The Role of Neural Networks in Computerized Classification of the Electrocardiogram

Abstract

Electrical forces generated by the heart are transmitted to the skin through the body’s tissues. These forces can be recorded on the body’s surface and are represented as an electrocardiogram (ECG). The ECG can be used to detect many cardiac abnormalities. Traditionally, ECG classification algorithms have used rule based techniques in an effort to model the thought and reasoning process of the human expert. However, the definition of an ultimate rule set for cardiac diagnosis has remained somewhat elusive, and much research effort has been directed at data driven techniques. Neural networks have emerged as a strong contender as the highly non-linear and chaotic nature of the ECG represents a well-suited application for this technique. This study presents an overview of the application of neural networks in the field of ECG classification, and, in addition, some preliminary results of adaptations of conventional neural classifiers are presented. From this work, it is possible to highlight issues that will affect the acceptance of this technique and, in addition, identify challenges faced for the future. The challenges can be found in the intelligent processing of larger amounts of ECG information which may be generated from recording techniques such as body surface potential mapping.