Implantable cardioverter defibrillator electrogram recognition with a multilayer perceptron.

Abstract

With the continuing development of new pacing and shock modalities, implantable cardioverter defibrillators must be able to recognize an increasingly wider range of arrhythmias so that therapy is delivered in an optimal way. Current rate-based systems can no longer meet this need. It is, therefore, necessary to develop new techniques that consider waveform morphology in addition to heart rate. Any developments, however, must be compatible with the strict power and space constraints imposed by implantable devices. Artificial neural networks offer a potentially viable way of meeting these objectives. In this article an artificial neural network based approach to the classification of arrhythmias from the ventricular intracardiac electrogram is described. It performs its classification on a set of easily extracted features that characterize waveform morphology and heart rate. Simulations showed that the artificial neural network performed better than a rate-based scheme similar to those used in some commercial devices.