Advanced Methods and Tools for ECG Data Analysis

By Patrick McSharry (eds)

Advanced Methods and Tools for ECG Data Analysis

Emphasizing the selection, modeling, classification, and interpretation of data based on advanced signal processing and AI techniques, this one-stop guide offers thorough, up-to-date coverage of the techniques and methods used in ECG data analysis.

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Table of Contents

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Advanced Methods and Tools for ECG Data Analysis

Chapter 1: The Physiological Basis of the Electrocardiogram

Chapter 2: ECG Acquisition, Storage, Transmission, and Representation

Chapter 3: ECG Statistics, Noise, Artifacts, and Missing Data

3.2: Spectral and Cross-Spectral Analysis of the ECG

3.3: Standard Clinical ECG Features

3.4: Nonstationarities in the ECG

3.5: Arrhythmia Detection

3.6: Noise and Artifact in the ECG

3.7: Heart Rate Variability

3.8: Dealing with Nonstationarities

3.9: Summary

References

Chapter 4: Models for ECG and RR Interval Processes

Chapter 5: Linear Filtering Methods

Chapter 6: Nonlinear Filtering Techniques

Chapter 7: The Pathophysiology Guided Assessment of T-Wave Alternans

Chapter 8: ECG-Derived Respiratory Frequency Estimation

Chapter 9: Introduction to Feature Extraction

Chapter 10: ST Analysis

Chapter 11: Probabilistic Approaches to ECG Segmentation and Feature Extraction

Chapter 12: Supervised Learning Methods for ECG Classification/Neural Networks and SVM Approaches

Chapter 13: An Introduction to Unsupervised Learning for ECG Classification

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