Automatic Classification of Continuous Heart Sound Signals Using the Statistical Modeling Approach

Conventional research works on the classification of the heart sound signal have been done mainly with the artificial neural networks. But the analysis results on the statistical characteristic of the heart sound signal have shown that the HMM is suitable for modeling the heart sound signal. In this paper, we model the various heart sound signals representing different heart diseases with the HMM and find that the classification rate is much affected by the clustering of the heart sound signal. Also, the heart sound signal acquired in real environments is a continuous signal without any specified starting and ending points of time. Hence, for the classification based on the HMM, the continuous cyclic heart sound signal needs to be manually segmented to obtain isolated cycles of the signal. As the manual segmentation will incur the errors in the segmentation and will not be adequate for real time processing, we propose a variant of the ergodic HMM which does not need segmentation procedures. Simulation results show that the proposed method successfully classifies continuous heart sounds with high accuracy.