Abstract

In this paper, a novel framework based on a time-frequency (TF) approach is proposed for detection of murmurs from heart sound signal. First, a high-resolution TF algorithm, matching pursuit, was used to decompose each heart beat into a series of TF atoms selected from a redundant dictionary. Next, representative components of murmurs were iden-tified by clustering the selected atoms of all the beats into a finite number of clusters. Then, Wigner-Ville distribution of the representative components was used to generate a set of 8 features which were fed to a classifier. Experiments with a dataset consisting of heart sounds from 35 normal and 35 pathological subjects showed a classification accuracy of 95.71% in distinguishing murmurs from normal heart sounds.