Identification of cardiomyopathy disease using hybrid multilayered perceptron network

Abstract

Cardiomyopathy is a progressive disease that affects the muscular walls of the heart. The resultant hypertrophic condition of the cardiac chambers alters the capability of the heart to contract which will then lead to deterioration of cardiac output. The abnormality can manifest itself in the form of an arrhythmic signal detectable by electrocardiogram (ECG). Hence, this paper proposes the hybrid multilayered perceptron (HMLP) network for identification of cardiomyopathy disease. Initially, raw signals were acquired from the PTB Diagnostic ECG database for healthy, cardiomyopathy and other arrhythmias. The ECG underwent a signal preprocessing stage for noise reduction and baseline correction. Then, nine time-based sub-wave descriptors from the bipolar limb leads were retrieved via the median threshold approach. 600 beat samples were then utilized to train, test and validate the performance of the HMLP network. The HMLP network structures were tested for five variations of hidden nodes with four different learning algorithms. Findings indicate that the best convergence rate and detection accuracy are achievable with the Levenberg-Marquardt algorithm. Hence, the results suggest the potential application of HMLP for classification of arrhythmias.