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Automatic Heart Sound Segmentation and Murmur Detection in Phonocardiogram

This article aimed to design a software system for partial heart sound and define the problem to facilitate the doctor's primary job of diagnosing the normal or abnormal voice. The dataset was taken from Kaggle online site. The system was designed on several steps. The first step was to remove the noise by the use of a second-order bandpass filter, and the cutoff frequency was close to the heart sound frequency. The second step was to remove the spike by spike removal, and the final step was to use downsampling to normalize the amplitude of the signal to facilitate using the artificial neural network. The step that followed the signal processing was the segmentation and was done using Hilbert envelop, and then wavelet and PCA for feature extraction were used; finally, classification and training were done by feed forward neural network. All of these steps were implanted in MATLAB, and tests have shown that the accuracy is 97.639%. The number of data used in the training was 138 signal.