Classification of normal and abnormal heart by classifying PCG signal using MFCC coefficients and CGP-ANN classifier

  • Muhammad Israr Information and Communication Harbin Engineering University, China
  • Muhammad Zia Electronics Engineering University of Technology Nowshera KP Pakistan
  • Naveed Ur Rehman Department of Electrical Engineering City University, Peshawar
  • Imran Ullah Electronics Engineering University of Technology Nowshera KP Pakistan
  • Khushal Khan Department of Electrical Engineering CECOS University Peshawar

Abstract

Globally, A leading cause of death is heart disease and a serious public health concern. The anomalies in heart sound appears before the heart disease symptoms. The sounds are type of auscultation, which is a process dealing with sounds in a body that generates due to mechanical vibrations of organs, Auscultation is a potential method in medical science to detect abnormalities in heart sounds and in case of suspicion The patient follows up with a referral for other evaluations, such as an electrocardiogram. In medical sciences early detection of symptoms are of major importance, this research work is a good step toward the detection of abnormalities in heart before symptom appearing by processing the phonocardiogram (PCG) signal. In this paper PCG signals are classified by utilizing the features of Mel frequency cepstral coefficients (MFCC) through Cartesian Genetic Programming - Artificial Network (CGP-ANN) Classifier. The diagnostic accuracy of proposed methodology is found 99.50% which is more than other classifiers like Support Vector Machine (SVM) and Convolutional Neural Network (CNN). The accuracy of model as compared to other models can prove the performance superiority of the proposed system.

Published
Jul 21, 2023
How to Cite
ISRAR, Muhammad et al. Classification of normal and abnormal heart by classifying PCG signal using MFCC coefficients and CGP-ANN classifier. Mehran University Research Journal of Engineering and Technology, [S.l.], v. 42, n. 3, p. 160-166, july 2023. ISSN 2413-7219. Available at: <https://publications.muet.edu.pk/index.php/muetrj/article/view/2389>. Date accessed: 05 dec. 2024. doi: http://dx.doi.org/10.22581/muet1982.2303.16.
Section
Articles
This is an open Access Article published by Mehran University of Engineering and Technolgy, Jamshoro under CCBY 4.0 International License