![]() ![]() In this paper, we performed (1) an in-depth analysis of various time and frequency domain features, followed by experimental determination of effective feature subsets for improved classification performance (2) both segmented and unsegmented phonocardiogram signals are studied and important results concerning the respective feature subsets and their classification performances are reported (3) different classification algorithms including support vector machine, kth nearest neighbor, decision tree, ensemble classifier, artificial neural network and long short-term memory (LSTM) networks are employed to evaluate the performance of the proposed feature subsets and their comparison with other established features and methods is presented. In this paper, automatic heart sound classification using segmented and unsegmented phonocardiogram (PCG) signals is presented. Heart abnormality detection using heart sound signals (phonocardiogram) has been an active research area for the last few decades.
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