Advanced methods and tools for ECG data analysis:
Saved in:
| Other Authors: | |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Boston, Mass. <<[u.a.]>>
Artech House
2006
|
| Series: | Engineering in medicine & biology
|
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Item Description: | Includes bibliographical references and index |
| Physical Description: | XV, 384 S. Ill., graph. Darst. |
| ISBN: | 9781580539661 1580539661 |
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MARC
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| 245 | 1 | 0 | |a Advanced methods and tools for ECG data analysis |c Gari D. Clifford ..., ed. |
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Record in the Search Index
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| adam_text | Contents
Preface xi
The Physiological Basis of the Electrocardiogram 1
1.1 Cellular Processes That Underlie the ECG 1
1.2 The Physical Basis of Electrocardiography 4
1.2.1 The Normal Electrocardiogram 6
1.3 Introduction to Clinical Electrocardiography: Abnormal Patterns 12
1.3.1 The Normal Determinants of Heart Rate: The Autonomic
Nervous System 12
1.3.2 Ectopy, Tachycardia, and Fibrillation 15
1.3.3 Conduction Blocks, Bradycardia, and Escape Rhythms 18
1.3.4 Cardiac Ischemia, Other Metabolic Disturbances,
and Structural Abnormalities 20
1.3.5 A Basic Approach to ECG Analysis 23
1.4 Summary 24
References 24
Selected Bibliography 25
ECG Acquisition, Storage, Transmission, and Representation 27
2.1 Introduction 27
2.2 Initial Design Considerations 28
2.2.1 Selecting a Patient Population 28
2.2.2 Data Collection Location and Length 29
2.2.3 Energy and Data Transmission Routes 30
2.2.4 Electrode Type and Configuration 30
2.2.5 ECG-Related Signals 32
2.2.6 Issues When Collecting Data from Humans 33
2.3 Choice of Data Libraries 35
2.4 Database Analysis—An Example Using WFDB 37
2.5 ECG Acquisition Hardware 41
2.5.1 Single-Channel Architecture 41
2.5.2 Isolation and Protection 42
2.5.3 Primary Common-Mode Noise Reduction: Active Grounding
Circuit 43
2.5.4 Increasing Input Impedance: CMOS Buffer Stage 43
2.5.5 Preamplification and Isolation 44
v
2.5.6 Highpass Filtering 45
2.5.7 Secondary Amplification 45
2.5.8 Lowpass Filtering and Oversampling 46
2.5.9 Hardware Design Issues: Sampling Frequency Choice 47
2.5.10 Hardware Testing, Patient Safety, and Standards 48
2.6 Summary 50
References 50
ECG Statistics, Noise, Artifacts, and Missing Data 55
3.1 Introduction 55
3.2 Spectral and Cross-Spectral Analysis of the ECG 55
3.2.1 Extreme Low-and High-Frequency ECG 57
3.2.2 The Spectral Nature of Arrhythmias 57
3.3 Standard Clinical ECG Features 60
3.4 Nonstationarities in the ECG 64
3.4.1 Heart Rate Hysteresis 65
3.4.2 Arrhythmias 66
3.5 Arrhythmia Detection 67
3.5.1 Arrhythmia Classification from Beat Typing 68
3.5.2 Arrhythmia Classification from Power-Frequency Analysis 68
3.5.3 Arrhythmia Classification from Beat-to-Beat Statistics 68
3.6 Noise and Artifact in the ECG 69
3.6.1 Noise and Artifact Sources 69
3.6.2 Measuring Noise in the ECG 70
3.7 Heart Rate Variability 71
3.7.1 Time Domain and Distribution Statistics 73
3.7.2 Frequency Domain HRV Analysis 74
3.7.3 Long-Term Components 74
3.7.4 The Lomb-Scargle Periodogram 77
3.7.5 Information Limits and Background Noise 79
3.7.6 The Effect of Ectopy and Artifact and How to Deal with It 80
3.7.7 Choosing an Experimental Protocol: Activity-Related Changes 81
3.8 Dealing with Nonstationarities 83
3.8.1 Nonstationary HRV Metrics and Fractal Scaling 84
3.8.2 Activity-Related Changes 86
3.8.3 Perturbation Analysis 89
3.9 Summary 92
References 93
Models for ECG and RR Interval Processes 101
4.1 Introduction 101
4.2 RR Interval Models 102
4.2.1 The Cardiovascular System 102
4.2.2 The DeBoer Model 104
4.2.3 The Research Cardiovascular Simulator 106
4.2.4 Integral Pulse Frequency Modulation Model 108
4.2.5 Nonlinear Deterministic Models 109
4.2.6 Coupled Oscillators and Phase Synchronization 110
4.2.7 Scale Invariance 111
4.2.8 PhysioNet Challenge 113
4.2.9 RR Interval Models for Abnormal Rhythms 114
4.3 ECG Models 115
4.3.1 Computational Physiology 116
4.3.2 Synthetic Electrocardiogram Signals 117
4.4 Conclusion 126
References 127
Linear Filtering Methods 135
5.1 Introduction 135
5.2 Wiener Filtering 136
5.3 Wavelet Filtering 140
5.3.1 The Continuous Wavelet Transform 141
5.3.2 The Discrete Wavelet Transform and Filter Banks 142
5.3.3 A Denoising Example: Wavelet Choice 144
5.4 Data-Determined Basis Functions 148
5.4.1 Principal Component Analysis 149
5.4.2 Neural Network Filtering 151
5.4.3 Independent Component Analysis for Source Separation
and Filtering 157
5.5 Summary and Conclusions 167
References 167
Nonlinear Filtering Techniques 171
6.1 Introduction 171
6.2 Nonlinear Signal Processing 172
6.2.1 State Space Reconstruction 173
6.2.2 Lyapunov Exponents 173
6.2.3 Correlation Dimension 174
6.2.4 Entropy 176
6.2.5 Nonlinear Diagnostics 177
6.3 Evaluation Metrics 178
6.4 Empirical Nonlinear Filtering 179
6.4.1 Nonlinear Noise Reduction 179
6.4.2 State Space Independent Component Analysis 181
6.4.3 Comparison of NNR and ICA 182
6.5 Model-Based Filtering 186
6.5.1 Nonlinear Model Parameter Estimation 188
6.5.2 State Space Model-Based Filtering 191
6.6 Conclusion 193
References 194
The Pathophysiology Guided Assessment of T-Wave Alternans 197
7.1 Introduction 197
7.2 Phenomenology of T-Wave Alternans 197
7.3 Pathophysiology of T-Wave Alternans 197
7.4 Measurable Indices of ECG T-Wave Alternans 199
7.5 Measurement Techniques 201
7.5.1 Requirements for the Digitized ECG Signal 201
7.5.2 Short-Term Fourier Transform-Based Methods 202
7.5.3 Interpretation of Spectral TWA Test Results 203
7.5.4 Controversies of the STFT Approach 204
7.5.5 Sign-Change Counting Methods 205
7.5.6 Nonlinear Filtering Methods 206
7.6 Tailoring Analysis of TWA to Its Pathophysiology 207
7.6.1 Current Approaches for Eliciting TWA 208
7.6.2 Steady-State Rhythms and Stationary TWA 208
7.6.3 Fluctuating Heart Rates and Nonstationary TWA 209
7.6.4 Rhythm Discontinuities, Nonstationary TWA, and TWA Phase 210
7.7 Conclusions 211
Acknowledgments 211
References 211
ECG-Derived Respiratory Frequency Estimation 215
8.1 Introduction 215
8.2 EDR Algorithms Based on Beat Morphology 218
8.2.1 Amplitude EDR Algorithms 220
8.2.2 Multilead QRS Area EDR Algorithm 222
8.2.3 QRS-VCG Loop Alignment EDR Algorithm 224
8.3 EDR Algorithms Based on HR Information 228
8.4 EDR Algorithms Based on Both Beat Morphology and HR 229
8.5 Estimation of the Respiratory Frequency 230
8.5.1 Nonparametric Approach 230
8.5.2 Parametric Approach 232
8.5.3 Signal Modeling Approach 234
8.6 Evaluation 236
8.7 Conclusions 240
References 241
Appendix 8A Vectorcardiogram Synthesis from the 12-Lead ECG 243
Introduction to Feature Extraction 245
9.1 Overview of Feature Extraction Phases 245
9.2 Preprocessing 248
9.3 Derivation of Diagnostic and Morphologic Feature Vectors 251
9.3.1 Derivation of Orthonormal Function Model Transform-Based
Morphology Feature Vectors 251
9.3.2 Derivation of Time-Domain Diagnostic and Morphologic
Feature Vectors 257
9.4 Shape Representation in Terms of Feature-Vector Time Series 260
References 263
Appendix 9A Description of the Karhunen-Loeve Transform 264
ST Analysis 269
10.1 ST Segment Analysis: Perspectives and Goals 269
10.2 Overview of ST Segment Analysis Approaches 270
10.3 Detection of Transient ST Change Episodes 272
10.3.1 Reference Databases 273
10.3.2 Correction of Reference ST Segment Level 274
10.3.3 Procedure to Detect ST Change Episodes 275
10.4 Performance Evaluation of ST Analyzers 278
10.4.1 Performance Measures 278
10.4.2 Comparison of Performance of ST Analyzers 284
10.4.3 Assessing Robustness of ST Analyzers 286
References 287
Probabilistic Approaches to ECG Segmentation and Feature Extraction 291
11.1 Introduction 291
11.2 The Electrocardiogram 292
11.2.1 The ECG Waveform 292
11.2.2 ECG Interval Analysis 292
11.2.3 Manual ECG Interval Analysis 293
11.3 Automated ECG Interval Analysis 293
11.4 The Probabilistic Modeling Approach 294
11.5 Data Collection 296
11.6 Introduction to Hidden Markov Modeling 296
11.6.1 Overview 296
11.6.2 Stochastic Processes and Markov Models 297
11.6.3 Hidden Markov Models 298
11.6.4 Inference in HMMs 301
11.6.5 Learning in HMMs 302
11.7 Hidden Markov Models for ECG Segmentation 304
11.7.1 Overview 305
11.7.2 ECG Signal Normalization 305
11.7.3 Types of Model Segmentations 307
11.7.4 Performance Evaluation 307
11.8 Wavelet Encoding of the ECG 311
11.8.1 Wavelet Transforms 311
11.8.2 HMMs with Wavelet-Encoded ECG 312
11.9 Duration Modeling for Robust Segmentations 312
11.10 Conclusions 316
References 316
Supervised Learning Methods for ECG Classification/Neural Networks
and SVM Approaches 319
12.1 Introduction 319
12.2 Generation of Features 320
12.2.1 Hermite Basis Function Expansion 321
12.2.2 HOS Features of the ECG 323
12.3 Supervised Neural Classifiers 324
12.3.1 Multilayer Perceptron 325
12.3.2 Hybrid Fuzzy Network 326
12.3.3 TSK Neuro-Fuzzy Network 327
12.3.4 Support Vector Machine Classifiers 328
12.4 Integration of Multiple Classifiers 330
12.5 Results of Numerical Experiments 331
12.6 Conclusions 336
Acknowledgments 336
References 336
An Introduction to Unsupervised Learning for ECG Classification 339
13.1 Introduction 339
13.2 Basic Concepts and Methodologies 339
13.3 Unsupervised Learning Techniques and Their Applications in ECG
Classification 341
13.3.1 Hierarchical Clustering 342
13.3.2 -Means Clustering 343
13.3.3 SOM 343
13.3.4 Application of Unsupervised Learning in ECG Classification 346
13.3.5 Advances in Clustering-Based Techniques 347
13.3.6 Evaluation of Unsupervised Classification Models: Cluster
Validity and Significance 350
13.4 GSOM-Based Approaches to ECG Cluster Discovery and
Visualization 352
13.4.1 The GSOM 352
13.4.2 Application of GSOM-Based Techniques to Support ECG
Classification 354
13.5 Final Remarks 359
References 362
About the Authors 367
Index 371
|
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| spelling | Advanced methods and tools for ECG data analysis Gari D. Clifford ..., ed. Boston, Mass. <<[u.a.]>> Artech House 2006 XV, 384 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Engineering in medicine & biology Includes bibliographical references and index Elektrokardiogramm (DE-588)4070750-7 gnd rswk-swf Methode (DE-588)4038971-6 gnd rswk-swf Datenanalyse (DE-588)4123037-1 gnd rswk-swf Hilfsprogramm (DE-588)4231946-8 gnd rswk-swf Elektrokardiogramm (DE-588)4070750-7 s Datenanalyse (DE-588)4123037-1 s Hilfsprogramm (DE-588)4231946-8 s Methode (DE-588)4038971-6 s DE-604 Clifford, Gari D. edt HBZ Datenaustausch application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=018588109&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Advanced methods and tools for ECG data analysis Elektrokardiogramm (DE-588)4070750-7 gnd Methode (DE-588)4038971-6 gnd Datenanalyse (DE-588)4123037-1 gnd Hilfsprogramm (DE-588)4231946-8 gnd |
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| title | Advanced methods and tools for ECG data analysis |
| title_auth | Advanced methods and tools for ECG data analysis |
| title_exact_search | Advanced methods and tools for ECG data analysis |
| title_full | Advanced methods and tools for ECG data analysis Gari D. Clifford ..., ed. |
| title_fullStr | Advanced methods and tools for ECG data analysis Gari D. Clifford ..., ed. |
| title_full_unstemmed | Advanced methods and tools for ECG data analysis Gari D. Clifford ..., ed. |
| title_short | Advanced methods and tools for ECG data analysis |
| title_sort | advanced methods and tools for ecg data analysis |
| topic | Elektrokardiogramm (DE-588)4070750-7 gnd Methode (DE-588)4038971-6 gnd Datenanalyse (DE-588)4123037-1 gnd Hilfsprogramm (DE-588)4231946-8 gnd |
| topic_facet | Elektrokardiogramm Methode Datenanalyse Hilfsprogramm |
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