ECG Beat Classification By A Neural Network

Abstract

A neural network architecture that can be trained to classify e.c.g. is presented. It uses a feature extractor to characterize the e.c.g. before the presentation to a back-propagation network for classification. ne test results indicate that network may accurately classify most sample tapes in AHA database after it has ken trained on only four sample tapes.