Real-time heart sound denoising for cardiac disease detection using SVM

Shatiswaran  Vigian; R Kanesaraj  Ramasamy; Junaidi  Abdullah

doi:10.59400/sv1705

Real-time heart sound denoising for cardiac disease detection using SVM

Shatiswaran Vigian
Faculty Computing Informatics, Multimedia University, Cyberjaya 63100, Malaysia
R Kanesaraj Ramasamy
Faculty Computing Informatics, Multimedia University, Cyberjaya 63100, Malaysia
Junaidi Abdullah
Faculty Computing Informatics, Multimedia University, Cyberjaya 63100, Malaysia

Article ID: 1705

DOI: https://doi.org/10.59400/sv1705

Keywords: denoising; heart sounds; machine learning; phonocardiogram; signal processing; support vector machine

Abstract

Heart-sound recordings are highly susceptible to environmental and physiological noise, which complicates clinical interpretation and reduces the reliability of automated diagnostic systems. Effective denoising is therefore essential for preserving waveform morphology and enabling accurate feature extraction. This study proposes a heart-sound-specific wavelet approach and evaluates its denoising performance in comparison with conventional wavelets and support vector machine (SVM)–based methods. The method was assessed using publicly available datasets, including PASCAL and PhysioNet, which provide diverse normal and pathological phonocardiogram (PCG) recordings. Uniform and Gaussian white noise were added at varying signal-to-noise ratios (SNRs) to simulate realistic acquisition environments. Denoising performance was quantified using cross-correlation coefficients, SNR improvement, root-mean-squared error (RMSE), and mean absolute error (MAE). Results demonstrate that the proposed heart-sound wavelet achieved superior noise-suppression capability and a 7% performance gain over commonly used Db and Bior wavelets, while maintaining waveform integrity. Subsequent classification experiments showed that denoising quality directly influenced diagnostic performance: the model achieved 0.87 accuracy, 0.81 precision, and 0.83 sensitivity on the PASCAL dataset, and 0.997 accuracy, 0.946 sensitivity, and 0.944 precision on PhysioNet. These findings highlight the potential of tailored wavelet-based denoising to enhance automated heart-sound analysis and support more robust clinical and embedded diagnostic applications.

Published

2026-06-24

How to Cite

Vigian, S., Ramasamy, R. K., & Abdullah, J. (2026). Real-time heart sound denoising for cardiac disease detection using SVM. Sound & Vibration, 60(3). https://doi.org/10.59400/sv1705

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Vol. 60 No. 3 (2026)

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Article

This work is licensed under a Creative Commons Attribution 4.0 International License.

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Real-time heart sound denoising for cardiac disease detection using SVM

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