HEART SOUNDS SEGMENTATION AND CLASSIFICATION

Abstract

Heart valves, responsible for correct pumping of blood to entire body generate certain sounds during its functionality called as heart sounds. Listening and interpretation of these sounds using stethoscope is known as auscultation. Heart sounds commonly known as phonocardiogram signal provide valuable information for correct identification of any heart disease, if interpreted correctly. These sounds though audible but need an extensive practice and skills to be correctly understood. Any illness of heart valves like murmurs though appear in these sounds but are very difficult to be correctly identified by the cardiologist. These murmurs are even further having many types. Phonocardiogram signals can be utilized more efficiently by the cardiologists and medical officers when they are converted into some easily interpretable form rather through a conventional stethoscope. This research work is carried out with an aim to segment the heart sounds by identify the correct locations of first and second heart sounds and classify them to identify any illness of heart valves. Correct identification of S1 and S2 is an important but less addressed issue of segmentation problem. By correctly segmenting the phonocardiogram signal into its subparts any illness can easily be isolated, detected and classified. To undertake the segmentation task I have used the effectiveness of k-means clustering which is used to segregate and label the heart sounds as S1 and S2 sounds. For correct classification of illness I have used a novel feature set by combing the temporal and frequency domain characteristics. All distinct features from both the domains are made part of feature vector for classification purpose. To test the effectiveness of my method, I used PASCAL Classifying Heart Sounds Challenge 2011(PASCALCHSC2011) dataset and successfully obtained improved results for segmentation, identification and classification problem than any of the challenge participants.