Phonocardiogram Signals Segmentation by Using the Hidden Markov Models (HMM)

Heart sounds and murmurs provide crucial diagnosis information for several heart diseases such as natural or prosthetic valve dysfunction and heart failure. Many pathological conditions of the cardiovascular system cause murmurs and aberrations in heart sounds. Phonocardiography provides the clinician with a complementary tool to record the heart sounds heard during auscultation. The advancement of intra cardiac phonocardiography, combined with modern digital processing techniques, has strongly renewed researchers’ interest in studying heart sounds and murmurs. This paper presents an algorithm for the detection of heart sounds (the first and second sounds, S1 and S2) and heart murmurs. This paper is concerned to the segmentation of heart sounds by using state of art Hidden Markov Models (HMW) technology which used to extract a smooth envelogram which enable us to apply the tests necessary for temporal localization of heart sounds and heart murmurs. In the scope of this segmentation difficulty the well-known non-stationary statistical properties of Hidden Markov Models (HMW) concerned to temporal signal segmentation capabilities can be adequate to deal with this kind of segmentation problems.