Heart Sound Processing for Early Diagnostic of Heart Abnormalities using Support Vector Machine


  • Sebastian Michael Paschalis Universitas Katolik Indonesia Atma Jaya, Indonesia
  • Duma Kristina Yanti Hutapea Universitas Katolik Indonesia Atma Jaya, Indonesia
  • Karel Octavianus Bachri Universitas Katolik Indonesia Atma Jaya, Indonesia




Support Vector Machine, Heart Sound, Linear Kernel, Cross Validation, heart disease, early diagnostic


This paper addresses the critical issue of cardiovascular disease (CVD), the leading cause of global mortality, emphasizing the imperative for effective and early detection to mitigate CVD-related deaths. The research problem underscores the urgency of developing advanced diagnostic tools to identify heart abnormalities promptly. The primary objective is to create a Support Vector Machine (SVM) algorithm for accurate classification of different heart conditions, namely Normal heart, Mitral Stenosis, and Mitral Regurgitation. To achieve this objective, the study utilizes a dataset of heart sounds available online using a 10-fold cross-validation method. The focus is on evaluating the efficacy of various kernel functions within the SVM framework for heart sound classification. The findings demonstrate that the linear kernel exhibits superior accuracy and robustness in effectively classifying heart conditions. Notably, the proposed classification method attains an impressive 96% accuracy, highlighting its potential as a reliable tool for early detection of cardiovascular diseases. This research contributes to the ongoing efforts to enhance diagnostic capabilities and ultimately reduce the global burden of CVD-related fatalities.


Download data is not yet available.


World Health Organization, “Cardiovascular diseases.” Accessed: Mar. 03, 2023. Online. Available: https://www.who.int/health-topics/cardiovascular-diseases

I. H. Heiberg et al., “Undiagnosed cardiovascular disease prior to cardiovascular death in individuals with severe mental illness,” Acta Psychiatr. Scand., vol. 139, no. 6, pp. 558–571, 2019.

C. Pinto, D. Pereira, J. Ferreira-Coimbra, J. Português, V. Gama, and M. Coimbra, “A comparative study of electronic stethoscopes for cardiac auscultation,” in 2017 39th Annual international conference of the IEEE Engineering in Medicine and Biology Society (EMBC), IEEE, 2017, pp. 2610–2613.

V. Voin, R. J. Oskouian, M. Loukas, and R. S. Tubbs, “Auscultation of the heart: the basics with anatomical correlation,” Clin. Anat., vol. 30, no. 1, pp. 58–60, 2017.

A. Subasi, Practical guide for biomedical signals analysis using machine learning techniques: A MATLAB based approach. Academic Press, 2019.

S. McGee, Evidence-based physical diagnosis e-book. Elsevier Health Sciences, 2021.

M. Zauli et al., “Exploring Microphone Technologies for Digital Auscultation Devices,” Micromachines, vol. 14, no. 11, p. 2092, 2023.

E. G. Dimond and A. Benchimol, “Phonocardiography,” Calif. Med., vol. 94, no. 3, p. 139, 1961.

P. T. Krishnan, P. Balasubramanian, and S. Umapathy, “Automated heart sound classification system from unsegmented phonocardiogram (PCG) using deep neural network,” Phys. Eng. Sci. Med., vol. 43, no. 2, pp. 505–515, 2020.

D. Chen et al., “Automatic classification of normal–abnormal heart sounds using convolution neural network and long-short term memory,” Electronics, vol. 11, no. 8, p. 1246, 2022.

M. Nassralla, Z. El Zein, and H. Hajj, “Classification of normal and abnormal heart sounds,” in 2017 fourth international conference on advances in biomedical engineering (ICABME), IEEE, 2017, pp. 1–4.

C. Kwak and O.-W. Kwon, “Cardiac disorder classification by heart sound signals using murmur likelihood and hidden Markov model state likelihood,” IET signal Process., vol. 6, no. 4, pp. 326–334, 2012.

V. V Ramalingam, A. Dandapath, and M. K. Raja, “Heart disease prediction using machine learning techniques: a survey,” Int. J. Eng. Technol., vol. 7, no. 2.8, pp. 684–687, 2018.

A. Yadav, A. Singh, M. K. Dutta, and C. M. Travieso, “Machine learning-based classification of cardiac diseases from PCG recorded heart sounds,” Neural Comput. Appl., vol. 32, no. 24, pp. 17843–17856, 2020.

S. Douedi and H. Douedi, “Mitral Regurgitation,” Treasure Island (FL): StatPearls Publishing. Accessed: Sep. 25, 2023. Online. Available: https://www.ncbi.nlm.nih.gov/books/NBK553135/

R. Ranjan and G. S. Pressman, “Aetiology and epidemiology of mitral stenosis,” E-Journal Cardiol. Pract., vol. 16, p. 14, 2018.

Yaseen, G.-Y. Son, and S. Kwon, “Classification of heart sound signal using multiple features,” Appl. Sci., vol. 8, no. 12, p. 2344, 2018.

A. Syaifuddin and S. Suryono, “Fast Fourier Transform (Fft) Untuk Analisis Sinyal Suara Doppler Ultrasonik,” Youngster Phys. J., vol. 3, no. 3, pp. 181–188, 2014.

F. N. Abdillah, “Implementasi Algoritma Fast Fourier Transform (FFT) dan Algoritma Harmonic Product Spectrum (HPS) pada Tuner Gitar berbasis Android,” NUANSA Inform., vol. 11, no. 2, 2017.

D. T. Kusuma, “Fast Fourier Transform (FFT) Dalam Transformasi Sinyal Frekuensi Suara Sebagai Upaya Perolehan Average Energy (AE) Musik,” 2020.

S. Karamizadeh, S. M. Abdullah, M. Halimi, J. Shayan, and M. javad Rajabi, “Advantage and drawback of support vector machine functionality,” in 2014 international conference on computer, communications, and control technology (I4CT), IEEE, 2014, pp. 63–65.

M. Achirul Nanda, K. Boro Seminar, D. Nandika, and A. Maddu, “A comparison study of kernel functions in the support vector machine and its application for termite detection,” Information, vol. 9, no. 1, p. 5, 2018.

S. Hulu and P. Sihombing, “Analysis of performance cross validation method and K-Nearest neighbor in classification data,” Int. J. Res. Rev., vol. 7, no. 4, pp. 69–73, 2020.

S. A. Hicks et al., “On evaluation metrics for medical applications of artificial intelligence,” Sci. Rep., vol. 12, no. 1, p. 5979, 2022.

G. E. Güraksın and H. Uguz, “Classification of heart sounds based on the least squares support vector machine,” Int. J. Innov. Comput. Inf. Control, vol. 7, no. 12, pp. 7131–7144, 2011.




How to Cite

Paschalis, S.M. et al. 2024. Heart Sound Processing for Early Diagnostic of Heart Abnormalities using Support Vector Machine. Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer. 8, 1 (Jun. 2024), 57–65. DOI:https://doi.org/10.31961/eltikom.v8i1.1031.