Investigation on Elman neural network for detection of cardiomyopathy

Deterioration of structure and function of heart muscle is indicative of a degenerative disease known as cardiomyopathy. As a result, the hypertrophic condition of the heart often revealed itself in the form of abnormal sinus rhythm that can be detected via an electrocardiogram (ECG). In order to reduce the risk of misinterpretation by cardiologists, a variety of computational methods have been suggested for automated classification of arrhythmias. This paper proposes to explore Elman neural network for detecting cardiomyopathy. A total of 600 ECG beat samples were acquired from an established online database. Initially, the signals were filtered to eliminate high-frequency interference and perform baseline rectification. Nine time-based descriptors from leads I, II and III were used for training, testing and validation of the network structures. A total of five hidden-node node structures were tested with four different learning algorithms. Results show that all the network structure managed to achieve more than 90% classification accuracy. The fastest convergence was achieved with the Levenberg-Marquardt algorithm with an average of 16 epochs.