Segmentation of Phonocardiograms Signal

Abstract

Heart sound is a kind of bio-sound, mainly through the media pass sound signals. The measures of the heart sound signals involved in acoustics, fluid mechanics research. when added some noise for selecting pure PCG signal by using adaptive white Gaussian noise. Than denoise the PCG signal by using Discrete Wavelet transform and decomposes a signal into a 4 level of basic functions. These basic functions are called Discrete wavelet transform (DWT), which transforms a discrete time signal to a discrete wavelet representation. Experiments are conducted on 23 different recordings of heart sound where experiment working on 2 normal signal and 19 Abnormal signals where working on the accuracy of the PCG signal that is depending on the training and testing data. Segmentation process based on Shannon entropy method for low amplitude and GSF is based on high amplitude. Toward this objective, after preprocessing the PCG signal, for feature extraction of the PCG signal fixed windows were moved on the preprocessed signal, and in each analysis window, two frequency-and amplitude-based features were calculated from the excerpted segment. In order to recognize the delineated PCG sounds, ?rst, S1 and S2 were detected. Then, a new DS was regenerated from the signal whose S1 and S2 were eliminated to detect occasional S3 and S4 sounds. Finally, probable murmurs and souf?es were spotted. The proposed algorithm was applied to 6 beat PCG. signals gathered from patients with different valve diseases. This feature Extraction of the PCG signal is use PCA for reducing 216 dimensions to 7 dimensions of the PCG signal. and the classifier is used SVM method which is found the normal and abnormal heart sound of the PCG signal.