Recognition of heart sounds and murmurs for cardiac diagnosis

A pattern recognition system, able to classify the heart sounds and murmurs perfectly for cardiac diagnosis is presented. A mathematical model to describe the heart sounds and murmurs (HSAM) by a finite number of parameters was developed. The autoregressive model (AR) is selected to represent the HSAM at principal locations of cardiac auscultation and for different heart diseases. Feature extraction of the pre-emphasized signal, based on fourth-order linear prediction of the cardiac cycle frames, is performed. Pattern classification, based on an optimal dynamic time warping algorithm that minimizes the Euclidean distance between the features of the measured pattern and reference patterns, is verified. A decision, based on a minimum-distance criterion is then made. Furthermore, a bank consisting of 20 reference types of cardiac diseases has been generated