Advanced Methods and Tools for ECG Data Analysis

Transcription

1 Advanced Methods and Tools for ECG Data Analysis Gari D. Clifford Francisco Azuaje Patrick E. McSharry Editors ARTECH HOUSE BOSTON LONDON artechhouse.com

2 Preface XI The Physiological Basis of the Electrocardiogram 1.1 Cellular Processes That Underlie the ECG 1.2 The Physical Basis of Rlectrocardiography The Normal Electrocardiogram 1.3 Introduction to Clinical Rlectrocardiography: Abnormal Patterns The Normal Detcrminants of Heart Rate: The Autonomie Nervous System Ectopy, Tachycardia, and Fibrillation Conduction Blocks, Bradycardia, and Escape Rhythms Cardiac Ischemia, Other Metabolie Disturbances, and Structural Abnormalities A Basic Approach to ECG Analysis 1.4 Summary Refcrences Sclected Bibliography ECG Acquisition, Storage, Transmission, and Representation 2.1 Introduction 2.2 Initial Design Considerations Selecting a Patient Population Data Collection Location and Length Rnergy and Data Transmission Routes Rlectrode Type and Configuration RCG-Related Signals Issues When Collecting Data from Humans 2.3 Choice of Data Libraries 2.4 Database Analysis An Example Using WEDB 2.5 ECG Acquisition Hardware Single-Channel Architecture Isolation and Protection Primary Common-Mode Noise Reduction: Active Grounding Circuit Increasing Input Impedance: CMOS Buffer Stage Preamplification and Isolation v

3 c 2t n i s Highpass Eiltering Secondary Amplification Lowpass Filtering and Oversampling Hardware Design Tssues: Sampling Erequency Choice Hardware Testing, Patient Safety, and Standards Summary 50 Refercnccs 50 ECG Statistics, Noise, Artifacts, and Missing Data Introduction Spectral and Cross-Spectral Analysis of the ECG Extreme Low- and High-Frequency ECG The Spectral Nature of Arrhythmias Standard Clinical ECG Features Nonstationarities in the ECG Heart Rate Hysteresis Arrhythmias Arrhythmia Detection Arrhythmia Classification from Beat Typing Arrhythmia Classification from Power-Frequency Analysis Arrhythmia Classification from Beat-to-Beat Statistics Noise and Artifact in the ECG Noise and Artifact Sources Measuring Noise in the ECG Heart Rate Variability Time Domain and Distribution Statistics Erequency Domain HRV Analysis Long-Term Components The Lomb-Scargle Periodogram Information Limits and Background Noise The Effect of Ectopy and Artifact and How to Deal with 1t Choosing an Experimental Protocol: Activity-Related Changes Dealing with Nonstationarities Nonstationary HRV Metrics and Fractal Scaling Activity-Related Changes Perturbation Analysis Summary 92 References 93 Models for ECG and RR Interval Processes Introduction RR Interval Models The Cardiovascular System The DeBoer Model 104

4 4.2.3 The Research Cardiovascular Simulator Integral Pulse Frequency Modulation Model Nonlinear Dcterministic Models Coupled Osci Ilators and Phase Synchronization Scale Invariance PhysioNet Challenge RR Interval Models for Abnormal Rhythms ECG Models Computational Physiology Synthetic Electrocardiogram Signals Conclusion 126 References 127 CHARTERS Linear Filtering Methods Introduction Wiener Filtering Wavelet Filtering The Continuous Wavelet Transform The Discretc Wavelet Transform and Filter Banks A Denoising Example: Wavelet Choice Data-Determined Basis Functions Principal Component Analysis Neural Network Filtering Independent Component Analysis for Source Separation and Filtering Summary and Conclusions 167 References 167 Nonlinear Filtering Techniques Introduction Nonlinear Signal Processing State Space Reconstruction Lyapunov Exponents Gorrelation Dimension Entropy Nonlinear Diagnostic« Evaluation Metrics Empirical Nonlinear Filtering Nonlinear Noise Reduction State Space Independent Component Analysis Comparison of NNR and ICA Model-Based Filtering Nonlinear Model Parameter Estimation State Space Model-Based Filtering 191

5 6.6 Conclusion 193 References 194 The Pathophysiology Guided Assessment of T-Wave Alternans Introduction Phenomenology of T-Wave Alternans Pathophysiology of T-Wave Alternans Mcasurable Indices of ECG T-Wave Alternans Measurement Techniques Requirements for the Digitized ECG Signal Short-Term. Fourier Transform-Based Methods Interpretation of Spectral TWA Test Results Controversies of the STET Approach Sign-Change Counting Methods Nonlinear Filtering Methods Tailoring Analysis of TWA to Its Pathophysiology Current Approaches for Elicitmg TWA Steady-State Rhythms and Stationary TWA Fluctuating Heart Rates and Nonstationary TWA Rhythm Discontinuities, Nonstationary TWA, and TWA Phase Conclusions 211 Acknowledgments 211 References 211 ECG-Derived Respiratory Frequency Estimation Introduction EDR Algorithms Based on Beat Morphology Amplitude EDR Algorithms Multilead QRS Area EDR Algorithm QRS-VCG Loop Alignment EDR Algorithm EDR Algorithms Based on HR Information EDR Algorithms Based on Both Beat Morphology and HR Estimation of the Respiratory Frequency Nonparametric Approach Parametric Approach Signal Modeling Approach Evaluation Conclusions 240 References 241 Appendix 8A Vectorcardiogram Synthesis from the 12-Eead ECG 243 imläft Introduction to Feature Extraction \ Ovcrview of Feature Extraction Phases Prcprocessing 248

6 9.3 Derivation of Diagnostic and Morphologie Feature Vectors Derivation of Orthonormal Function Model Transform-Based Morphology Feature Vectors Derivation of Time-Domain Diagnostic and Morphologie Feature Vectors Shape Representation, in Terms of Feature-Vcctor Time Series 260 References 263 Appendix 9A Description of the Karhunen-Loeve Transform 264 ST Analysis ST Segment Analysis: Perspectives and Goals Ovcrview of ST Segment Analysis Approaches Detection of Transient ST Change Episodes Reference Databases Correction of Reference ST Segment Level Procedure to Detect ST Change Episodes Performance Evaluation of ST Analyzers Performance Measures Comparison of Performance of ST Analyzers Assessing Robustness of ST Analyzers 286 References 287 Probabilistic Approaches to ECG Segmentation and Feature Extraction Introduction The Rlcctrocardiogram The ECG Waveform ECG Tnterval Analysis Manual ECG Interval Analysis Automated ECG Interval Analysis The Probabilistic Modeling Approach Data Collection Introduction to Hidden Markov Modeling Overview Stochastic Processes and Markov Models Hidden Markov Models ,6.4 Inference in HMMs ,6.5 Learning in HMMs Hidden Markov Models for ECG Segmentation Overview ECG Signal Normalization Types of Model Segmentati ons Performance Evaluation Wavelet Encoding of the ECG Wavelet Transforms HMMs with Wavelet-Encoded ECG 312

7 11.9 Duration Modeling for Robust Segmentations Conclusions 316 References 316 CHAPTER12 Supervised Learning Methods for ECG Classification/Neural Networks and SVM Approaches lntroduction Generation of Features Hermite Basis Function Expansion HOS Features of the ECG Supervised Neural Classifiers Multilayer Perceptron Hybrid Fuzzy Network TSK Neuro-Fuzzy Network Support Vector Machine Classifiers Integration of Multiple Classifiers Results of Numerical Experiments Conclusions 336 Acknowledgments 336 References 336 An lntroduction to Unsupervised Learning for ECG Classification lntroduction Basic Concepts and Methodologies Unsupervised Learning Techniques and Their Applications in ECG Classification Hierarchical Clustering Ä-Means Clustering SOM Application of Unsupervised Learning in ECG Classification Advances in Clustering-Based Techniques Evaluation of Unsupervised Classification Models: Cluster Validity and Significance GSOM-ßased Approaches to ECG Cluster Discovery and Visualization TheGSOM Application of GSOM-Based Techniques to Support ECG Classification Final Remarks 359 References 362 About the Authors 367 Index 371