Real-time heart sound denoising for cardiac disease detection using SVM.
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| Title: | Real-time heart sound denoising for cardiac disease detection using SVM. |
|---|---|
| Authors: | Vigian, Shatiswaran1, Ramasamy, R. Kanesaraj1 r.kanesaraj@mmu.edu.my, Abdullah, Junaidi1 |
| Source: | Sound & Vibration. 2026, Vol. 60 Issue 3, p1-13. 13p. |
| Subjects: | Signal denoising, Wavelet transforms, Sound recordings, Computer-aided diagnosis, Support vector machines, Signal-to-noise ratio, Cardiovascular disease diagnosis |
| 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. [ABSTRACT FROM AUTHOR] |
| Copyright of Sound & Vibration is the property of Academic Publishing and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 195090136 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Real-time heart sound denoising for cardiac disease detection using SVM. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Vigian%2C+Shatiswaran%22">Vigian, Shatiswaran</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Ramasamy%2C+R%2E+Kanesaraj%22">Ramasamy, R. Kanesaraj</searchLink><relatesTo>1</relatesTo><i> r.kanesaraj@mmu.edu.my</i><br /><searchLink fieldCode="AR" term="%22Abdullah%2C+Junaidi%22">Abdullah, Junaidi</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Sound+%26+Vibration%22">Sound & Vibration</searchLink>. 2026, Vol. 60 Issue 3, p1-13. 13p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Signal+denoising%22">Signal denoising</searchLink><br /><searchLink fieldCode="DE" term="%22Wavelet+transforms%22">Wavelet transforms</searchLink><br /><searchLink fieldCode="DE" term="%22Sound+recordings%22">Sound recordings</searchLink><br /><searchLink fieldCode="DE" term="%22Computer-aided+diagnosis%22">Computer-aided diagnosis</searchLink><br /><searchLink fieldCode="DE" term="%22Support+vector+machines%22">Support vector machines</searchLink><br /><searchLink fieldCode="DE" term="%22Signal-to-noise+ratio%22">Signal-to-noise ratio</searchLink><br /><searchLink fieldCode="DE" term="%22Cardiovascular+disease+diagnosis%22">Cardiovascular disease diagnosis</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Sound & Vibration is the property of Academic Publishing and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.59400/sv1705 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 13 StartPage: 1 Subjects: – SubjectFull: Signal denoising Type: general – SubjectFull: Wavelet transforms Type: general – SubjectFull: Sound recordings Type: general – SubjectFull: Computer-aided diagnosis Type: general – SubjectFull: Support vector machines Type: general – SubjectFull: Signal-to-noise ratio Type: general – SubjectFull: Cardiovascular disease diagnosis Type: general Titles: – TitleFull: Real-time heart sound denoising for cardiac disease detection using SVM. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Vigian, Shatiswaran – PersonEntity: Name: NameFull: Ramasamy, R. Kanesaraj – PersonEntity: Name: NameFull: Abdullah, Junaidi IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 05 Text: 2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 15410161 Numbering: – Type: volume Value: 60 – Type: issue Value: 3 Titles: – TitleFull: Sound & Vibration Type: main |
| ResultId | 1 |
