Clifford G.D., Azuaje F., McSharry P. Advanced Methods And Tools for ECG Data Analysis

N.-Y.: Artech House Publishers, 2006. — 400 p.The electrocardigram (ECG) is a recording of the electrical activity of the heart that is used to diagnose heart disorders. In recent years, new state-of-the-art approaches to ECG analysis have emerged that are now of significant interest to biomedical and electrical engineers, as well as healthcare professionals. This practical book is the first one-stop resource to offer a thorough, up-to-date treatment of the techniques and methods used in ECG data analysis, from fundamental principles to the latest tools in the field. The book places emphasis on the selection, modeling, classification, and interpretation of data based on advanced signal processing and artificial intelligence techniques. Professionals find guidance on designing, implementing, and evaluating software systems used for the analysis of ECG and related data. Moreover, this comprehensive resource offers a solid grounding in the relevant basics of physiology, data acquisition and database design, and addresses the practical issues of improving existing data analysis methods and developing new applications.Cellular Processes That Underlie the ECG
The Physical Basis of Electrocardiography
Introduction to Clinical Electrocardiography: Abnormal PatternsSelected BibliographyInitial Design Considerations
Choice of Data Libraries
Database Analysis-An Example Using WFDB
ECG Acquisition HardwareSpectral and Cross-Spectral Analysis of the ECG
Standard Clinical ECG Features
Nonstationarities in the ECG
Arrhythmia Detection
Noise and Artifact in the ECG
Heart Rate Variability
Dealing with NonstationaritiesRR Interval Models
ECG ModelsWiener Filtering
Wavelet Filtering
Data-Determined Basis FunctionsNonlinear Signal Processing
Evaluation Metrics
Empirical Nonlinear Filtering
Model-Based FilteringPathophysiology of T-Wave Alternans
Measurable Indices of ECG T-Wave Alternans
Measurement Techniques
Tailoring Analysis of TWA to Its PathophysiologyEDR Algorithms Based on Beat Morphology
EDR Algorithms Based on HR Information
EDR Algorithms Based on Both Beat Morphology and HR
Estimation of the Respiratory Frequency
EvaluationAppendix A Vectorcardiogram Synthesis from the -Lead ECGOverview of Feature Extraction Phases
Preprocessing
Derivation of Diagnostic and Morphologic Feature Vectors
Shape Representation in Terms of Feature-Vector Time SeriesAppendix A Description of the Karhunen-Lo`eve Transform
ST Segment Analysis: Perspectives and Goals
Overview of ST Segment Analysis Approaches
Detection of Transient ST Change Episodes
Performance Evaluation of ST AnalyzersThe Electrocardiogram
Automated ECG Interval Analysis
The Probabilistic Modeling Approach
Introduction to Hidden Markov Modeling
Hidden Markov Models for ECG Segmentation
Wavelet Encoding of the ECG
Duration Modeling for Robust SegmentationsGeneration of Features
Supervised Neural Classifiers
Integration of Multiple Classifiers
Results of Numerical ExperimentsBasic Concepts and Methodologies
Unsupervised Learning Techniques and Their Applications in ECG Classification
GSOM-Based Approaches to ECG Cluster Discovery and Visualization
Final Remarks