Analysis of Heart Sound Signals using Sparse Modeling with Gabor Dictionary

We propose applying sparse modeling using matching pursuit with a Gabor dictionary of scaled time-frequency atoms to analyze and approximate the heart sound signals of the PhysioNet2016 challenge. The time and frequency resolution of the dictionary is governed by a scaling factor, and the atoms temporally overlap by 50% of their temporal duration. We aim to optimize the time and frequency resolution of the dictionary to achieve the highest approximation quality at the desired sparsity. We also investigate the approximation quality of matching pursuit with a reduced size dictionary constructed from either overlapping or nonoverlapping atoms with downsampled frequency resolution. Experimental analysis shows that a dictionary of balanced time and frequency resolution performs well for highly sparse approximations of the signals. Moreover, the highest approximation quality is obtained when the approximation become less sparse, with a dictionary containing atoms of high time and low frequency resolution. On the other hand, a half-size dictionary of overlapping atoms with downsampled frequency resolution performs well in comparison to the original full-size dictionary. Furthermore, the approximation quality is highly degraded when the dictionary contains nonoverlapping atoms rather than atoms with downsampled frequency resolution.