This cutting-edge resource provides you with a practical and theoretical understanding of state-of-the-art techniques for electrocardiogram (ECG) data analysis. Placing an emphasis on the fundamentals of signal etiology, acquisition, data selection, and testing, this comprehensive volume presents guidelines to help you design, implement, and evaluate algorithms used for the analysis of ECG and related data. Additionally, explanations of open source software and related databases for signal processing are given.
The book focuses on the modeling, classification, and interpretation of features derived from advanced signal processing and artificial intelligence techniques. Key topics covered include physiological origin, hardware acquisition and filtering, time-frequency quantification of the ECG and derived signals (including heart rate variability and respiration), analysis of noise and artifact, models for ECG and RR interval processes, linear and nonlinear filtering techniques, and adaptive algorithms such as neural networks.
Much of the book is devoted to deriving robust, clinically meaningful parameters such as the QRS axis, QT-interval, the ST-level, and T-wave alternan metrics. Methods for applying these metrics to clinical classification are also discussed, together with supervised and unsupervised classification techniques. Including over 190 illustrations, the book offers you 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.
Table of Contents:
Introduction ?Introduction to Physiological Basis and Clinical Interpretation of ECG. Introduction to ECG Information Acquisition, Representation and Storage. Advanced Signal Processing and Artificial Intelligence for ECG Data Analysis.
Mathematical Characterization of the ECG and Its Contaminants ?Noise/Signal/Artifact Comparision, Characterizing Unwanted? Signals Through Measures of Stationarity, Gaussianity, Nonlinearity, and Color. Long Term Trends (Circadian Rhythms, Segmentation, Nonstationary Shifts).
Filtering, Compression, Decompression, and Interpolation ? Linearity and Stationary Filtering and Compression, Resampling, Interpolation, and Wavelets. Multidimensional Filtering. Nonlinear Projective Filtering.
Feature Extraction ?Feature Extraction from ECG. Temporal Feature Extraction ECG-Derived Respiration and Heart Rate Variability.
Supervised and Unsupervised Classification ‑ Introduction to Linear Supervised Learning. Supervised Neural Networks. Support Vector Machine Methods. Clustering-Based Analysis Methods. Unsupervised Learning Methods for Supporting Pattern Discovery and Interpretation. Hybrid Intelligent Classification Techniques.
Visualization Methods, Knowledge Management and Emerging Methods ?Methods for Displaying ECG Information and Analysis Outcomes. Methods for Automatically Describing and Evaluating ECG Data Clusters and Classes. Introduction to Causal Reasoning.