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Heart Sound Analysis for Abnormality Detection

Zainab Arshad1, Sohail Masood Bhatti2,*, Huma Tauseef3, Arfan Jaffar2

1 The University of Lahore, Gujrat, Pakistan
2 The Superior university, Lahore, Pakistan
3 Lahore College for Women University, Lahore, Pakistan

* Corresponding Author: Sohail Masood Bhatti. Email: email

Intelligent Automation & Soft Computing 2022, 32(2), 1195-1205. https://doi.org/10.32604/iasc.2022.022160

Abstract

According to the World Health Organization, 31% death rate in the World is because of cardiovascular diseases like heart arrhythmia and heart failure. Early diagnosis of heart problems may help in timely treatment of the patients and hence control death rate. Heart sounds are good signals of heart health if examined by an expert. Moreover, heart sounds can be analyzed with inexpensive and portable medical devices. Automatic heart sound classification can be very useful in diagnosing heart problems. Major focus of this research is to study the existing techniques for heart sound classification and develop a more sophisticated method. A signal processing technique is been proposed for heart sound classification. Five classifiers, Naive Bayes algorithm, Sequential (SMO), J48, Rep tree and Random Forest (RF) are used for this experiment. A detailed experimentation is performed to fine-tune the method and finally results are compared with the existing systems. The best proposed classifying technique results the overall accuracy of 91.33%.

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Cite This Article

APA Style
Arshad, Z., Bhatti, S.M., Tauseef, H., Jaffar, A. (2022). Heart sound analysis for abnormality detection. Intelligent Automation & Soft Computing, 32(2), 1195-1205. https://doi.org/10.32604/iasc.2022.022160
Vancouver Style
Arshad Z, Bhatti SM, Tauseef H, Jaffar A. Heart sound analysis for abnormality detection. Intell Automat Soft Comput . 2022;32(2):1195-1205 https://doi.org/10.32604/iasc.2022.022160
IEEE Style
Z. Arshad, S.M. Bhatti, H. Tauseef, and A. Jaffar "Heart Sound Analysis for Abnormality Detection," Intell. Automat. Soft Comput. , vol. 32, no. 2, pp. 1195-1205. 2022. https://doi.org/10.32604/iasc.2022.022160



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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