Detacting heart murmur in phonocardiograms

Heart auscultation is one of the oldest non-invasive method for detection valvular heart disease. In thesis we have focused on the analysis of phonocardiograms with digital signal processing methods and methods of artificial intelligence for classification. We have divided the signal obtained from electronic stethoscope in to segments where one of the segment corresponds to one cardiac cycle. After that we calculate MFCC features on one of the segments. The features serve as an input to machine learning algorithms in system Orange. Target classification class is the condition of a patient. We have distinguish the phonocardiograms with a heart murmur and without it. The best classification accuracy that we achieved is with naive Bayes classificator of 92.4 %. The expected accuracy of majority class was 75.2 %. The best achieved sensitivity and specificity was 76.9 % and 95.4 % respectively, also with naive Bayes classificator. In our opinion such system could be used by physicians to help diagnose heart valve diseases. In theoretical part of the thesis we have described algorithms that we used and what are the limitations of processing a signal