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Cepstral Features Extraction for Heart Sounds Classification


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DOI: https://doi.org/10.15866/iree.v13i5.14965

Abstract


A novel method for separation between normal and abnormal heart sounds based on Phonocardiogram (PCG) is presented in this article. For features extraction phase, Mel-Frequency Cepstral Coefficients (MFCC) algorithm is used to extract information from heart sound signals. In this step, changing the frames size, during framing process, shows it influence on the obtained result. In classification step, Support Vector Machines (SVM) is used with different kernels. Simulation results obtained from different databases are compared and discussed. The developed system gave good results when applied to different datasets with an accuracy of 96% and 98% for the first and the second dataset respectively. The developed system can be used for other applications in biomedical domains
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Keywords


Phonocardiogram; Signal Processing; MFCC; SVM; Classification

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References


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