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.
- 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.
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
13.3.4 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