An Introduction to Unsupervised Learning for ECG Classification - Chapter 13

H. Wang & F. Azuaje

This page provides supplementary information and relevant links for Chapter 13 in Advanced Methods for ECG Analysis, which is co-edited by Francisco Azuaje and Patrick McSharry, and is published by Artech House. The main URL for this book can be found here, together with ordering information. Much of the software associated with this book can be found here.

 

 

Links:

• Netlab -- A free set of pattern recognition & neural network tools in Matlab by Ian Nabney. the associated book is simply wonderful. 
• ECGtools - A selection of ECG analysis tools in Matlab including a self-explaining QRS detector, filtering tools and HRV analysis algorithms. Includes FIR, SVD, ICA and Wiener filtering code. 

Contents:

13.1 Introduction

 

13.2 Basic Concepts and Methodologies

 

13.3 Unsupervised Learning Techniques and their Applications in ECG Classification

13.3.1 Hierarchical Clustering

13.3.2 k-Means Clustering

13.3.3 SOM

13.3.4 Application of Unsupervised Learning in ECG Classification

13.3.5 Advances in Clustering-Based Techniques

13.3.6 Evaluation of Unsupervised Classification Models: Cluster Validity and Significance

13.4 GSOM-based Approaches to ECG Cluster Discovery and Visualization

13.4.1 The GSOM

13.4.2 Application of GSOM-Based Techniques to Support ECG Classification

13.5 Final Remarks