Evaluation of Perceptual and Multi Sub-Band Energy Features for Classification of Normal, Pathological and Noisy Phonocardiogram

Abstract

Murmurs in the heart sound signal convey crucial diagnostic information on valvular defects. It is a significant challenge to identify murmurs in heart sound signals due to its narrow bandwidth and the short time duration. In the clinical examination, these sounds are categorized based on human auditory perception. In this paper, we explore the popular perceptual features mel frequency cepstral coefficients (MFCC) and rhythm pattern (RP), and various multiple sub-band energy (MSBE) features for evaluation and quantification of murmurs and noises. After a comprehensive analysis of MFCC at a different number of coefficient values, it is observed that four MFCC coefficients are optimal and more discriminative than other conventional features. This is further compared with the proposed MSBE feature. The proposed feature performs better achieving an average M Acc score of 68.27%, 79.83% and 94.71% for respective classes. M Acc is the mean value of sensitivity and specificity. The estimation of sub-band energy values from log magnitude spectrogram and across larger frequency intervals improve the quantification of low amplitude high-frequency components introduced into the heart sound signals.