Classification and diagnosis of heart sounds and murmurs using artificial neural networks

Abstract

Cardiac auscultation still remains today as the basic technique to easily achieve a cardiac valvular diagnosis. Nowadays, auscultation can be powered with automated computer-aided analysis systems to provide objective, accurate, documented and cost-effective diagnosis. This is particulary useful when such systems offer remote diagnosis capabilities. ASEPTIC is a telediagnosis system for cardiac sounds that allows the analysis of remote phonocardiographic signals. The pattern recognition stage of ASEPTIC is presented in this paper. It is based in feature selection from the cardiac events, and classification using a multilayer perceptron artificial neural network trained with Levenberg-Marquardt algorithm for fast convergence. Three categories of records have been considered: normal, with holosystolic murmur, and with midsystolic murmur. Experimental results show high correct classification rates for the three categories: 100%, 92.69%, and 97.57%, respectively. © Springer-Verlag Berlin Heidelberg 2007.