced these arrhythmia events in a number of occasions
during their food challenge. However, even in light of these abnormal heart beats — which
occurred in some instances over five times per minute — Subject 20 was correctly classified
as non-allergic at all epoch lengths.
It was previously discussed how Subject 16 presented with unusual HRV features which
were due to agitation that the subject felt throughout the challenge. Yet, this subject was
also correctly classified as non-allergic by the classification routine presented here at all
epoch lengths.
The fact that these two non-allergic subjects who presented with abnormal HRV features
were both correctly classified as non-allergic in all routines investigated shows that the
classification routine presented in this chapter is robust in discrimination between allergic
and non-allergic subjects. Indeed, the system is robust in discrimination between allergic
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Amplitude (µV)
400
200
0
2756 2757 2758 2759 2760 2761 2762 2763 2764
Time (s)
Heart Rate (BPM)
(a) Raw ECG trace presenting with arrhythmia between seconds 2758 and 2762.
200
100
0
2756 2757 2758 2759 2760 2761 2762 2763 2764
Time (s)
(b) The beat-to-beat heart rate calculated and the effect of arrhythmia beats on this.
Figure 5.12: Example of arrhythmia on the ECG trace (a) and the effect this has on the
heart rate (b) on Subject 20.
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and allergic-like signatures (i.e. HRV signatures which are affected by agitation and
arrhythmia).
It is very common to pre-process extracted QRS points and remove arrhythmia and ectopic
beats with HRV. This was not performed here for a number of reasons, principally because
later chapters will employ classification routines which discover QRS points automatically.
It is possible for non-QRS points to be mis-labelled as QRS points by these algorithms
under the influence of artefacts. Therefore, by allowing arrhythmia and ectopic beats in
this training set, the developed models should be more tolerant to artefacts, see Chapters
6 and 7.
5.7.3
Parameter selection
Many factors can influence parameter selection. In the attempt to achieve the optimal
results, post-processing parameters were selected to achieve 100% specificity and the
maximum sensitivity on the training data. Yet, the values chosen will be in a region where
there is the possibility that mis-classifications might occur. This can be seen in Figure
5.11 where individual cases of the likelihood surpass the threshold at regular intervals,
and it is with the selection of appropriate duration parameters that the previously stated
robustness is obtained. This is evidenced by the fact that perfect specificity was obtained
at all epoch lengths and that very high sensitivity was also obtained.
5.7.3.1
Importance of correct parameter selection
Subjects 1, 2 and 3 presented with HRV features which did not vary greatly due to their
allergic reactions, and it is because of this this fact, the selection of PCA and GMM
parameters is crucial for appropriate emphasis of novel regions. Figure 5.13 shows the
likelihood of Subject 2 where the PCA and GMM parameters were selected manually to
illustrate how a poor choice of modelling parameters can affect the range of the computed
likelihoods. The extent of the anomaly which classified the subject as allergic at the 65th
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Likelihood
100
10−1
10−2
10−3
0
10
20
30
40
50
60
70
80
90
100
Time (minutes)
Figure 5.13: The likelihood series chosen for Subject 2 which does not diverge from the
background level significantly enough to classify allergy. PCA preserved 80% of the
feature variance which was modelled with a GMM order of 32 at an epoch length of 60
seconds.
minute in Figure 5.9a was not equalled in Figure 5.13. Indeed, the deviation shown in this
figure is no more varied than the likelihoods obtained in the background.
This example illustrates the importance of parameters selection, and expresses the fact
that allergy is non-trivial to detect through statistical HRV feature analysis while also
demonstrating the data-driven nature of the classification procedure.
5.7.3.2
Alternative parameter selection
Appendix A presents a study in which alternative parameter selection routines to those
employed in this chapter were investigated. It shows that while the means by which
parameters are selected is important, alternative methods can be utilised which will also
achieve 93% sensitivity and 100% specificity. However, the selection routine in this
chapter is time gain aware (i.e. parameter selection selects the parameters which obtained
the best time gain results in training data) and those are discussed in Appendix A were
not. This is reflected in the results which were obtained, and in all cases the parameter
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Chapter 5:
selection routine described in this chapter achieved superior time gain, doses saved and
activation percentage results. This provides evidence that the parameter selection and
cost function routines which were discussed in this chapter are very well suited for the
classification problem.
5.7.4
Role of classification in OFCs
The classification routine presented here is well suited as a diagnostic assistance tool.
This is because the allergist can never be replaced, as remote monitoring of physiological
signals cannot administer allergens or antihistamines when required. Therefore, the
allergist will always be present during OFC, and if this classification system is to be used
in conjunction with the standard OFC, machine learning algorithms can greatly assist.
Excellent time gain metrics were obtained, and on average, approximately 35 minutes
would have been saved by this classification routine. This time could be employed to good
advantage for the administration of antihistamines which could reduce — and possibly
eliminate! — allergic symptoms and reactions in some cases.
For machine-based allergy classification, false negative classifications (i.e. classifying an
allergic subject as non-allergic) can be tolerated, as these challenges reduce to the standard
OFC, which is the current state of clinical art. The classification routine here should
complement the diagnosis of allergy and should be used alongside the allergists. With
having obtained 100% specificity in a subject-independent manner, machine-based allergy
detection can introduce many improvements to the clinical diagnosis of allergy.
It is worth noting that allergists diagnose allergy in these patients by having access
to more subjective signals and information sources such as mood and temper which
were not available for this automated analysis. These are not employed for machine
learning purposes because it is difficult to monitor these objectively and in a non-invasive
manner. Non-invasive monitoring is important for allergy detection, as the introduction
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Section 5.7: Discussion
of discomfort might aggravate the subjects, and may increase the number of non-allergic
subjects who presented with likelihood traces similar to Subject 16.
Clinically other signals, such as the blood pressure, blood oxygen saturation and temperature, will be the last physiological metrics which change as a result of allergy. As the
goal of this research is to detect allergy as early as possible, only the ECG is recorded.
However, the system developed here presents an additional insight into the temporal
nature of subjects’ states of allergy during OFCs, and even without the extra data which
the allergists have access to, excellent results were obtained.
141
CHAPTER 6
Automatic QRS detection
6.1
T
Introduction
HE features which were extracted for classification in Chapter 5 were extracted
from QRS points which were manually annotated. These were used in order to
definitively assess whether it was possible to identify allergic subjects through analysis
of HRV features which has not been done before. It was discovered that allergy affects
HRV in a manner which allows classification, and therefore automated QRS detection is
assessed here before fully automated allergy classification is assessed in Chapter 7.
6.2
QRS detection
Software-based QRS detection has been an important research topic for more than 40 years
(Kohler et al., 2002). At the core of QRS detection are algorithms which were designed to
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Section 6.2: QRS detection
specifically enhance the QRS complex of the ECG while diminishing the levels of artefact
and other features of the heart beat (i.e. P– and T-waves).
All QRS detection algorithms require a thresholding stage, and the underlying algorithms
which are employed cover a wide range of disciplines of DSP. The rich set of algorithms
which can be employed reflects how technological ability has evolved over the years in
which QRS detection has been researched, and popular methods can involve derivative
processing (Okada, 1979; Fraden and Neuman, 1980; Ahlstrom and Tompkins, 1983;
Arzeno et al., 2008); digital filter banks and wavelets (Gyaw and Ray, 1994; Di Virgilio
et al., 1995; Bahoura et al., 1997; Afonso et al., 1999; Chen et al., 2006; Strang and
Nguyen, 1996); template matching (Dobbs et al., 1984); adaptive filtering architectures
(Kyrkos et al., 1987; Hamilton and Tompkins, 1988; Thakor and Zhu, 1991); artificial
neural networks (Xue et al., 1992; Vijaya et al., 1998; Rajendra Acharya et al., 2003);
transformation methods (Bolton and Westphal, 1981a, 1984; Benitez et al., 2000, 2001),
and new methods are always being investigated.
The reason that many different areas of signal processing have been employed for QRS
detection is because it is very difficult to generalise one algorithm towards the complete
set of ECG waveform shapes. The extent of algorithms which exist is also indicative of the
fact that it is not appropriate to cater towards ‘well behaved’ ECG only, because the shape
of the ECG and the resulting HRV features can be affected by age (Nunan et al., 2010; Aziz
et al., 2012), gender (Antelmi et al., 2004), heart disease (Rajendra Acharya et al., 2006),
physical fitness (Hamer and Steptoe, 2007) and even by coffee consumption (Monda et al.,
2009). These differences in ECG-shapes can result in missed or extra QRS points which
each have the effect of inaccurately reporting the HRV features.
6.2.1
QRS validation
Of the previously mentioned algorithms, each has their own advantages, but in every
case it is necessary to validate the QRS points with hard limits which generally cannot
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Chapter 6:
be exceeded, i.e. the maximum heart rate that can be obtained is age-dependent and is
only surpassed in very exceptional circumstances. For this reason, routines have been
developed which validate QRS points which are returned by QRS detection algorithms. A
set of rules were defined by Hamilton (2002) and are reproduced here:
• Ignore all peaks that precede or follow larger peaks by less than 200 ms.
• If a peak occurs, check to see whether the raw signal contained both positive and
negative slopes. If not, the peak represents a baseline shift.
• If the peak occurred within 360 ms of a previous detection check to see if the
maximum derivative in the raw signal was at least half the maximum derivative of
the previous detection. If not, the peak is assumed to be a T-wave.
• If the peak is larger than the detection threshold call it a QRS complex, otherwise
call it noise.
• If no QRS has been detected within 1.5 RR intervals, there was a peak that was larger
than half the detection threshold, and the peak followed the preceding detection by
at least 360 ms, classify that peak as a QRS complex.
These rules provide the ability to increase the accuracy of the QRS detection which was
performed.
6.2.2
Validation databases
Typically, QRS detection algorithms will be validated on databases which provide an
abundance of healthy and unhealthy ECG shapes. This strategy allows for objective
comparisons between QRS detection algorithms with regard to detection accuracy and
computational complexity on challenging data.
If QRS detection was assessed on
data recorded from healthy volunteers only, quantification of arrhythmias and other
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Section 6.2: QRS detection
cardiovascular defects would not be addressed, and accuracy would be very subjectively
reported.
One of the more popular databases on which QRS detection is assessed is the Massachusetts
Institute of Technology Beth Israel Hospital (MIT-BIH) database (Mark et al., 1982;
Goldberger et al., 2000; Moody and Mark, 2001). This database consists of 48 half-hour,
two-channel ambulatory ECG recordings, which were recorded between 1975 and 1979.
Twenty-three of the recordings were manually selected at random from a set of 4,000 in–
and out-patients. The remainder were selected from the same set to include clinically
significant arrhythmias which were not well represented by the initial selection. Twicevalidated expert annotations accompany this database.
In this chapter, QRS detection results will be discussed from the MIT-BIH and allergy
databases to verify the assessment of QRS detection. When discussing the MIT-BIH
arrhythmia database, individual cases are labelled as patient records, while when discussing the allergy database individual cases are labelled as subjects. From the medical
perspective, this is because participants of the allergy database were not admitted as
patients, whereas those of the MIT-BIH database were. This naming convention is also
employed to facilitate simple distinction between the two databases without the need to
reference the name of the database in question, as references to patients refer to persons
from the MIT-BIH database, and references to subjects refer to persons from the allergy
database.
6.2.3
Sensitivity and positive predictivity
The sensitivity of QRS detection measures true positive rate of QRS detection and is
defined by
Se =
TP
⇥ 100%,
TP + FN
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(6.1)
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Chapter 6:
where, TP and FN are as described previously.
The specificity, which was employed in the previous chapter to measure the true negative
classification rate, is an inappropriate measurement for QRS detection, and therefore the
precision or positive predictivity (+P) of the automatically extracted points QRS points is
computed by
+P =
TP
⇥ 100%,
TP + FP
(6.2)
where FP is also as was described previously.
6.2.4
Good detection window
In some cases the location of the R-wave can be ambiguous, in particular if the patient
suffers from cardiovascular disease or arrhythmia. In these cases, automatic QRS detection
algorithms might identify the heart beat, but the point might not be localised on the apex
of the R-wave. Friesen et al. (1990) and Ganong and Ganong (2005), state that the duration
of the QRS complex is approximately 88 ms. Therefore, in this work, if a QRS complex was
found to be within this range of the annotated point, it is flagged as a true positive, as the
QRS complex has been identified. However, if the identified point is found to be outside
of this range of annotated points, the candidate QRS point is flagged as a false positive as
the QRS complex was not identified.
6.2.5
Feature accuracy
It will be seen in later sections that under certain conditions the accuracy of QRS detection
is pessimistically reported. The cause for this is that the QRS points reported from the
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Section 6.2: QRS detection
detection algorithms report times which are outside of the 88 ms ‘good detection window’.
These QRS points are therefore reported as false positives by the algorithm. However,
while the QRS points which are reported are not within the allowed QRS window, beats
are periodically detected, but were localised incorrectly on the S– and T-waves. In order to
assess the effect of this, HRV-based feature metrics are computed and these are compared
to the same HRV features which were obtained from the manual QRS annotations.
It will be shown that when incorrect QRS localisation occurs, QRS beats are consistently
localised at the same part in the wave, i.e. if the QRS is first located on the T-wave, it
will generally be located on the T-wave for that recording. Insight into the effect of poor
localisation can be ascertained by computing the normalised differences between the HRV
features calculated from the manual and automatic sets of QRS points. The difference is
computed with the PRD metric which was employed in Chapter 3, and the equation is
reproduced below, and has been modified for HRV case.
v
u
t
PRD =
1
N
0N
! 1
BBX f m (n) − f a (n) 2 CC
BB
CC ⇥ 100%,
B@
CA
f a (n)
(6.3)
n=1
where f m is feature vector obtained from the manual annotations, f a is the vector obtained
by automated data, and both of these features are of the same length, N .
6.2.6
Box-plots
Box-plots are employed in order to graphically present a number of statistical parameters
of a distribution. Figure 6.1 shows an example box plot (upper) and its relationship to a
normal distribution (lower). Five points are presented with the box plot: the median, the
lower and upper quartiles, and the largest and smallest values with 1.5 quartile ranges of
the distribution.
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Chapter 6:
IQR
Ql − 1.5 ⇥ IQR
Ql
m
Qu
Qu + 1.5 ⇥ IQR
Figure 6.1: Relationship between a box-plot, and quartile ranges with a normal
distribution. The locations marked Ql and Qu are the lower and upper quartiles
respectively, and the median is marked as m.
The lower– and upper-quartile ranges are shown with the horizontal boundaries of the
rectangle in Figure 6.1, and this is termed the inter-quartile range (IQR). The vertical line
found within the rectangle is the median of the distribution (which can also be termed the
50th percentile) and is assigned the label m in this figure. In this plot the median is equal
to the mean of the distribution shown, however, this will not be the case for general data.
To the left and right of the IQR in the box plot, dashed lines radiate until the maximum
and minimum values within 1.5 IQRs of the lower and upper quartiles of the distribution
are obtained. This region is highlighted in the lighter shade in Figure 6.1.
The box plot is a visual aid which allows the variance of the data to be easily visualised.
In Figure 6.1 the box-plot is presented in a horizontal orientation in order to facilitate
comparisons to a normal distribution, but box-plots are typically presented vertically. The
box-plot is employed in this chapter because it facilitates immediate quantification of the
IQR ranges, median, etc, while with a normal distribution a viewer would be required to
estimate these values. This also helps in the evaluation of median-based statistics which
are useful means of assessing effectiveness of algorithms such as QRS detection.
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Section 6.3: Choice of QRS detector
6.3
Choice of QRS detector
Two QRS detectors were investigated for this work.
The first QRS detection algorithm which was chosen employs the Hilbert transform
(Hilbert, 1912). This algorithm was chosen because the Hilbert transform method of QRS
detection has been used by many researchers for over 30 years with very good results
(Bolton and Westphal, 1981b; Nygårds and Sörnmo, 1983; Bolton and Westphal, 1985).
This algorithm has also recently been employed for a robust QRS detector which reduces
the effect of baseline drift, muscular and motion artefacts (Benitez et al., 2001). These are
necessary traits for use during OFC as the subjects are free to move around the bed they
lie on.
The second QRS detector which was selected was presented by Afonso et al. (1999). This
method was selected because it incorporates filter banks (which can be thought of as
being analogous to wavelet transforms) in order to decompose the signal into uniform
bandwidth constituents. Wavelet and filter-bank based signal processing have been shown
to be very useful for DSP applications and therefore this was investigated.
Both of these algorithms have reported over 99% sensitivity and positive predictivity when
employed on the MIT-BIH database, and the next two sections discuss the procedures and
algorithmic properties of these.
Where possible, the difference equations that were used by the QRS detection algorithms
will be provided. However, it is not feasible to provide these in every case as the algorithms
will often require filters that are approximately of the order of the sampling rate. Where
this occurs, the digital design toolbox of Matlab®can be easily used to generate the
difference equations required.
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Chapter 6:
6.4
Hilbert transform based QRS detection
6.4.1
Theory of Hilbert transform
The first QRS detection algorithm investigated involves the use of the Hilbert transform
(Hilbert, 1912). The Hilbert Transform is defined by
b
x(t) = H [x(t)] ,
Z
1 1
1
d⌧,
=
x(⌧)
⇡ −1
t −⌧
(6.4)
(6.5)
where, H is the Hilbert transform operator and b
x is the Hilbert transform of signal, x(t).
The Hilbert transform provides a time varying and a linear function of x(t) and Equation
(6.4) can be rewritten as the convolution between the signal and
b
x = x(t) ⇤
1
.
⇡t
1
⇡t ,
i.e.
(6.6)
Convolution can be performed efficiently in the frequency domain as the convolutions
theorem states that the Fourier transform (FT) of a convolutions is the pointwise product
of the individual Fourier transforms (Katznelson, 2004). Using this, the Hilbert transform
can be rewritten as
F {b
x(t)} =
1 1
F { }F {x(t)}.
⇡
t
150
(6.7)
Section 6.4: Hilbert transform based QRS detection
The Fourier transform of
Niall Twomey
1
is simplified to
t
1
F{ }=
t
Z
1
1 −2⇡f xdx
e
,
−1 x
= −j⇡ sign(f ),
(6.8)
(6.9)
where the sign(f ) = 1 for f > 0, 0 when f = 0, and -1 when f < 0. With this result, Equation
(6.7) is rewritten as
F {b
x(t)} = −j sign(f ) F {x(t)}.
(6.10)
Taking the inverse Fourier transform of Equation (6.10) yields real and complex timedependent variables, illustrated in Figure 6.2. The envelope of this function and the
original signal is used in many applications and is calculated by
q
x2 (t),
B(t) = x2 (t) + b
(6.11)
and the instantaneous phase angle can be computed by
!
b
x(t)
.
✓(t) = arctan
x(t)
151
(6.12)
Niall Twomey
Chapter 6:
jy
x
b(t)
.B(t)
θ(t)
x
x(t)
Figure 6.2: The real and imaginary components resulting from the Hilbert Transform of
the ECG.
6.4.2
Method of QRS detection with Hilbert transform
The Hilbert transform was previously formed in the continuous domain.
However,
discrete-domain equivalents can be obtained by performing the FFT, which was discussed
in Chapter 4. The Hilbert transform is an odd function, which means that whenever there
is a change in inflection of the signal (i.e. when the signal slope changes from positive
to negative, or from negative to positive) the Hilbert transformed signal will cross the
horizontal axis. This property is favourable for QRS detection as the R-wave of the complex
is characterised by this.
The Hilbert transform was first used for QRS detection by Bolton and Westphal (1981a;
1981b; 1984; 1985) and has since been employed by a variety of ECG researchers (Nygårds
and Sörnmo, 1983; Mietus et al., 2000; Benitez et al., 2000, 2001; de Oliveira and Cortez,
2004; Arzeno et al., 2006). However, for computational reasons in some of the early
publications, the Hilbert transform was approximated by a band-limited finite impulse
response (FIR) filter and the envelope was also approximated by
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Section 6.4: Hilbert transform based QRS detection
d
dt
Filtering
ECG
H
Envelope
Peak
Detection
Subset
Windowing
QRS Points
Figure 6.3: The flowchart for QRS detection from the Hilbert Transform.
d = |x(n)| + |b
B(n)
xapprox (n)|.
(6.13)
While the original work required optimisation for computational and power reasons, the
Hilbert transform was not approximated in this work and was computed by the means
outlined in Section 6.4.1. The pipeline for QRS identification is summarised in Figure 6.3.
The raw ECG signal is first filtered by a high-order Kaiser-Bessel band-pass FIR filter with
the pass-band specified by the bandwidth of typical QRS complexes, i.e. 8 — 20 Hz (Dinh
et al., 2001; Kohler et al., 2002; Schlindwein et al., 2006). This frequency band reduces the
effect of muscular artefacts while pre-emphasising the QRS complex. After filtering, the
ECG signal is differentiated by
1
d
x(n) =
[x(n + 1) − x(n − 1)] ,
dn
2∆t
153
(6.14)
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Chapter 6:
10
5
0
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
3.5
4
4.5
5
(a) Raw ECG trace (x(t)).
2
0
−2
0
0.5
1
1.5
2
2.5
3
d x(t)).
(b) Derivative of ECG trace after filtering ( dt
4
2
0
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
5
4.5
5
d x(t))).
(c) Hilbert transform of derivative of the ECG (H( dt
4
3
2
1
0
0.5
1
1.5
2
2.5
3
3.5
4
d x(t)))).
(d) Result of enveloping of the Hilbert transform (B(H( dt
Figure 6.4: The stages employed by the Hilbert Transform QRS detection algorithm (the
ECG data was obtained from Patient 113 in the MIT-BIH Database).
where x(n) is the nth sample, and ∆t is period of the sampling frequency. The reason for
choosing a non-causal filter was not discussed, but it is a popular methodology in the QRS
detection algorithms (Kohler et al., 2002).
Figure 6.4 shows the progression of the ECG trace from recording to enveloping. The
original ECG (Figure 6.4a) is filtered and the derivative of the result is calculated (Figure
154
Section 6.4: Hilbert transform based QRS detection
Niall Twomey
6.4b). The Hilbert transform is then performed on this data (Figure 6.4c) and enveloping
is obtained by Equation (6.11) (Figure 6.4d).
These figures show how the effect of
high-amplitude T-waves are reduced by this algorithm, and how the QRS complex is
accentuated whereas other aspects of the ECG are attenuated.
6.4.3
Beat identification
Higher values of the envelope indicate a higher probability of a true QRS peaks. The P–
and T-waves are typically characterised by a lower frequency bandwidth than the QRS
complex, so even with T-waves of comparable amplitude to the QRS complex (Figure
6.4a) the resulting envelope of the QRS complexes is significantly more pronounced than
resulting from the P– and T-waves (see Figure 6.4d).
Adaptive thresholding is incorporated to identify QRS complexes as stated by Benitez et al.
(2001). Thresholds are dynamically calculated with regard to estimates of signal noise. In
this case, noise is any non-QRS ECG signal shapes and is approximated by computing the
RMS value of the result of the envelope over a 1024-sample time window. If the RMS
value at a particular instance is greater than 18% of the maximum value in the same time
window, the level of noise is considered high, and a threshold of 39% of the maximum
value over the window is selected and points which exceed this are selected as QRS points.
If the noise estimate is less than 18% of the maximum value, the threshold is set low to
1.6 times the RMS noise estimate. If two peaks are detected within 200 ms, one of the
peaks is eliminated upon review of both amplitudes and relative position of both peaks to
the previous QRS peak. By this process, QRS detection is obtained through the use of the
Hilbert transform.
In subsequent sections this algorithm is referred to as the Hilbert transform algorithm. It
should be noted, however, that the association refers to the underlying algorithm and not
to the author of the publication.
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Chapter 6:
H1 (z)
#
H2 (z)
#
..
.
..
.
HM (z)
#
ω1 (n)
ω2 (n)
"
F1 (z)
"
F2 (z)
..
.
..
.
"
FM (z)
y(n)
x(n)
ω4 (n)
Figure 6.5: The generic filter banks flow chart incorporating both bandpass and synthesis
filters. The # and " symbols represent down– and up-sampling respectively.
6.5
Filter-banks based QRS detection
6.5.1
Theory of filter banks
A filter bank is an array of filters whose purpose is to decompose an input signal into M
components (Soman et al., 1993; Saramäki and Bregovic, 2002). In the digital domain,
this is achieved by an array of FIR or infinite impulse response (IIR) filters and Figure
6.5 presents the general architecture which can achieve this. In this Figure, the filters H1
— HM are applied to the input signal x(n). In some filter bank applications the signal is
then down-sampled before analysis is performed. The signal can then be up-sampled, resynthesised by the synthesis filters F1 — FM and combined to generate the representation
of the original signal, y(n). The idealised output of the filter banks method is shown in
H1
F1
H2
F2
H3
F3
HM
FM
dB
......
1π
M
2π
M
3π
M
Mπ
M
ω
Figure 6.6: The idealised filter response of the filter banks, with M equally-wide subbands.
156
Section 6.5: Filter-banks based QRS detection
Niall Twomey
Figure 6.6. In this Figure, M frequency responses are shown, and each band is labelled
with the decomposing filters (Hm ) which were employed to achieve the response.
6.5.2
QRS detection with filter banks
Afonso et al. (1999), presented a QRS detection algorithm involving filter banks in which
the ECG was decomposed into four banks of uniform bandwidths (BWs) of 5.6 Hz (each
decomposition filter contains fs components). Once the signal has been decomposed, the
algorithm down-samples the signal on each subband by a ratio R which is calculated by
!
fs/2
.
R = round
BW
(6.15)
Here, fs is the signal sampling rate and BW is the bandwidth as before, and R = 23 for
fs = 256 Hz.
The purpose of down-sampling the subband signals is to reduce the noise and to increase
the signal to noise ratio (SNR) of the derived signals (Afonso et al., 1999, 1995). With
this algorithm the signals are not reconstructed, so the up-sampling blocks and synthesis
shown in Figure 6.5 are not used.
The remainder of this QRS detection involves multi-stage processing incorporating many
threshold stages.
6.5.2.1
Pre-processing
Four features are extracted from the sub-bands, and these features are employed to aid in
QRS detection. Each feature combines certain sub-bands of interest and these are defined
by Equations (6.16) — (6.19).
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Chapter 6:
P1 =
3
X
|wl (z)|
(6.16)
|wl (z)|
(6.17)
|wl (z)|
(6.18)
wl (z)2
(6.19)
l=1
P2 =
4
X
l=1
P3 =
4
X
l=2
P4 =
3
X
l=1
where wl represents the output of the l th bandpass filter. These features effectively
measure the energy which is found within the bands of interest by linearly combining
the absolute values of the sub-bands. For example, feature P1 combines the output of the
first three bandpass filters, so this is representative of the energy up to 16.8 Hz.
Two-sample moving window integration is performed on each feature.
6.5.2.2
Beat-classification logic
Afonso’s algorithm involves a complicated series of filtering and thresholding routines.
Figure 6.7 attempts to describe the algorithm simply. First, the ECG is filtered into
four component bands and downsampled. Then multiple levels of QRS validation are
performed, and the result of this is a series of validated QRS points. Subsequently, six
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Section 6.5: Filter-banks based QRS detection
ECG
H1
H2
H3
H4
#
#
#
#
Feature extraction and multi-level validation
QRS Points
Figure 6.7: Overall simplified flowchart of Afonso’s QRS detection method.
levels of QRS validation are executed over all candidate QRS points. These are described
here.
6.5.2.2.1
Level 1 The first validation Level involves identifying all the positive peaks
of the moving window integrator of feature P1 , Equation (6.16). This stage is designed to
identify as many QRS candidate events as possible. While this process will produce a large
number of false-positive points, subsequent validation levels will eliminate inappropriate
candidates. Figure 6.8a shows the raw ECG trace which was recorded, and Figure 6.8b
shows the events which are selected by this feature. It can be seen that every positive
peak which occurred in the time-series is marked as a QRS point. In four instances at t
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Chapter 6:
= {141.75, 144.5, 146, 146.75} seconds it can be seen that T-waves contributed to positive
peaks and were identified by this level of QRS detection.
6.5.2.2.2
Level 2 The second Level involves two single-channel threshold stages in
which candidate QRS points from level 1 are employed as estimates of ‘signal’ and ‘noise’
of the ECG signal. Here, ‘signal’ refers to the times that Level 1 have identified as QRS
candidate points, and ‘noise’ refers to points which were flagged as QRS candidates.
The mean value of feature P2 at the candidate QRS times from Level 1 is computed. The
initial ‘signal’ level is estimated as 10% above this mean value, while the noise level is
initially set as 10% below this. The values of the candidate QRS points are compared
against these levels giving two sets of QRS events. A parameter is introduced to identify
a measure of confidence of whether these are truly signal or noise, and is termed decision
strength. This is defined by
DS(i) =
feature(i) − N L
,
SL − N L
(6.20)
where DS(i) is the decision strength measure for the i th QRS candidate, NL is the Noise
Level estimate, SL is the Signal Level estimate, and feature(i) is the i th candidate of the
feature being considered. This equation is parametrised because the decision strength is
used in later levels for validation. The lower the decision strength parameter, the more
likely that the candidate point was noise, but the higher the value, the more likely it is a
QRS point. The value of the parameter is force-bounded between 0 and 1.
The thresholding which is introduced at this Level is performed on the decision strength
parameter. A low and high threshold are utilised, and the low threshold is set at 0.08, while
the high threshold is set at 0.7. The low threshold is set so as to identify a large number of
candidate points, while the high threshold is set so as to produce a high degree of certainty
with a set of candidate points. The signal and noise levels are updated throughout the QRS
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Section 6.5: Filter-banks based QRS detection
142
143
144
145
146
147
146
147
(a) Raw ECG.
142
143
144
145
(b) Moving window integration of feature P1 (Level 1).
142
143
144
145
146
147
(c) Moving window integration of feature P2 (Level 2).
142
143
144
145
146
147
(d) Moving window integration of feature P3 (Level 4).
142
143
144
145
146
147
Time (s)
(e) ECG with automatically extracted QRS points (Level 6).
Figure 6.8: The effect of the various QRS validation levels from Afonso’s QRS detection
algorithm. In these charts, the ◦ symbols represent the candidate QRS points. Charts b —
d have been down-sampled.
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Chapter 6:
detection stage based on whether the classification of the current event was deemed to be
‘signal’ or ‘noise’ with regard to these thresholds and how they are employed later. The
events classified in this stage with sample ECG is shown in Figure 6.8c. It can be seen here
that all of the false positive QRS points have been removed, but the detection algorithm
has also removed a true QRS beat which can be found at 145.75 seconds.
6.5.2.2.3
Level 3
Level three of the beat-classification logic fuses the two candidate QRS
points from Level 2 together, i.e. the times at which the decision strengths surpassed the
upper and lower thresholds. The results of stage two are termed ’channel 1’ (which gives
the QRS candidate times with reference to the low threshold) and ’channel 2’ (QRS events
with the high threshold).
Three possible outcomes can occur:
1. If channel 2 detected an event, a QRS event is always deemed to have occurred as the
threshold was set very high. In this situation channel 1 will also have been triggered.
2. If neither channels detect a QRS event, the QRS event from the previous levels is
eliminated from consideration.
3. If channel 1 (low threshold) detects an event, but channel 2 does not, the decision
strengths of each channel are then computed. Two further parameters are computed
from these decision strengths and are defined by
∆1 =
DS1 − th1
,
1 − th1
(6.21)
∆2 =
th2 − DS2
,
th2
(6.22)
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where th1 and th2 are the thresholds which were used when computing the candidate
events and ∆1 and ∆2 relate to channels 1 and 2 respectively. If ∆1 is greater than ∆2 ,
the event is deemed to have been a QRS event. Otherwise is deemed to have been
noise.
6.5.2.2.4
Level 4
The fourth Level uses another one-channel detection block, this time
using feature P3. A new threshold of 0.3 (of decision strength) is used here. If Level 3
removed a candidate QRS event, and the decision strength from P3 exceeds the threshold
of 0.3, the beat is re-introduced as a candidate. This stage reduces the number of false
negative QRS events in this algorithm. This Level only operates on events which were
removed Level 3. The events classified in this stage are shown in Figure 6.8d. It can be
seen that the point which was removed in Figure 6.8c at 145.75 seconds is re-introduced
by Level 4.
6.5.2.2.5
Levels 5 and 6 The fifth stage reviews the points which are still under
consideration QRS and performs decision logic based on timing between the QRS events.
If the time between two consecutive QRS events is greater than 1.5 times the mean of
the previous 100 QRS events, a lower decision strength threshold of 0.2 is employed to
accept events which were removed by the validation stages. Furthermore, if the difference
between two consecutive QRS events is less than 0.24 seconds, the point which resulted in
the smaller peak in the original ECG signal is removed.
6.5.2.3
Overall
The set of candidate points which are obtained after Level 6 are considered as true QRS
points by the algorithm and no further post-processing is performed. The means by which
the points relate to the original ECG trace is shown in Figure 6.8e where the QRS points are
shown with the symbol ◦. In subsequent sections, this algorithm is referred to as Afonso’s
algorithm.
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Chapter 6:
Table 6.1: Differences in reported and calculated sensitivity and positive predictivity.
Afonso
Reported This work
Sensitivity
Positive Predictivity
6.6
99.59%
99.56%
99.15%
98.16%
Hilbert Transform
Reported This work
99.87%
99.94%
99.03%
97.43%
Results obtained on MIT-BIH database
Here, automatic QRS detection is assessed on the MIT-BIH database.
The Hilbert
transform and Afonso’s QRS detection algorithms are employed for this detection process.
6.6.1
Sensitivity and positive predictivity
Table 6.1 tabulates the sensitivity and positive predictivity of QRS detection results which
were obtained with the MIT-BIH database. Sensitivity and positive predictivity of 99.15%
and 99.16% were obtained with Afonso’s algorithm, and the Hilbert transform algorithm
yielded sensitivity of 99.03% and positive predictivity of 97.43%. The distribution of these
results are presented in Figure 6.10, which shows that the majority of the QRS points
which were detected were within the window of acceptance. However, these results are
slightly different from the results which were obtained in the literature, and the cause for
this was due to incorrect localisation of the QRS points on the ECG signal trace.
Patient 8, whose ECG is shown in Figure 6.9, has a non-standard QRS complex in
comparison to the example shown in Chapter 1, where it can be seen that the S-wave drops
significantly below the baseline level of the ECG trace. Figure 6.9 illustrates how the bad
QRS localisation can occur. In this figure, the solid trace is the raw ECG, the ⇥ markers
represent the expert QRS annotations, the ◦ markers identify the QRS points from Afonso’s
algorithm, and the ⇤ markers represent the times which the Hilbert transform identified
as the QRS complexes. It can be seen that Afonso’s algorithm misclassified the T-wave as
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Amplitude (µV)
200
0
−200
−400
71.6 71.8 72 72.2 72.4 72.6 72.8 73 73.2 73.4 73.6 73.8 74 74.2 74.4
Time (s)
Figure 6.9: Incorrect QRS complex localisation (Patient 8 of MIT-BIH arrhythmia
database). Manual QRS annotations are marked with ⇥ and automatic detections are
marked with ⇤ (Hilbert transform algorithm) and ◦ (Afonso’s algorithm).
a R-wave while the Hilbert transform algorithm misinterprets the S-wave as the R-wave.
The positions of these mis-localisations are logical given the nature of the two algorithms.
All of these points are flagged as false negative and false positive points as they were
outside of the window of acceptance, and therefore the sensitivity and positive predictivity
results that were obtained are reduced. For some patients, the incorrect localisation
occurred intermittently during their recording. As the majority of the QRS complexes
extracted on these patients was consistent with the annotations, it was not deemed
appropriate to eliminate these for performance evaluation. Indeed, using median-based
statistics, the sensitivity and positive predictivity of detection can all be seen to be above
99.5% from Figure 6.10.
It is believed that the reason for the superior results obtained in the literature is due to a
wider window of acceptance. However, it will be shown later that widening the window
is not appropriate for this this work.
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Chapter 6:
100
Positive Predictivity
Sensitivty
100
99.5
99
Afonso
Hilbert
98
96
Afonso
(a) Sensitivity.
Hilbert
(b) Positive Predictivity.
Figure 6.10: Sensitivity and positive predictivity box-plots of QRS detection on the
MIT-BIH arrhythmia database.
6.6.2
Percentage RMS difference
The mean and standard deviation of the heart rate for the patients in the MIT-BIH database
were computed from the expert QRS annotations and those extracted automatically by the
QRS detectors based on the Hilbert transform and based on Afonso’s algorithm. The PRD
was computed according to Equation (6.3), and Figure 6.11 shows the box-plot of the PRD
of these features over the MIT-BIH database. The closer the PRD is to 0%, the more similar
the automatically generated features are to the manual features. Indeed, it can be seen
that the median of the PRD in all cases is very low, indicating a high degree of agreement
between the manual and automatic QRS points in the majority of cases.
Figure 6.11a shows the PRD values which were calculated from the mean heart rate. This
figure shows that even though incorrect localisation of the QRS complex occurred, the
features which were obtained from these points do not distort the features, and that the
values which were extracted are representative of the heart rates obtained with the manual
annotations. This is further supported by the low variance which was computed on the
same subjects.
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Section 6.7: Requirement for artefact detection
0.6
PRD %
PRD %
3
2
1
0
0.4
0.2
0
Afonso
Hilbert
Afonso
(a) Mean HR PRD.
Hilbert
(b) Standard Deviation PRD.
Figure 6.11: PRD box-plots of the mean (µ) and standard deviation (σ) of the heart rate
over all subjects in the MIT-BIH arrhythmia database.
6.6.3
Conclusions on QRS detection on MIT-BIH database
This section has shown that the two QRS detection algorithms which were developed
achieved competitive results when considered against the results which were published.
While these results have already been obtained in the literature, it is important to
verify that the results were reproducible, in particular when the algorithms which were
assessed are intended to be employed for diagnostic applications. The two QRS detection
algorithms which were employed were found to perform slightly better in the literature.
6.7
Requirement for artefact detection
6.7.1
Introduction
In the MIT-BIH database, the patients whose data were recorded were adults that were
undergoing important medical examinations. Therefore, these patients were not inclined
to move, and while there were a tendencies for some artefacts, their contribution were low
in general. However, the subjects who participated in the OFCs were children with a mean
age of approximately 5 years, who wanted to play games and move during their tests. As
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Chapter 6:
a result, artefacts were introduced in the ECG that was recorded, and artefact detection
is required in order to provide confidence that the QRS points which were extracted were
accurate.
6.7.2
Artefact detection algorithm
The bandwidth of the QRS complex ranges from between approximately 10 and 25 Hz
(Dinh et al., 2001; Kohler et al., 2002; Schlindwein et al., 2006). For this reason, almost
all QRS detectors will filter the ECG with a band-pass (BP) filter so as to attenuate nonQRS complex shapes, such as artefact, P– and T-waves. ECG artefacts are signals which
interfere with the signal trace and they arise from physiological (motion, muscular spasm,
and touching electrodes), electrical (mains interference) and resistive (poor connection
with the skin) conditions. Artefacts can lead to false-detections and non-detections of the
QRS complex, in both cases the accuracy of the features which are extracted from the ECG
signal are affected.
Even with de-noising filters which reduce the extent of artefacts on the ECG, noise can
still affect the fidelity of the QRS complex. Therefore, when considering unconstrained,
fully automated biomedical classification platforms, artefact-awareness is an important
requirement. An algorithm incorporating energy detection was adopted to detect the
signal strength in the ‘noise bands’ of the ECG. ‘Noise bands’ are the frequency bands
outside of the bandwidth of interest of the QRS complex. The lower noise band was set to
0 — 10 Hz and the high noise band includes all frequencies greater than 25 Hz.
Energy detection estimation is performed on these frequency bands in order to assess the
extent of noise in the ECG. Energy detection is a popular means of signal detection and
is used by communications researchers (Horgan and Murphy, 2010). Here, two 50th order
FIR filters were designed and are denoted as Hh and Hl . Hh is a high-pass filter with a
corner frequency of 25 Hz which was employed in order to detect high-frequency artefacts
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Section 6.7: Requirement for artefact detection
resulting from muscular, motion noise, etc. A low-pass filter, Hl , was also designed with a
cutoff frequency of 10 Hz. The purpose of this filter was to detect low-frequency artefacts.
Energy estimates at a time index k are computed from the output of Hh and Hl in a
time window of length N , i.e. between k − N and k. In this work, the window length
of 0.25 seconds (i.e. 64 points when sampled at 256 Hz) was chosen in order to detect
short-duration artefacts. The energy E(n) of a signal x can be computed by a ‘square and
accumulate’ algorithm in Equation (6.23).
E(k) =
k
X
x2 (i)
(6.23)
i=k−N
6.7.3
Demonstration of artefact detection
Figures 6.12a and 6.12b show the scaled output of the energy detection algorithm for
high frequency artefact detection. At t = 45.6 minutes in Figure 6.12a high-frequency
muscular artefacts can be observed. The QRS complexes for 6 seconds after this time are
obscured by high-frequency artefacts. The output of the high-frequency artefact detection
filter is normalised by the standard deviation measurement from all other Subjects in the
databases where this algorithm was used by a LOO procedure similar to the one employed
for classification in the previous chapter. This output, shown in Figure 6.12b can be seen
to rise at the same time as the high-frequency noise presents in the ECG. The artefact
output was smoothed with a 2 second moving average filter. The filter was centred so as
to remove any phase delay which might occur due to the moving average process.
Dynamic thresholding is applied to the output of the energy detection filters and when the
result surpasses the threshold, that epoch is considered ‘noisy’ and will not be considered
for feature extraction. The threshold for artefact detection is set dynamically for each
subject. A moving window of 1 minute of non-artefact energy estimates is selected and the
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ECG (µHz)
Chapter 6:
45.45
45.5
45.55
45.6
45.65
Time
45.7
45.75
45.8
45.85
Normalised energy
(a) ECG with high-frequency artefacts present.
45.5
45.55
45.6
45.65
Time
45.7
45.75
45.8
ECG (µHz)
(b) Output of high-frequency artefact detection filter.
8
8.05
8.1
8.15
8.2
8.25
8.3 8.35
Time
8.45
8.5
8.55
8.6
8.45
8.5
8.55
8.6
8.4
Normalised energy
(c) ECG with low-frequency artefacts present.
8
8.05
8.1
8.15
8.2
8.25
8.3 8.35
Time
8.4
(d) Output of low-frequency artefact detection filter.
Figure 6.12: Normalised output of high-frequency (a,b) and low-frequency (c,d) energy
estimators for artefact detection (data from Subject 8 of allergy database).
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Section 6.8: Results obtained on allergy database
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mean, µ, and standard deviation, σ, of this window is calculated. If the signal surpassed
µ + 3σ, the ECG at this segment was labelled ‘noisy.’
Figures 6.12c and 6.12d are similar to Figures 6.12a and 6.12b, but they presents the
output of the low-pass artefact detection filter. At t = 8.28 minutes a baseline deviation
can be observed in the ECG trace. This artefact is likely to be as a result of the subject
in question touching the ECG electrode or electrode pop of the sensor. The internal
filtering in the hardware ECG daughterboard of the SHIMMER device will compensate for
the electrode pop artefact gradually which results in the slower wave which can be seen.
Once again the artefact affects the amplitude of the QRS complex to the point where the
accuracy of reliable QRS detection is compromised. The normalised output of the energy
detection is shown in Figure 6.12b and a change in the artefact signal can be observed at
the same time as a change in the ECG trace.
The output of the low and high frequency artefact detection filters are fused together by
the logical OR-ing operation.
6.8
Results obtained on allergy database
Here, the QRS detection results which were obtained with the automatic QRS detectors on
the allergy database are presented.
6.8.1
Artefact detection
The number of artefact events which were was flagged during the OFC recordings of the
subjects in the allergy database are presented in the bar chart in Figure 6.13. The subjects
indices of the allergy database are presented on the horizontal axis while the number of
instances of artefact is presented on the vertical axis.
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Chapter 6:
# artefact events detected
30
20
10
0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Subject ID
Figure 6.13: The breakdown of the number of artefact events which were detected by the
artefact detection algorithm for each subject of the allergy database.
The subject which presented with the greatest number of artefacts was Subject 3 where
28 individual events were identified. The events which were detected were similar to the
high and low frequency artefacts which were shown in Figures 6.12a — 6.12d. In earlier
chapters, the most stringent requirement for allergy detection was stated to be obtaining
100% specificity (i.e. no false positive allergy classifications) as only with these conditions
will the OFC be improved. It will be shown in the next chapter that the presence of
artefacts in the ECG will have the capacity to mislabel segments of data as allergy.
Approximately 50% of the artefacts which were detected were recorded during the
checkup periods the oral food challenge. When the allergist checks the heart rate, blood
pressure, blood oxygen saturation levels and temperature of the subjects during the
checkups, the subject is required to move so that the equipment is correctly applied. As
a result, high and low frequency artefacts were prevalent during checkups. In allergy
classification, the time periods which satisfy the classification criteria and which occur
during checkup periods are not classified as allergy. This is because checkups interfere
with the heart rate of the subjects, and causes them to move which introduces artefacts to
the ECG. It is also the case that artefact detection during the checkup periods is not strictly
necessary because if this were used in a clinical setting, the allergists would be aware of
the introduction of artefacts, and would likely disregard this data.
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Section 6.8: Results obtained on allergy database
Table 6.2: Sensitivity and positive predictivity of QRS detectors on allergy database.
Sensitivity
Positive Predictivity
Afonso
Hilbert Transform
92.89%
93.15%
92.17%
93.00%
However, in over 50% of cases, artefacts were not due to the influence of the allergist on
the subject. Every event which was flagged as an artefact was visually inspected, and it
was found that the majority of the artefacts which remained contaminated the ECG points
to the degree where QRS detection was unreliable. When QRS annotations were labelled
manually for previous chapters, it was possible to identify the QRS points during time
periods such as this with careful visual inspection, and it was at times like this when QRS
extraction failed.
With approximately 5% of the artefact events, the accuracy of automatic QRS extraction
was not affected. While it is possible that short-duration signatures of allergy might not
be considered for allergy classification, it was deemed that less than 10% false positive
artefact events was an acceptable trade in the attempt to preserve 100% specificity for
fully automated allergy classification.
6.8.2
Sensitivity and positive predictivity
Table 6.2 tabulates the mean of the sensitivity and positive predictivity values obtained
with QRS detection on the allergy database by the QRS detectors against the manual
annotations. The sensitivity and positive predictivity values calculated are of the same
overall percentage (⇡ 92 — 93%). The majority of these are above 99% which can be seen
from the median lines in the box plots of Figure 6.14. In some cases sensitivity and positive
predictivity values of approximately 70% were obtained.
Upon investigation of the subjects from whom poor QRS detection was obtained, it was
discovered that the quality of the recorded ECG was the primary source of the reduction in
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Positive predictivity (%)
Chapter 6:
Sensitivity (%)
100
98
96
94
Afonso
Hilbert
100
(a) Sensitivity.
99
98
97
Afonso
Hilbert
(b) Positive Predictivity.
Figure 6.14: Sensitivity and positive predictivity box-plots of QRS detection on the allergy
database.
accuracy metrics. It was difficult to determine the exact cause for the poor quality of ECG
recorded, but it is believed that it is predominantly due to high-resistance connections
between the ECG electrodes and the subject’s skin. Figure 6.15 shows an example of the
poor quality of ECG which was obtained over a 6 second period after applying filtering
(Clifford et al., 2006). Contrasting the ECG in this recording to the ECG found in Figure
6.9, for example, it can be seen that even after filtering, the baseline of the recording
in Figure 6.15 contains a significant amount of noise which is of the same bandwidth
as the QRS complexes. The amplitude of the QRS complex is also much diminished in
comparison to those shown previously in Figure 6.9.
A second cause for the poor performance of QRS detection was the phenomenon of
incorrect QRS which was previously discussed for the MIT-BIH database in Section 6.6
and is illustrated in Figure 6.9. This contributed to the lower sensitivity and positive
predictivity values which were obtained on the allergy database. Interestingly, while the
cause of this in the MIT-BIH database was due to the rich set of arrhythmias which were
recorded, the cause for the incorrect localisation on the allergy database was due to poor
integrity of ECG signal that was recorded.
The overall sensitivity and positive predictivity could be improved by widening the
window of acceptance on the allergy database. As a consequence of doing this, however,
many of the QRS complexes would be identified as true positives, even though the true
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Voltage (µV )
150
100
50
0
−50
83 83.01 83.02 83.03 83.04 83.05 83.06 83.07 83.08 83.09 83.1
Time (mins)
Figure 6.15: Example of poor quality of the ECG signal after the application of denoising
filters which contributed to poor sensitivity and positive predictivity values for Subject 3.
source of the point was from artefacts. This would optimistically report the sensitivity and
positive predictivity. Therefore, the criteria for true positive and false positive labelling
was not relaxed in this work.
6.8.3
Percentage RMS difference
The PRD of the mean and standard deviation features were extracted to assess the effect
of obtaining approximately 93% sensitivity and positive predictivity on the HRV metrics.
Table 6.3 tabulates the results which were obtained. Excellent mean PRD values were
obtained with both Afonso and the Hilbert transform algorithms (< 1.3% when averaged
over all detection algorithms and features). The distribution of the PRD values obtained
is outlined in Figures 6.16a and 6.16b. Interestingly, the PRD results obtained here are
slightly better than those which were obtained on the MIT-BIH arrhythmia database even
though poorer overall sensitivity and positive predictivity results were obtained with
the allergy database. The PRD values obtained by the mean heart rate of the Hilbert
transform present a significantly lower variance between subjects. Once more, medianbased statistics of these indicate that for the majority of the subjects, excellent data are
recorded, and as before, it is with some subjects that QRS detection is not as accurate.
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Chapter 6:
Table 6.3: Distribution of mean and standard deviation of the PRD values calculated from
automatically extracted QRS points.
Mean Heart Rate
Standard Deviation
6.9
Afonso
Hilbert Transform
0.68%
1.26%
0.56%
1.1%
Overall discussion
The accuracy of the two QRS detection algorithms was assessed on two databases. Initially,
QRS extraction performance assessment was performed on the MIT-BIH arrhythmia
database and sensitivity and specificity measurements were computed. It was observed
that correct localisation of the QRS complex on the R-wave was not always obtained, and
that in certain situations QRS detection algorithms can localise the QRS complex on the S–
and T-waves. This resulted in the discrepancies between the results which were reported
and those which were obtained here. The slight discrepancies were due to a number of
patients’ ECG traces toggling between regular ECG and arrhythmia. Further accuracy
assessment was performed by extracting HRV features from the QRS points reported by
the QRS detection algorithms. The differences between the features were calculated via
the PRD function, and average discrepancies of ⇡ 1% were obtained, but median-based
analysis yielded excellent results.
0.6
PRD (%)
PRD (%)
3
2
1
0
0.4
0.2
0
Afonso
Hilbert
Afonso
(a) PRD of mean heart rate.
Hilbert
(b) PRD of standard deviation.
Figure 6.16: Boxplots of the PRD of the mean and standard deviation of the heart rate
between the manual and automatic QRS points extracted.
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Section 6.9: Overall discussion
In contrast to the patients of the MIT-BIH arrhythmia database, the subjects of the
allergy database were generally of an age where they were unwilling to remain still
during their tests. For this reason, artefacts presented frequently in the ECG recordings
in allergy database. Artefact detection was performed on the ECGs recorded to instill
confidence that the features extracted from the ECG were contributions of the heart beats
rather than from artefact noise. The sensitivity, positive predictivity, and the mean and
standard deviation of the heart rates resulting from the automatic QRS detection were then
extracted from the regions which were not flagged as artefact. The sensitivity and positive
predictivity results were ⇡ 93%, but once more, median-based results of over 99% were
obtained for both QRS detectors. This indicates that in the majority of cases, excellent
QRS detection is obtained. The extracted features were also very close to the true feature
values.
The algorithms that were employed here were adapted directly from the literature which
investigated the accuracy of QRS detection for adults. As the subjects under investigation
for allergy classification are children under the age of 10 years, it could be argued that in
varying certain parameters (e.g. threshold values) that better accuracy would be obtained.
However, it was not possible to perform this tuning as no independent database consisting
of children’s ECG was available to allow objective parameter tuning, and therefore the
‘stock’ values that were provided in the original publications were employed.
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CHAPTER 7
Fully automated allergy detection
7.1
I
Introduction
N Chapter 5, classification of allergy was performed through statistical modelling of
background, non-allergic heart rate variability features. Through these means, 93%
sensitivity and 100% specificity of classification were achieved. However, these results
were obtained on features which were extracted from QRS points that were manually
annotated. This process provided an indication of the validity of heart rate variability
based allergy classification, but due to the manual intervention required to acquire these
annotations, the process would not be suitable for a real-time classification environment.
Therefore, Chapter 6 discussed the accuracy of QRS detection achieved by fully automated
QRS detection algorithms. Two automatic QRS detection algorithms were investigated
and the accuracy of QRS detection was benchmarked against a number of ECG databases.
Based on these results, both of the QRS detection algorithms were deemed suitable for
investigation here.
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Section 7.2: Methods
ECG
Models from
Chapter 5
Unmatched
decision
making
QRS
Testing
data
Unmatched
classification
result
Training
Data
Automatic
model
generation
and selection
Matched
decision
making
Matched
classification
result
Figure 7.1: The flow of how the matched (right) and unmatched (left) classification results
are obtained.
In this chapter, the heart rate variability features extracted with the automatic QRS points
are employed in conjunction with the classification framework in order to assess fully
automated allergy classification.
7.2
Methods
Two means of automatic classification of allergy are assessed in this chapter. These are
assessed because for applications of this nature where two representations of the same
data are available (i.e. manual and automatic HRV features), the accuracy of machine
learning algorithms can be assessed in a number of directions. These avenues are termed
matched and unmatched classification and the processes of these are shown in Figure 7.1.
In this figure, the right hand side shows the process by which the matched classification
results are obtained, while the left hand side presents the unmatched process.
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Chapter 7:
The first method assesses the results obtained by employing the classification models
which were obtained in Chapter 5 (‘manual models’) with the normalised HRV features
which were extracted from the QRS points which were obtained from the automatic QRS
detectors discussed in Chapter 6. These sets of features are termed ‘automatic data’ to
simplify discussions of the results in later sections, and automatic data is also subdivided
into ‘Afonso’ and ‘Hilbert’ data, and relate to the feature data of each QRS detection
algorithm. As these results employ manual models and automatic data, these results
are termed ‘crossover’ and ‘unmatched’ results henceforth. Because the features which
were obtained in Chapter 6 presented low PRD against the features which were extracted
from manual QRS annotations, crossover classification sheds light on the sensitivity of the
classification framework to input data which is of the same nature, but which is different
in origin.
The second method investigated employed the model generation framework from Chapter
5, but trained new classification models and newly selected decision making parameters
based only on the automatic data. This process was investigated because the classification
framework has previously been stated as being data-driven, and unmatched classification
might not be appropriate for fully automated allergy classification. Models generated in
this process are termed ‘automatic’ models. Automatic models are further subdivided into
‘Afonso’ and ‘Hilbert’ models in reference to the QRS detection algorithms which were
employed in their generation (see Chapter 6). The procedures employed in this chapter
utilised the epoch-length fusion which was introduced in Chapter 5.
Matched and unmatched methods are investigated because classification of this variety has
not been performed on allergy data previously. Therefore, it is uncertain which procedure
will perform best for automatic classification, and this chapter investigates this. Typically,
it would be expected to obtain better results with unmatched results as the models which
would be employed in this case relate to manual models which were generated without
error.
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Section 7.3: Unmatched classification results
Table 7.1: Sensitivity and specificity of classification results obtained with the manual
classification models on the automatically extracted HRV features (i.e. crossover
classification results).
7.3
QRS detector
Sensitivity
Specificity
Afonso
Hilbert
60%
66.66%
88.88%
88.88%
Manual
93.33%
100%
Unmatched classification results
This section presents the crossover classification results, and then discusses the applicability of this routine for autonomous classification of allergy.
7.3.1
Results of unmatched classification
Table 7.1 presents the sensitivity and specificity results which were computed by the
crossover classification process. It can be seen from this table that in both cases specificity
of 88.88% was calculated, indicating that false positive classifications were made. The
sensitivity which was obtained is also quite poor in comparison to Chapter 5, with Afonso
data achieving six false negatives (60% sensitivity) and Hilbert transform obtaining five
false negatives (66.66% sensitivity).
The time gain metrics which were obtained with crossover classification will not be
discussed here. This is because in previous chapters it was repeatedly stated that the most
important aspect of allergy classification is obtaining 100% specificity, and this metric was
not obtained with the crossover classification results. To employ an analogy, obtaining
suboptimal specificity and discussing time gain metrics is equivalent to discussing a novel
feature of a vehicle, when its braking mechanism does not function correctly. The priority
in these cases should be safety, and this must be addressed before additional features
warrant analysis. Without obtaining perfect specificity, machine-based classification of
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allergy should not be trusted because the impact on the quality of life of a subject would
elicit in unacceptable consequences.
7.3.2
Discussion on unmatched classification results
Subtle dynamics control the applicability of models for classification of allergy. This is
evidenced by the fact that both poor sensitivity and poor specificity were obtained with
crossover classification. From a decision making point of view, poor sensitivity implies
that the duration and threshold parameters that were employed were too large as the
allergy classification criteria were not satisfied in a number of cases. However, as this
process also obtained suboptimal specificity, these decision making parameters were also,
in fact, too low in other cases. Therefore, it can be stated that classification of allergy is
a very strong function of the modelling parameter selection routine, and the novelty of
certain segments can be under-represented with poor choices in these parameters. Indeed,
this is perhaps not surprising. In Chapter 5, for example, it was shown that even with
matched classification that the extent of the ‘novelty’ of certain regions of the HRV can be
under-represented with some choices of parameters.
This suggests that the model and parameter selection is data driven, and that optimal
automated allergy classification is not obtained by crossover classification. Every aspect
of the classification framework is subject to the data which was employed to generate
the models, and even with slight variations with these data, different results, model and
decision making parameters are obtained. This, then, explains that while 100% specificity
and good sensitivity was preserved between training and testing in Chapter 5, it was not
preserved between unmatched results.
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Section 7.4: Matched classification results
7.4
Matched classification results
The results achieved in this section employed the boosting ensemble-based classification
framework and learnt new classification models from the HRV data obtained from the
QRS detection algorithms. The sets of PCA, GMM, duration and multiplicative parameters
utilised for the generation of these results are all new to these methods and are entirely
independent of the manual models, see Figure 7.1.
7.4.1
Sensitivity and specificity
Table 7.2 presents the overall results which were obtained by the two QRS algorithms
which were investigated.
In both cases, specificities of 100% were obtained by the
automatic classification algorithms. This means that no false positive classifications were
encountered. While 100% specificity has already been obtained in Chapter 5, obtaining
100% specificity with fully automated models is a very significant result as it means that
a subject, having been classified allergic, can immediately be diagnosed as allergic at
this time without the risk of false positive classifications. The same confidence which
is attributed to the diagnosis of allergy can, therefore, be attributed to classification of
allergy as classification of allergy is equivalent to a diagnosis of allergy.
Automatic classification obtained 80% sensitivity with both QRS detection algorithms.
The subjects who were misclassified are Subjects 1, 2 and 3. Subject 1 was not classified
as allergic by either the manual or automatic classification routines. The reason for this
Table 7.2: Sensitivity and specificity of classification results obtained by Afonso’s and the
Hilbert transform QRS detectors.
QRS detector
Sensitivity
Specificity
Afonso
Hilbert
80%
80%
100%
100%
Manual
93.33%
100%
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Chapter 7:
Likelihood
10−1
10−2
0
2
4
6
8
10
12
14
10
12
14
10
12
14
(a) Manual model.
Likelihood
10−1
10−2
0
2
4
6
8
(b) Afonso model.
Likelihood
10−1
10−2
0
2
4
6
8
(c) Hilbert model.
Figure 7.2: Likelihood plots of Subject 1 for manual and automatic models at epoch
lengths of 60 seconds. Subplots (a) — (c) show the likelihoods which were obtained with
manual, Afonso and Hilbert models respectively. In all cases the threshold for allergy
classification is off the scope of the figures.
is because the Subject reacted to the food three minutes after consuming it. Within this
short time frame, the features which were extracted did not change sufficiently for the
allergy classification criteria to be satisfied. Figure 7.2 shows the likelihood traces which
were obtained for Subject 1 with manual (Figure 7.2a), Afonso (Figure 7.2b) and Hilbert
transform (Figure 7.2c) models at epoch lengths of 60 seconds. It should be noted that
while the length of the recording for Subject 1 is approximately 14 minutes, the first dose
of the allergen was administered 11 minutes after the challenge began.
It can be seen in this Figure that the set of three likelihood traces tend to deviate away from
the background likelihood levels before the challenge was terminated. This is due to the
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Section 7.4: Matched classification results
allergic reaction which the subject would experience that induced the subject to vomiting
at approximately 14 minutes. While there is a visible departure from the background
likelihood range with manual, Afonso and Hilbert likelihoods, the extent of this departure
was insufficient for correct classification of this subject and in all cases the threshold is
outside the boundary of the vertical axes. This is because some subjects (Subject 16, for
example) presented with non-background-like HRV features throughout the OFC and in
order to obtain 100% specificity in training, larger thresholds are selected. The slight
differences in the likelihoods also support the data-driven nature of the classification
process, and models selected for each process will display the same trends (i.e. departure
from background as allergic reaction manifests) but the extent of the trends will vary
depending on the models selected.
Subjects 2 and 3 were correctly classified by the manual models, but were not classified
correctly with automatic models. In Chapter 5 the likelihoods which were computed for
Subject 2 were shown (Figure 5.9, page 126) and it was seen that at approximately 65
minutes a period of the likelihood was classified as allergic. It was also shown that these
departures can disappear with poor choices of classifier model with matched classification
(see Chapter 5). The extent of the departures obtained with fully automated methods was
not sufficient for machine-based classification of allergy for these subjects.
7.4.2
Artefact detection
The requirement for artefact detection in the ECG was discussed in Chapter 6. It will
be shown in this section that artefact detection did not reduce the sensitivity, but rather
allowed Subjects 7 and 10 to be correctly classified as allergic when, without artefact
detection, they would have been incorrectly classified as non-allergic.
Figure 7.3a shows an example of of Subject 7’s likelihood trace with epoch lengths
of 60 seconds. The threshold is also presented in this figure when artefact detection
was not employed. The shaded regions indicate the background and checkup times as
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Chapter 7:
10−1
10−3
0
10
20
30
40
50
60
(a) Allergy detection of Subject 7 without the application of artefact detection.
Likelihood
100
10−1
10−2
10−3
0
10
20
30
40
50
60
(b) Allergy detection of Subject 7 with the application of artefact detection.
Figure 7.3: Likelihood plots of Subject 7. Subplot (a) shows the threshold which was
computed without the aid of artefact detection and how allergy classification does not
classify allergy. Subplot (b) shows the threshold which was computed when artefact
detection was incorporated and how allergy classification is successful with artefact-aware
classification.
before. In this Figure, the threshold can be seen to have been selected at approximately
7 ⇥ 10−4 , and this threshold was not surpassed once during the OFC even though two
substantiated deviations from the background levels can be seen in the likelihood traces
at approximately 30 and 50 minutes.
The reason that a large threshold was selected is because the mean and standard deviation
of the background likelihood was contaminated with artefact, which can be seen near
t = 0. This increased the mean and standard deviation of the background data, and in the
calculation of the threshold, this increase was scaled by the multiplicative parameter to
the point where the allergic criteria were unable to be satisfied.
Figure 7.3b shows the same likelihood trace, but when the region at the beginning of
the challenge was dismissed by the artefact detection algorithm. As a result, the mean
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Section 7.4: Matched classification results
and standard deviation of the background was calculated with only ‘normal’ data and the
threshold which was calculated allowed allergy to be classified for this subject with a time
gain of approximately 30 minutes.
It should be stated that it is uncertain what occurred at 31 minutes in Figure 7.3. It is
possible — owing to the close proximity of this to the checkup at the 34th minute — that
the subject became agitated and that the reduced likelihood is as a result of the impending
checkup, but it is also possible that the deviation is due to the subject fighting the allergic
reaction. It is believed, however, that the latter option occurred in this case due to the fact
that no false positives were obtained in any of the experiments that were performed on the
allergy database (with the manual and automatic models).
7.4.3
Time gain
Table 7.3 tabulates the time gain metrics which were obtained with fully automated allergy
classification. The metrics which are presented are the specific time gain metrics, see
Chapter 5. The reason for choosing the specific time gain is because when comparing
the total time gain metrics against those obtained by the manual models the comparison
will favour the manual models. This is because the total time gain obtained by manual
models would be diluted with only one single 0 minute time gain (as 93% sensitivity was
obtained), whereas automatic results would contend with three (as 80% sensitivity was
obtained), so specific time gain results present a fairer comparison of the performance.
It can be seen from Table 7.3 that in all cases the time gain which was obtained by manual
models outperformed the time gain metrics which were obtained from the automatic
models. It can also be seen that the time gain metrics which were obtained with the
Hilbert transform QRS detection algorithm consistently outperformed those obtained
by Afonso’s algorithm even though both algorithms obtained identical sensitivity of
detection.
However, the mean difference between Hilbert and Afonso time gains is
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Table 7.3: Specific time gain metrics obtained from fully automatic allergy classification
based on Afonso and Hilbert transform QRS detectors.
QRS detector
Time gain
(mins)
Doses saved
(portions)
Activation
percentage
Afonso
Hilbert
34.62
35.47
1.47
1.67
53.77%
50.63%
Manual
39.15
1.78
31.91%
approximately one minute, which is not a significant difference. The total time gain is a
linear scaling of the sensitivity and with both QRS detectors is approximately 30 minutes.
7.5
Discussion
The unmatched classification results that were obtained were not acceptable as suboptimal
specificity was obtained. This was due to the data-driven nature of the classification and
model generation process, and, with new types of data (i.e. automatic QRS data), poor
sensitivity and specificity were obtained with the use of manual models. Because of the
disparity between the crossover results and fully manual results, it is stated that crossover
classification is not appropriate for the classification of allergy and fully automated
classification must be employed.
Fully automatic models (i.e. automatic models generated with automatic data) obtained
100% specificity. This is a very significant result as it means that (due to the subjectindependent means in which the results were obtained) equivalence can be attributed to
machine-based classification of allergy and clinical diagnosis of allergy. 80% sensitivity
was also obtained, and this corresponds to three false negative classifications. While this
sensitivity is lower than the sensitivity that was obtained with manual models, automatic
classification of allergy can still introduce a significant improvement to the state of the
art of allergy diagnosis as OFCs could have been terminated on average over 30 minutes
sooner with machine-based classification. This time gain can be used to good advantage by
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Section 7.6: Conclusion
administering antihistamines, such as Zyrtec, to counter the symptoms of allergy. Zyrtec
begins to take effect between 10 — 20 minutes and by achieving time gain of over 30
minutes it might therefore be possible to eliminate the effects of allergy for some subjects
who underwent OFC.
The thresholds which were selected for some subjects were too large to obtain classification. Figures 7.2a — 7.2c shows the likelihood traces for Subject 1, and the threshold
is outside the limits of the axes in each case. The reason for this is due to the subjectindependent means by which the threshold is selected, and the requirement to obtain
100% specificity in training as some subjects contributed to larger deviations from the
background. However, OFC will be tolerant to false negative classifications. This is
because the same vigilance which is employed during OFC would be employed during
monitored OFC, so in the cases of false negatives, the current state of clinical art is not
impaired, and safe diagnosis of allergy will always be obtained.
For Subjects 2 and 3 the models which were selected did not exemplify the signatures of
allergy that were obtained by the manual models and data. It was stated in Chapter 5 and
earlier in this chapter that the choice of an appropriate classification model is important
for the detection of signatures of allergy in likelihood traces, and indeed that a poor choice
in these parameters can have the effect of under-representing the ‘novelty’ of the HRV data.
This occurred with Subjects 2 and 3 with the automatic models, where, upon manual
investigation of alternative likelihoods, larger deviations from background levels could
have been chosen with automatic models. It is inappropriate to infer whether different
model choices would have yielded correct classification, as this would introduce manual
analysis, and the aim of this chapter is to assess automated classification of allergy.
7.6
Conclusion
This chapter has demonstrated the means by which fully automated classification of
allergy can be achieved. Because of the subject-independent nature of the procedure
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Chapter 7:
employed here, the results which were obtained should be representative of results that
will be obtained with future data. Importantly, these results obtained no false positive
classifications on the unseen data in a fully objective manner, and therefore the machinebased classification of allergy which is presented here is equivalent to clinical diagnosis
of allergy. Consequently an important question to consider is whether this procedure
might be employed for oral food challenges. Having demonstrated the ability of obtaining
100% specificity and 80% sensitivity, the application of machine-assisted classification
is appropriate for automated detection of allergy without compromising the quality of
medical diagnoses. Manual annotations (which provide an upper bound of the expected
performance) have demonstrated that it is possible to achieve higher sensitivity, but even
in light of this, the time gain results which were obtained allow the state of clinical
art to be significantly improved by over 30 minutes, which introduces the capability
of reducing the effect of allergic reactions on subjects through the early administration
of antihistamines. Therefore, the state of the clinical art of food allergy diagnosis can
be significantly advanced with digital signal processing and artificial intelligence based
monitoring of the heart rate variability features during oral food challenge.
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CHAPTER 8
Overall summary, final conclusions
and future work
8.1
T
Summary of this thesis
HIS thesis has presented an investigation of the applicability of machine-based
analyses of non-invasive on-body sensors for the purpose of automatically detecting
signatures of food allergy. This work was inspired by two observations of allergists who
have conducted food challenges. The observations were that there is both a tendency
for subjects to become quiet before the onset of allergic reactions, and that there is also
a tendency for the heart rate of the subjects to change before allergic reactions occur
(Bindslev-Jensen et al., 2002).
It was shown that the accelerometer-based approaches investigated in this thesis did not
provide a viable means of assessing the state of allergy, even though this is one of the
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observations identified by the allergists as being indicative of the onset of allergy. Indeed,
this is, perhaps, the more identifiable of the two observations for the allergists as it is
easily observed. The reason for the poor efficacy of acceleration-based analysis is that the
range of activities — and therefore the movements and related energy expenditure — that
the subjects can undertake is limited by the fact that subjects are required to remain on
a bed for the duration of the oral food challenge. As a result of this, no separability was
achieved between allergic and non-allergic subjects with activity-based metrics and with
energy expenditure estimation algorithms. It is possible that by allowing subjects to play
freely that a means of allergy detection based on activity and energy analyses could be
formed. Furthermore, it is possible that with placebo-based challenges that more nonallergic data would be available for model generation. However, these new procedures
would require a new brand of oral food challenge and were not investigated.
The second observation was then investigated.
In order to confirm the existence of
signatures of allergy in the ECG, the QRS points were manually annotated. Heart rate
variability features were then extracted from these annotations, and the features were
separated based on whether the data originated from an allergic subject. By these means,
it was possible to assess the characteristic differences between the allergic and nonallergic heart rate variability features. This is the first work which has investigated this,
and indeed it was shown that, even in single-dimensional representations, many of the
features extracted from the ECG obtained separability between the allergic and the nonallergic classes. In comparison to the separation capabilities which were obtained with the
accelerometer data, the heart rate variability data provides a more suitable platform on
which to base machine learning algorithms.
GMM-based novelty detection was employed on manually annotated HRV feature data
with nested, subject-independent parameter selection and performance assessment routines. This type of classification was performed because the only set of labelled data
available was the background data which was recorded before the administration of
the first dose of the allergen. By these novelty detection means, 93% sensitivity and
100% specificity were obtained. These metrics also facilitated in obtaining approximately
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Section 8.1: Summary of this thesis
39 minutes time gain after consumption of just 30% of the dose which can drastically
reduce the risk of reactions. This is the first research which has been performed for the
classification of allergy based on the heart rate variability features. The results show that
this approach is very suitable and that through these means excellent classification, perfect
specificity and very high time gain results can be obtained. Manual QRS annotations yield
undeniable affirmation of this as there is no uncertainty about the validity of the QRS
points and therefore the features.
These results were obtained with manually annotated QRS points, and therefore represent
the upper-bound of the expected results that could be obtained with automatically
extracted heart rate variability features.
Two automatic QRS detection algorithms
were therefore investigated, and these were validated against the well-known MITBIH arrhythmia database. These algorithms were then tested against the ECG which
was recorded during the oral food challenges.
Both of these algorithms performed
satisfactorily against both databases, and were therefore employed for the fully automated
and autonomous classification of allergy.
From the automatically identified QRS points, heart rate variability features were extracted. These were employed with the subject-independent classification framework
mentioned previously. It was found that 100% specificity can be obtained by this process.
While this was previously obtained with manual classification models, this is a very
significant result as they were obtained in a fully automated manner. For this fullyautomated classification of allergy framework, therefore, it can be stated that classification
of allergy is equivalent to a diagnosis of allergy because no false positive classifications
were obtained.
Sensitivity of 80% was obtained by the fully automated routine. This value is lower than
that which was obtained with manual annotations, but the time gain metrics which were
obtained with this data were very strong, and approximately 30 minutes would have
been saved on average. While this time gain is also lower than that which was obtained
with the manual classification models, achieving this in a fully automated manner is a
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very significant result. This time gain could be employed to administer antihistamines
to the subjects which could potentially eliminate allergic reactions for some subjects.
Alternatively, this time gain could be employed to allow the subjects to recover from the
allergic condition naturally, as with no additional doses of the allergen administered it
may be possible that the subjects could ‘fight’ the reaction and overcome the symptoms
naturally.
8.2
Primary contribution of this thesis
Allergy is a chronic disorder which can only be diagnosed in a clinically invasive manner.
This thesis has been the first to investigate two approaches to the classification of allergy.
It was shown previously that accelerometer-based assessment of activity (Twomey et al.,
2010b) during oral food challenges is unable to characterise the allergic and non-allergic
classes in a manner which allows separability. This thesis has ruled out a number of
avenues for its applicability with allergy classification.
However, it was discovered that allergy affects the heart rate variability in a detectable
manner where it can be exploited to classify the condition (Twomey et al., 2010a,
2011, 2013b). It was also discovered that signatures of allergy are sometimes better
exemplified at different epoch lengths. By employing novel result fusion in a subjectindependent manner, subtle characteristics of the signatures of allergy can be identified
and classification of allergy can be achieved in an objective manner that obtains 100%
specificity. This allows equivalence to be drawn between the automatic classification and
clinical diagnosis of allergy.
Signatures of allergy were repeatedly identified on the heart rate variability features before
the onset of physical reactions. In fact, this thesis has shown how signatures of allergy
can be classified approximately 30 minutes earlier than allergists with the consumption
of approximately two-thirds less of the problem foods. The significance of this is that
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Niall Twomey
subjects could be diagnosed with the same certainty and accuracy, but with a reduced risk
of allergic reactions and anaphylaxis (Twomey et al., 2013a).
The chapters of this thesis described the means to significantly and safely advance the
current state of clinical art of allergy diagnosis in an objective and automatic manner, and
this work, therefore, supports the case for the inclusion and use of intelligent monitoring
for the diagnosis of allergy during oral food challenges.
8.3
Possible avenues of future work
This thesis has explored a number of approaches to classifying food allergy. There are
other avenues which deserve examination that could not be investigated in this thesis.
These are briefly described below.
8.3.1
Data collection
The results obtained excellent and consistent results, which, due to the subject independent nature of the classification routine, is indicative of the relationship between allergy
and the HRV. In order to further define the effect of allergy on the HRV features, a
larger database on which to test this classification routine should be obtained. This
should be the primary focus of any researcher who takes on this research, and with
this, other classification methods, such as bootstrapping, can be investigated with ‘fixed’
models. In contrast to the leave one out procedure that was used in previous chapters,
bootstrapping utilises multiple test subjects and would validate the robustness of one
model over multiple participants in the test set.
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8.3.2
Chapter 8:
Alternative novelty detectors
Chapters 2 and 5 discussed the area of novelty detection, and the reasons that GMM-based
classification was selected. However, while GMM-based novelty detection was stated
as being preferable for the initial analysis, investigation into alternative classification
routines should also be performed. Two alternative novelty detection routines which
might be analysed include:
One-class SVM: The principal alternative is the one-class SVM. These classifiers are
the single-class equivalent to the popular SVM discriminative classifier (Schölkopf
et al., 2000, 2001) and these estimate the boundary of support of the singly-labelled
data, efficiently making this a discriminative novelty detection classifier. Whereas
GMM-based novelty detection computed the likelihood of new data belonging to the
background class, one-class SVMs compute the distance of new data to the surface
of a hypersphere which surrounds the background distribution. One-class SVMs
can also employ the ‘kernel trick’ (Aizerman et al., 1964) which can perform nonlinear mapping of features to higher-dimensional feature space. This has proven to
be useful for many applications, and may prove to be a useful feature for allergy
classification.
Elliptic envelope: Minimum covariance determinant estimator (Rousseeuw and Van Driessen,
1999) can also be employed to detect novelty as described here. This algorithm effectively fits elliptic curves about training distributions, and benefits from rotational
symmetry which one-class SVMs and GMMs may not encompass. This method
works well for normal (and normal-like) data, but with multimodal distributions,
elliptic envelopes have a tendency to poorly model the underlying structure as it
cannot generalise to multimodal distributions unlike GMMs and GMMs. Yet, it
might yield interesting results with allergy detection as it should result in a simpler
model representation.
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8.3.3
Niall Twomey
Real-time and portable implementation
All of the classification and analyses which were performed here were performed offline, i.e. the data was recorded and stored, and the data interrogation was performed
on a computer. While the results which were presented at the end of this thesis are
fully automated, it would be interesting to verify that the same classification results are
obtained in a real-time solution. Preliminary work for this has been performed already
(Gutiérrez et al., 2013).
For a fully-wireless solution, a mobile device would record the ECG data, and compute
features and their novelty. However, as it might be necessary to review the decisions that
the mobile solution obtained, it would be necessary to log the ECG data electronically
and transfer this via a wireless link. These two operations are heavy consumers of power
in mobile platforms. With lossy compression, however, the data which needs to be
transmitted can be compressed to a high degree. Twomey et al. (2010c) demonstrated that
with a compression ratio of 30, reliable heart rate variability features can be extracted, and
the area of ECG compression has also been investigated by other researchers (Olmos et al.,
1996; Craven et al., 2012). Analysis of the effect of compression on classification accuracy
and overall time gain would unveil the applicability of the lossy compression solution for
long-term monitoring situations.
8.3.4
Feature and epoch analysis
It was demonstrated that in some cases different epoch lengths display the effects of
certain characteristics of allergy better than others. It would be useful to investigate if
features which are employed with speech classification (Temko et al., 2010, 2011a) or EEG
classification (Doyle et al., 2010) might be suitable in obtaining accurate classification of
allergy with fusion of fewer epoch lengths.
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Additional insight could be gained from quantifying the effectiveness of the individual
features which were employed for the classification. Preliminary work which investigates
the importance of the categories of features was performed on this in Appendix B, and it is
concluded that the full set of features is required for the best classification and time gain
results. However, fuller investigation is justified, and analysis of the results of algorithms
such as recursive feature elimination could be assessed.
8.4
Publications resulting from this work
The publications which have resulted from this research and which have been published,
are currently in review and preparation are listed below.
Twomey, Niall and Faul, Stephen and Marnane, William P (2010); Comparison of
accelerometer-based energy expenditure estimation algorithms; Pervasive Computing Technologies for Healthcare; pages 1–8.
Twomey, Niall and Walsh, Noel and Doyle, Orla and McGinley, Brian and Glavin, Martin
and Jones, Edward and Marnane, WP (2010); The effect of lossy ECG compression on QRS
and HRV feature extraction; Engineering in Medicine and Biology Society; pages 634–638.
Twomey, Niall and Faul, Stephen and Daly, Deirdre and Hourihane, JO and Marnane,
William P (2010); Classification of biophysical changes during food allergy challenges;
International Symposium on Applied Sciences in Biomedical and Communication Technologies;
pages 1–5.
Twomey, Niall and Temko, Andrey and Hourihane, Jonathan O’B and Marnane, William
P (2011); Allergy detection with statistical modelling of HRV-based non-reaction baseline
features; International Symposium on Applied Sciences in Biomedical and Communication
Technologies; pages 134–138.
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Niall Twomey
Twomey, Niall and Temko, Andrey, and Cullinane, Claire, and Daly, Deirdre, and
Marnane, William P, and Hourihane, Jonathan O’B (2013); Detection of heart rate variation
could improve patient safety and diagnostic yield during oral food challenge; European
Academy of Allergology and Clinical Immunology; In press.
Twomey, Niall and Gutiérrez, Raquel, and Marnane, William P, and Campos-Garcia, Jesús
(2013); Real-Time Allergy Detection; IEEE Society of Intelligent Signal Processing; In press.
Twomey, Niall and Temko, Andrey and Hourihane, Jonathan O’B and Marnane, William
P (2013); Fully automated allergy detection from paediatric ECG; IEEE Transactions on
Information Technology in Biomedicine; In press.
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APPENDIX A
Alternative parameter selection
routines
A.1
T
Introduction and Methods
HIS chapter investigates the effect of alternative model selection routines. Previously, decision making parameters were chosen based on a cost function and
candidates which did not satisfy the cost function were eliminated from consideration.
This process resulted in the elimination of many tens of thousands of parameters. While
this process yields excellent classification and time gain results, this chapter investigates
whether the optimal results are obtained by employing alternative parameter selection
routines based on less-austere cost functions.
In Chapter 5, the means by which the parameters were chosen was discussed. The number
of parameters which are selected is strongly influenced by the constraint of selecting the
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Chapter A:
parameters which obtain the maximum time gain in the training data, and in general
only one parameter is selected from the entire search space. The effect of relaxing this
restriction on the data, and alternative means of selecting post-processing parameters was
investigated here.
The statistical mode (i.e. the most frequently occurring parameter) was the means by
which parameters were selected in Chapter 5. The parameter search space is reduced by
means of a cost function which assesses the entire search space and reduces the quantity
by the following three factors (listed in order of importance):
1. Eliminate parameters which fail to obtain 100% specificity.
2. Eliminate parameters which fail to obtain the maximum sensitivity from the subset
resulting from step 1.
3. Eliminate parameters which fail to obtain the maximum time gain from the subset
resulting from step 2.
While item 1 is the most important (followed by items 2 and 3) the number of parameters
which are eliminated by each step are ordered in descending order of importance of each
step, i.e. step 3 eliminates the majority of the parameters while step 1 eliminates the
fewest. Approximately ten thousand parameters are removed by step 3⇤ , and it is by
considering the set of ten thousand parameters that this alternative parameter selection
is performed.
Figure A.1 shows an example density of parameters obtained before the time gain criteria
eliminated parameters. The density distribution is plotted against the multiplicative and
duration parameters, and darker regions indicate areas of higher density. Parameter
selection which terminates at item two of the itemisation is termed ‘relaxed parameter
selection’ henceforth, as the time gain criteria is not considered by this process and this is
⇤ This number is dependent on many factors, but 10,000 was calculated based on the average number of
parameters which were obtained over all of the internal leave-one-out stages from the simulations which were
performed.
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Section A.1: Introduction and Methods
Figure A.1: The estimated distribution of duration and multiplicative post-processing
parameters which achieve 100% specificity and maximum sensitivity on the training
dataset. The image is limited to d and n parameters of 75. The darker regions indicate
a higher density of suitable parameters.
viewed as a relaxation of the routine. The parameters which result from this are termed the
‘relaxed parameter set’. For clarity, the original parameter selection routine from Chapter
5 is termed the ‘original parameter selection routine’ in this section and the ‘original
parameter set’ are the parameters which result from this.
It can be seen in Figure A.1 that at a duration of 1 yields the highest density of parameters.
This is an intuitive value as with a constant multiplicative parameter and an increasing
duration parameter, there will be a larger quantity of points which satisfy a smaller
duration parameter than would a larger parameter.
It is also expected that with a constant duration parameter and an increasing multiplicative parameter that the density will increase to a maximum value and will then
reduce, as is shown in Figure A.1. This is because low multiplicative parameters will
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Chapter A:
not satisfy the 100% specificity criteria and are rejected from step 1 and few parameters
are chosen. As the multiplicative parameter increases an increasing number of points are
selected. However, as the parameter continues to rise the number of points which satisfy
the maximum sensitivity criteria begins to roll off, and fewer points are selected as the
maximum sensitivity is not attained.
Ripples which are seen on the chart for increasing multiplicative values are due to the
distribution of the duration and multiplicative parameters. The search space consists of
the unique, integer-rounded values which are logarithmically distributed between 1 and
300. The density chart was obtained by KDE and in Figure A.1 the x– and y-axes are
continuously distributed while the search space is not. Therefore, when the density of the
parameters is estimated between the discrete values, the density reduces until the next
parameter has been encountered.
This section investigates the effect of selecting the mean, median and statistical mode of
the distribution of the relaxed parameter set.
A.2
Results
Table A.1 tabulates the sensitivity, specificity and time gain metrics which were obtained
when employing the relaxed parameter selection constraints. The results which were
presented in this table were calculated by the boosting classification procedure described
in Chapter 5.
The mean selection routine achieved the same sensitivity as the results from the original
parameter selection routine. Subject 16 was misclassified as allergic by this parameter
selection routine, however. This is the same subject who was stated in Chapter 5 as being
physically opposed to consuming the food type. Even though the subjected ended up not
being allergic to the food type, given the subject’s attitude during the OFC, it is likely that
allergists performing the challenge would not utilise automatic classification.
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Section A.2: Results
Table A.1: Tabulation of sensitivity, specificity, and the time gain metrics which were
obtained by selecting the mean, median and mode of the set of post-processing parameters
from the training data. In the case of the mean method, imperfect specificity was obtained.
Selection type
Sensitivity
%
Specificity
%
Time gain
mins
Mean
Median
Mode
93.33
86.66
93.33
88.88
100.00
100.00
28.36
21.75
20.29
Chapter 5
93.33
100.00
36.5
The cause for the misclassification of Subject 16 is that the duration and multiplicative
parameters are typically biased in a region where the threshold can be surpassed by
both allergic and non-allergic subjects.
The duration parameter will reject spurious
deviations from the background and only substantiated departures from the background
will be classified as allergy. However, upon investigation of Figure A.1, it can be seen
that a high proportion of duration parameters at a value of 1 are obtained. As a result,
when computing the mean parameter of the distribution, a lower duration parameter was
selected, and Subject 16 was misclassified as allergic as a result.
The median and mode selection methods obtained perfect specificity, and sensitivity of
87% and 93% respectively; results which are competitive with those obtained by the
original parameter selection criteria. It is interesting to note, however, that for both full
and relaxed selection criteria, the parameter selection routine based on the mode of the
distributions obtained the same sensitivity and specificity.
Table A.1 also shows the mean specific time gain which was calculated from these
parameter selection methods. Of the values obtained with the relaxed parameter selection,
parameter selection based on the mean of the distribution gain obtained the largest time
gain. This is also due to the high percentage of low-value duration parameters in the
distribution. The selection routines based on the median and mode functions achieved
approximately equal time gain. In all cases, however, the total time gain obtained from
the original parameter selection criteria outperform the relaxed criteria.
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A.3
Chapter A:
Discussion
Alternative parameter selection routines can yield classification results which are as
accurate as the parameter selection routine which was described in Chapter 5 (sensitivity
of 93.33% and 100% specificity). This is because the full parameter selection routine
selects parameters which have been ‘known’ (from training data) to obtain the best time
gain results, and this high accuracy was preserved between training and unseen testing
data.
In many cases the signature of allergy is detected a number of times during the OFC, as is
shown by Figure 5.8 (Chapter 5, page 123) where the allergy is classified at approximately
45, 60 and 80 minutes. With the original parameter selection routine, the multiplicative
and duration parameters were selected in such a way as to specialise towards finding the
first signature of allergy from the training data. With a plurality of departures from
the background likelihoods, it was observed that there is a tendency that the extent of
departure from the background levels increases in comparison to the previous departure.
Therefore, smaller multiplicative parameters will yield better time gain results as they
provide the greatest likelihood of satisfying the failure criteria.
However, the relaxed parameter selection routine gives no consideration to the time gain
values when selecting parameters. Hence the sensitivity and specificity results which were
obtained were excellent and in some cases matched the sensitivity and specificity results
obtained in Chapter 5.
A.4
Conclusion
This section investigated whether the parameter selection routine utilised previously
selected the optimal parameters in comparison to other parameter selection routines.
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Section A.4: Conclusion
It was shown that alternative selections can yield equal sensitivity and specificity to what
was obtained in Chapter 5. However, as the selection routine is not time-gain aware, the
time gain results were approximately one half of those which were obtained with time
gain aware parameter selection.
Therefore, the parameter selection routine which was incorporated previously is the best
methodology of all obtained, as it achieved the best accuracy and time gain results and
this process should be used for parameter selection .
207
APPENDIX B
Investigation into the importance of
features
B.1
F
Introduction and methods
OR the classification of allergy, PCA is performed on normalised training data. This
transformation was performed in order to de-correlate the features which was a
requirement for allergy classification as the allergy database is insufficiently sized to train
GMMs with full covariance matrices.
With the transformed features, GMMs were generated. The EM algorithm is employed to
compute the optimal means, covariances and weights of the mixture model. It is possible
to investigate the explained variances of the PCA matrices, (Wold et al., 1987) and the
weights which are attributed to the features in order to assess the relative significance of
the features. However, these values relate to the PCA components rather than the features
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Section B.1: Introduction and methods
themselves. It is not appropriate to assign interpretation to the transformed features, in
particular when normalisation was performed (Webb et al., 2011; Bishop et al., 2006) so
explained variances and Gaussian weights cannot be utilised to assess the importance of
the features.
It is also difficult to employ the probability density functions which were presented in
Chapter 4 as a means of quantifying the effectiveness of individual features. This is
because these will contain a significant amount of non-allergic data (as non-allergic HRV
features will present in an allergic subject’s challenge until an allergic reaction occurs).
Therefore, in order to quantify the importance of feature categories, the allergy classification framework utilised previously was employed on subsets of features, where each
subset is representative of a feature type. The full set of features can be loosely grouped
into time, frequency, Poincaré and sequential groups (see Chapter 4 for the members of
each group). Classification was then performed once for each subset of feature category.
Based on the results of this process, higher feature importance is attributed to feature sets
which obtain superior results. The order of importance of the results are as follows:
1. 100% Specificity.
2. Sensitivity.
3. Time gain.
and ranking is achieved first by descending specificity, then by descending sensitivity and
finally by descending time gain.
Two key differences between the classification framework outlined in Chapter 5 and this
process outlined here exist. Firstly, as the process outlined here performs classification
based on a subset of features, the dimensionality of the input to the classification
framework is reduced from 18 to N , where N is the number of features associated
with a particular category.
The second difference is that the full set of N features
is then preserved after the PCA transformation, and dimensionality reduction is not
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Chapter B:
performed. This procedure was chosen because this process was executed to assess the
significance of feature categories. Allowing feature reduction on the feature categories
would not illuminate the importance of the feature categories as only the importance of
the components could be attained.
The models which were generated are termed time, frequency, Poincaré and sequential
domain models and these relate directly to the category of the features on which the
models were trained.
B.2
Results
Table B.1 tabulates the results which were obtained. This table tabulates the sensitivity,
specificity, total time gain, doses saved and activation percentage metrics for all models
investigated (for definitions of these metrics, see Chapter 5). The time gain metrics which
are presented are the total time gain results which were obtained by the experiment. This
metric was chosen over the specific time gain parameter because the overall performance
of classification is of interest rather than the specific performance on certain correctly
classified subjects.
Table B.1: Classification metrics which were obtained with the time–, frequency–,
Poincaré– and sequential-domain classification models ranked by order of importance of
the feature category in question.
Feature
Category
Specificity
Sensitivity
Time gain
(mins)
Doses saved
(portions)
Activation
percentage
Time domain
Poincaré domain
Frequency domain
Sequential domain
100.00%
100.00%
77.77%
66.66%
80.00%
53.33%
6.66%
13.33%
30.25
15.63
0.65
0.06
1.27
1.0
0
0.13
54.44%
69.19%
100.00%
95.55%
Chapter 5
100%
93.33%
36.5
1.67
38.57%
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Section B.2: Results
It can be seen that time-domain features achieved good classification metrics in all cases,
obtaining 100% specificity and 80% sensitivity. Interestingly, Subjects 1, 3 and 4 were
misclassified by the time domain models. It is not surprising that Subjects 1 and 3
were misclassified as Subject 1 wasn’t classified correctly by any classification routine and
Subject 3 was inconsistently classified at different epoch lengths in Chapter 5. However,
this is the first instance over all classification runs up to this point where Subject 4 is
misclassified as non-allergic. It is interesting to note that while the time domain models
achieve very good overall performance, one of the most consistently classified subjects of
the allergy database is misclassified by the highest ranked feature category.
The frequency domain models achieved very poor results.
This was an unexpected
result because the frequency domain parameters can estimate the sympathetic and
parasympathetic response of the ANS (Kamath et al., 1993; Stein et al., 1999; Hedman
et al., 2008).
It was postulated that quantification of these metrics would be good
indicators of allergic signatures in Chapter 4, as a subject’s ANS should react to the onset
of an allergic reaction. It is uncertain why such poor classification of allergy was achieved
with frequency domain features. However, upon investigation of the histograms which
were obtained in Chapter 4, it can be seen that poor separation was obtained between
the allergic and non-allergic categories. It is interesting to note that Subject 4 was the
only subject who was correctly classified by these models when this subject was one of the
subjects who was misclassified by the time domain models. The frequency domain models
also misclassified two non-allergic subjects as allergic, and achieved very low average time
gain as so few subjects were correctly classified.
The Poincaré models performed adequately, yielding 53% sensitivity and 15 minutes
average time gain. However, Poincaré features also measure the response of the ANS
(Brennan et al., 2002; Mourot et al., 2004; Piskorski and Guzik, 2005) but the models
that were generated based on these features outperformed the frequency-domain models
which can also measure the response ANS. It is also interesting to note that the Poincaré
features identified allergy in Subject 3, who has been inconsistently classified. Indeed,
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Chapter B:
this is the only feature category which correctly classified Subject 3 as allergic. Poincaré
features obtained 100% specificity.
The sequential domain models performed poorest of all, misclassified three non-allergic
subjects, obtained the lowest specificity (66.66%), and only correctly classified two allergic
subjects (13.33% sensitivity). It is very interesting to note that one of the two subjects who
was classified correctly as allergic was Subject 1 (obtaining a time gain of approximately
15 seconds). This is the only case in which the parameters which were selected achieved
correct classification of this subject. However, while correct classification of Subject 1
occurred, it should be noted that the majority of the remaining allergic subjects were
incorrectly classified.
B.3
Discussion
It must be noted that the results which were obtained in this section are not intended to
compete with the classifiers discussed in previous chapters. The purpose of this section is
to assess the importance of each feature category and to investigate whether the optimal
results were obtained in Chapter 5 with the full set of features.
When ranking the feature categories based off the importance of the results, the most
important feature category was found to be the time-domain features. These features
yielded 100% specificity, 80% sensitivity and an average of 30 minutes time gain. Three
subjects were misclassified as non-allergic by these features because the likelihoods which
were selected did not deviate from background levels to the same degree as they did with
manual models. Upon investigation of these subjects, it was seen that two were correctly
classified by the manual models. The cause for misclassification of these subjects is that
the time domain features alone did not exhibit signatures of allergy for these subjects.
The specific time gain of the time domain features, approximately 37 minutes (30.25 ⇥
15
12
minutes), is very close to the specific time gain obtained by manual models (39.15
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Section B.3: Discussion
minutes). Therefore, the time domain models are the biggest contributor of time gain to
allergy classification as well as the best performing individual feature category.
Interestingly, classification based on the sequential domain features correctly classified
Subject 1 as allergic. This is the first and only instance where this Subject was correctly
classified as allergic.
While this subject was correctly classified, the sensitivity and
specificity which were obtained with the sequential domain classification models was
quite poor. Therefore, it appears that the sequential domain features are suitable for the
classification of quick-onset allergic reactions, but these features do not appear to be able
to well distinguish between allergic and non-allergic subjects. The number of quick-onset
allergic reactions is not sufficient to substantiate this assertion with certainty. However,
evidence based on previous models support the argument as these correctly classified
the entire set of allergic subjects except for Subject 1. Indeed, as this feature achieved
poor specificity it is likely that contributions of features in this category were removed by
feature reduction during PCA model selection.
Subject 16 was one of the three non-allergic subjects who were misclassified as allergic by
the sequential features. This subject was discussed in Chapter 5 and it was stated that they
presented with allergy-like signatures in the likelihoods which were obtained. As a result,
the likelihood values computed for this subject resembled those which might be obtained
by allergic subjects.
The Poincaré models yielded 100% specificity and approximately 50% sensitivity. However, while this feature category did not yield as high a sensitivity as the time domain
features, Subjects 3 and 4, who were both misclassified by the time domain models,
are correctly classified by the Poincaré models.
If one considered the fusion of the
results of the time domain and Poincaré models, one would achieve the same accuracy
of classification that was obtained in Chapter 5.
The fact that the combination of the time domain and Poincaré domain features matches
the sensitivity obtained previously is a retrospective result which sheds light on the
importance of considering combinations of features, and should not be interpreted as
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Chapter B:
the best of features which will obtain the optimal features results. The reason for this is
that the choice of time domain and Poincaré domain features together might yield results
which are comparable to those obtained by the manual models, but they may not yield the
optimal results, and inclusion of less important features may yield better results.
One of the Poincaré features computes the CSI which is a measure of the sympathetic
response of the autonomic nervous system. However, while the frequency domain features
also measure this response, the frequency domain parameters did not achieve good
classification results, achieving the second lowest specificity, and the lowest sensitivity.
B.4
Conclusion
This section performed allergy classification on subsets of features for the purpose
of assessing the importance of each category of feature in allergy classification and
identifying whether the results obtained in Chapter 5 could be improved.
It was discovered that the time domain features account for the majority of the sensitivity
and time gain that was obtained in the Chapter 5. However, the sensitivity obtained
with time gain models alone did not account for the entirety of the results obtained in
previous chapters. For the optimal results, the inclusion of other features complement the
classification process and yield the best results.
However, correct classification of Subject 1 was obtained by the sequential domain
features. This indicates that there might is a possibility of obtaining perfect sensitivity of
classification. It is uncertain whether it might also be possible to obtain perfect specificity
as the sequential domain features achieved very poor specificity. However, as the most
important criterion for classification of allergy has consistently been stated as obtaining
100% specificity, it is believed that the overall results which were obtained by utilising the
full set of features cannot be improved upon, and that the full set of features should be
employed for allergy classification.
214
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