Automated Cardiac Rhythm Diagnosis for Electrophysiological Studies, an Enhanced Classifier ...

291 Pages·2015·4.32 MB·English

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City, University of London Institutional Repository
Citation: Bostock, J. (2014). Automated Cardiac Rhythm Diagnosis for
Electrophysiological Studies, an Enhanced Classifier Approach. (Unpublished Doctoral
thesis, City University London)
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Automated Cardiac Rhythm Diagnosis for
Electrophysiological Studies, an Enhanced Classifier
Approach

Julian Bostock

Submitted towards the degree of Doctor of Philosophy
at
City University London

Centre for Health Informatics

Supervisor: Dr Peter Weller

August 2013
1
Table of Contents

Title Page 1
Table of Contents 2
List of Tables 12
List of Illustrations 14
Acknowledgements 16
Declaration 17
Abstract 18
Abbreviations and Acronyms 19
Chapter 1 Introduction 25
1.1 Overview 25
1.2 Background 25
1.3 Conventional Cardiac Arrhythmia Diagnosis and Treatment 26
1.3.1 Non-Invasive Arrhythmia Diagnosis 26
1.3.2 Invasive Arrhythmia Diagnosis 27
1.3.3 Treatments for Cardiac Arrhythmia 28
1.3.4 Implantable Pacemakers and Cardioverter Defibrillators (ICDs) 28
1.3.5 Ablation 30
1.3.6 Arrhythmia Surgery 30
1.4 Computerised Arrhythmia Diagnosis 30
1.4.1 Rhythm Classification in ECG Interpretation 31
1.4.2 Rhythm Classification in Electrophysiological Studies 31
1.4.3 Rhythm Classification in ICDs 31
1.4.4 Rhythm Diagnosis Algorithms – Bench Studies 32
1.4.5 Computational Power and Automated Rhythm Diagnosis 32
1.4.6 Misdiagnosis in Automatic Rhythm Diagnosis 33
1.5 Towards Accurate Arrhythmia Diagnostic Algorithms 34
1.6 Research Question 35
1.7 Aims and Objectives 35
1.8 Hypotheses 36
1.9 Research Plan 36
1.10 Summary 36
Chapter 2 Literature Review 37
2.1 Overview 37
2.2 Search Terms 37
2.2.1 Search Scope 38
2.3 Inclusion and Exclusion Criteria 38
2
Table of Contents (continued)

Chapter 2 Literature Review (continued)
2.3.1 Inclusion Criteria 38
2.3.2 Exclusion Criteria 39
2.4 Literature Sources 39
2.5 Search Strategy 40
2.5.1 Granularity of Search 40
2.5.2 Relevance 40
2.6 Search Results 40
2.6.1 Results by Year of Publication 41
2.6.2 Results by Journal 41
2.6.3 Results by Author 42
2.6.4 Conferences 43
2.6.5 Results by Algorithm 43
2.7 Selection of References 43
2.7.1 Bibliometric Analysis 43
2.7.2 Supplementary References 44
2.7.3 Evidence 44
2.8 Critical Reviews 44
2.8.1 Review Papers 44
2.8.2 Statistical Classifiers 46
2.8.3 Syntactic Classification 46
2.8.4 Neural Network Classifiers 46
2.8.5 Fuzzy Classifiers 48
2.8.6 Decision Tree Classifiers 48
2.8.7 Support Vector Machine Classifiers 48
2.8.8 k-Nearest Neighbour Classifiers 49
2.8.9 Other Classifier Technologies 49
2.8.10 Hybrid Classifiers 50
2.8.11 Multi-Classifier Systems (MCS) 50
2.8.12 Comparative Studies of Classifiers 52
2.8.13 Single ICD Algorithms 52
2.8.14 Comparative Studies of ICD Algorithms 53
2.9 Summary of Literature 55
2.9.1 Reviews 55
2.9.2 Single Classifiers 55
2.9.3 Hybrid and Multi-Classifier Systems 58
3
Table of Contents (continued)

Chapter 2 Literature Review (continued)
2.9.4 ICD Classifiers 59
2.9.5 Feature Selection 60
2.10 Classifier Data Sources 61
2.11 Summary 61
Chapter 3 Methodological Approach 63
3.1 Overview 63
3.2 Artificial Intelligence 64
3.3 Logical Reasoning in AI 64
3.4 Problem-Solving in AI 64
3.5 Pattern Recognition and Classification 65
3.5.1 Template Matching 65
3.5.2 Statistical Pattern Recognition 65
3.5.3 Syntactic Pattern Recognition 65
3.5.4 Unsupervised and Supervised Learning 65
3.5.5 Classification 66
3.6 Uncertainty in Classifiers 66
3.7 Data Sets for Classifiers 66
3.8 Data Partitioning 67
3.9 Missing Data 67
3.10 A Knowledge-Based System 67
3.10.1 Domain 67
3.10.2 Ontology 68
3.10.3 Knowledge Engineering 68
3.10.4 A Cognitive Model of the Diagnostic Process 73
3.11 Types of AI Classifier 73
3.12 Measuring Classifier Performance 73
3.12.1 The Gold Standard Test 73
3.12.2 Confusion Matrices and Contingency Tables 73
3.12.3 Measures of Diagnostic Test Performance 74
3.12.4 Weak and Strong Learners 74
3.12.5 Consistency and Generalisability 75
3.12.6 Error, Bias and Variance 75
3.13 Comparing Classifiers 75
3.13.1 Bayesian Analysis 75
3.14 Classifier Selection 76
4
Table of Contents (continued)

Chapter 3 Methodological Approach (continued)
3.15 Designing a Multiple Classifier System 76
3.15.1 Accuracy and Diversity 76
3.16 Classifier Combiners 77
3.16.1 Combining Specialist Classifiers 78
3.17 Current ICD algorithms 79
3.18 The Basis of Cardiac Rhythm Analysis 80
3.18.1 Rhythm Change – Guidelines 81
3.18.2 Number of Beats to Diagnose Rhythm 81
3.19 System development 83
3.19.1 The Incremental Build Model 84
3.20 Summary 84
Chapter 4 Implementing the System Development Process 86
4.1 Overview 86
4.2 User Requirement 86
4.3 System Specification 86
4.4 System Design 89
4.5 Iteration Implementation (Prototype Build) 89
4.6 Iteration Verification (Prototype Testing) 89
4.7 Prototype Operation and Maintenance 90
4.8 Summary 90
Chapter 5 Feature Selection 91
5.1 Overview 91
5.2 Features used in Implantable Pacemakers and Defibrillators 91
5.2.1 Electrogram Intervals 91
5.2.2 Electrogram Morphology 93
5.2.3 Accelerometry 94
5.3 Feature Selection by Review of the Literature 94
5.3.1 QRS Duration 94
5.3.2 Heart Rate Variability 95
5.3.3 Heart Rate Turbulence 95
5.3.4 QT interval and T waves 95
5.3.5 Peak Endocardial Acceleration and Heart Sounds 96
5.3.6 Body Temperature 96
5.3.7 Blood Oxygen Saturation 96
5.3.8 Blood pH 97
5
Table of Contents (continued)

Chapter 5 Feature selection (continued)
5.3.9 Blood Pressure 97
5.3.10 Bio-impedance 97
5.4 Feature Selection by Knowledge Engineering 98
5.4.1 Domain Expertise 98
5.4.2 Clinical Diagnosis of Arrhythmia 98
5.5 Results of Feature Selection 99
5.6 Summary 99
Chapter 6 Preparation for Data Collection 101
6.1 Overview 101
6.2 Guidelines for Diagnostic Trials 101
6.3 Sub-study Population 101
6.3.1 ICD Implant Patients as Sub-study Population 102
6.3.2 Electrophysiological Studies Patients as Sub-study Population 103
6.3.3 Sub-study Population 103
6.4 Sample Size Estimation - Powering the Sub-study 103
6.4.1 Effect, α, β and Power 103
6.4.2 Sample size Estimation 104
6.4.3 Converting Samples into Patients 105
6.5 Sub-study Ethics Application 106
6.5.1 Type of Study 106
6.5.2 Sub-study Objective 106
6.5.3 Setting 106
6.5.4 Duration of the Sub-study 106
6.5.5 Recruitment 106
6.5.6 Conduct Monitoring 106
6.5.7 Potential Risks and Benefits to Participants 107
6.5.8 Funding 107
6.5.9 Main Ethical Issues 107
6.5.10 Data Types 107
6.5.11 Data Security 107
6.5.12 Data Retention 108
6.5.13 Data Verification 108
6.5.14 Sub-study Inclusion and Exclusion Criteria 108
6.5.15 Participant Recruitment 109
6.5.16 Informed Consent 109
6
Table of Contents (continued)

Chapter 6 Preparation for Data Collection (continued)
6.5.17 Withdrawal of Consent 109
6.5.18 Insurance and Liability 109
6.6 Converting Features to Measured Parameters 110
6.6.1 Features from Clinical Evaluation 110
6.6.2 Features from Diagnostic Testing 110
6.6.3 ECG Features 110
6.6.4 Features Related to Stress 111
6.6.5 Haemodynamic Status 113
6.6.6 Pace Termination 115
6.6.7 Equivalent Measurable Parameters 116
6.7 Equipment Selection 116
6.7.1 A Generic Equipment List 116
6.7.2 Technical Specification: Bandwidth, Sampling Rate and Resolution 116
6.7.3 Satisfying the Specification 117
6.7.4 Additional Equipment 118
6.8 Consumables 118
6.9 Summary 118
Chapter 7 Data Collection 121
7.1 Equipment Assembly and Workflow 121
7.2 Procedure Worksheet 122
7.3 Electrophysiological System 122
7.3 Digital Thermometer 124
7.4 Accelerometer 124
7.5 Impedance Cardiograph 127
7.6 Clock Synchronisation 128
7.7 Summary 129
Chapter 8 Data Preparation and Pre-processing 130
8.1 Overview 130
8.2 Data Cleaning 130
8.3 Data Integration 131
8.3.1 Time Synchronisation 131
8.3.2 Up-sampling 131
8.4 Data Transformation 132
8.4.1 Clinical History and Examination Data 133
8.4.2 Interference and Far-Field Electrograms 133
7
Table of Contents (continued)

Chapter 8 Data Preparation and Pre-processing (continued)
8.4.3 Power Spectral Analysis 134
8.4.4 Filtering and Differentiation 134
8.5 Data Reduction 135
8.5.1 Clinical History and Examination Data 136
8.5.2 Intracardiac Electrograms – Peaks and Fiducial Points 136
8.5.3 T wave Detection 138
8.5.4 Impedance Cardiogram Wave Detection and Fiducial points 138
8.5.5 Respiration Peak and Trough Detection 139
8.5.6 Temperature and Body Motion Data Reduction 140
8.6 Data Discretisation 141
8.6.1 Clinical History and Examination Data 141
8.6.2 Electrogram Intervals 141
8.6.3 Electrogram Morphology 141
8.6.4 Derivation of Corrected QT interval 142
8.6.5 Haemodynamic Parameter Derivation 143
8.6.6 Respiration Derivatives 143
8.6.7 Temperature 143
8.6.8 Body Motion Data Discretisation 143
8.6.9 Generation of Feature Sets as Classifier Inputs 143
8.7 Summary 144
Chapter 9 Data Collection Results 147
9.1 Overview 147
9.2 Demographics of Patients Studied 147
9.3 Rhythms Detected 147
9.4 Data Quality 150
9.4.1 Data Completeness 150
9.4.2 Data Validity 151
9.4.3 Data Consistency 151
9.4.4 Data Timeliness 151
9.4.5 Data Accuracy 151
9.5 Factors Affecting Data Analysis 151
9.5.1 Dimensionality 152
9.5.2 Data Imbalance 152
9.5.3 Empty Classes 153
9.6 Summary 153
8