Noise Robust Detection of Fundamental Heart Sound using Parametric Mixture Gaussian and Dynamic Programming.

In this work, we propose an unsupervised algorithm for fundamental heart sound detection. We propose to detect the heart sound candidates using the stationary wavelet transforms and group delay. We further propose an objective function to select the candidates. The objective function has two parts. We model the energy contour of S1/S2 sound using the Gaussian mixture function (GMF). The goodness of fit for the GMF is used as the first part of the objective function. The second part of the objective function captures the consistency of the heart sounds' relative location. We solve the objective function efficiently using dynamic programming. We evaluate the algorithm on Michigan HeartSound and Murmur database. We also assess the algorithm's performance using the three different additive noises- white Gaussian noise (AWGN), Student-t noise, and impulsive noise. The experiments demonstrate that the proposed method performs better than baseline in both clean and noisy conditions. We found that the proposed method is robust in the case of AWGN noise and student-t distribution noise. But its performance reduces in case of impulsive noise.