Abstract

In this paper, We address the Heart Sound signal modeling problem. The approach taken is based on sparse and redundant representations over an overcomplete dictionary. We apply matching pursuit (MP) and orthogonal matching pursuit (OMP) on two sets of normal and pathological phonocardiograms (PCGs). The methods are based on a classical Gabor wavelet or time-frequency atom which is the product of a sinusoid and a Gaussian window function. The normalized root-mean-square error (NRMSE) was computed between the original and the reconstructed signals. The results show that the OMP method is very suitable to the transient and complex properties of the PCG’s, as it yielded excellent NRMSE’s around 1.61% for normal sounds and 5.19% for pathological signals.