NRC-Net: Automated noise robust cardio net for detecting valvular cardiac diseases using optimum transformation method with heart sound signals

Shuvo, SB; Alam, SS; Ayman, SU; Chakma, A; Barua, PD; Acharya, UR

NRC-Net: Automated noise robust cardio net for detecting valvular cardiac diseases using optimum transformation method with heart sound signals

Shuvo, SB Alam, SS Ayman, SU Chakma, A Barua, PD Acharya, UR

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Publisher:: ELSEVIER SCI LTD
Publication Type:: Journal Article
Citation:: Biomedical Signal Processing and Control, 2023, 86
Issue Date:: 2023-09-01

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Field	Value	Language
dc.contributor.author	Shuvo, SB
dc.contributor.author	Alam, SS
dc.contributor.author	Ayman, SU
dc.contributor.author	Chakma, A
dc.contributor.author	Barua, PD
dc.contributor.author	Acharya, UR
dc.date.accessioned	2024-04-05T02:17:35Z
dc.date.available	2024-04-05T02:17:35Z
dc.date.issued	2023-09-01
dc.identifier.citation	Biomedical Signal Processing and Control, 2023, 86
dc.identifier.issn	1746-8094
dc.identifier.issn	1746-8108
dc.identifier.uri	http://hdl.handle.net/10453/177493
dc.description.abstract	Objective: Cardiovascular Diseases (CVDs) can be effectively treated when detected early, reducing mortality rates significantly. Traditionally, Phonocardiogram (PCG) signals have been utilized for detecting cardiovascular disease due to their cost-effectiveness and simplicity. Nevertheless, various environmental and physiological noises frequently affect the PCG signals, compromising their essential distinctive characteristics. The prevalence of this issue in overcrowded and resource-constrained hospitals can compromise the accuracy of medical diagnoses. Therefore, this study aims to discover the optimal transformation method for detecting CVDs using noisy heart sound signals and propose a noise-robust network to improve the CVDs classification performance. Methods: For the identification of the optimal transformation method for noisy heart sound data, Mel Frequency Cepstral Coefficients (MFCCs), Short-Time Fourier Transform (STFT), Constant-Q Nonstationary Gabor Transform (CQT), and Continuous Wavelet Transform (CWT) has been used with Visual Geometry Group with 16-layer deep model architecture (VGG16). Furthermore, we propose a novel Convolutional Recurrent Neural Network (CRNN) architecture called Noise Robust Cardio Net (NRC-Net), which is a lightweight model to classify Mitral Regurgitation, Aortic Stenosis, Mitral Stenosis, Mitral Valve Prolapse, and normal heart sounds using PCG signals contaminated with respiratory and random noises. An attention block is included to extract important temporal and spatial features from the noisy corrupted heart sound. Results: The results of this study indicate that CWT is the optimal transformation method for noisy heart sound signals. When evaluated on the GitHub heart sound dataset, CWT demonstrates an accuracy of 95.69% for VGG16, which is 1.95% better than the second-best CQT transformation technique. Moreover, our proposed NRC-Net with CWT obtained an accuracy of 97.4%, which is 1.71% higher than the VGG16. Conclusion: Based on the outcomes illustrated in the paper, the proposed model is robust to noisy data and can be used in polyclinics and hospitals to detect valvular cardiac diseases accurately.
dc.language	English
dc.publisher	ELSEVIER SCI LTD
dc.relation.ispartof	Biomedical Signal Processing and Control
dc.relation.isbasedon	10.1016/j.bspc.2023.105272
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0903 Biomedical Engineering, 0906 Electrical and Electronic Engineering, 1004 Medical Biotechnology
dc.subject.classification	Biomedical Engineering
dc.subject.classification	3006 Food sciences
dc.subject.classification	4003 Biomedical engineering
dc.title	NRC-Net: Automated noise robust cardio net for detecting valvular cardiac diseases using optimum transformation method with heart sound signals
dc.type	Journal Article
utslib.citation.volume	86
utslib.for	0903 Biomedical Engineering
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	1004 Medical Biotechnology
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Civil and Environmental Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2024-04-05T02:17:34Z
pubs.publication-status	Published
pubs.volume	86

Abstract:

Objective: Cardiovascular Diseases (CVDs) can be effectively treated when detected early, reducing mortality rates significantly. Traditionally, Phonocardiogram (PCG) signals have been utilized for detecting cardiovascular disease due to their cost-effectiveness and simplicity. Nevertheless, various environmental and physiological noises frequently affect the PCG signals, compromising their essential distinctive characteristics. The prevalence of this issue in overcrowded and resource-constrained hospitals can compromise the accuracy of medical diagnoses. Therefore, this study aims to discover the optimal transformation method for detecting CVDs using noisy heart sound signals and propose a noise-robust network to improve the CVDs classification performance. Methods: For the identification of the optimal transformation method for noisy heart sound data, Mel Frequency Cepstral Coefficients (MFCCs), Short-Time Fourier Transform (STFT), Constant-Q Nonstationary Gabor Transform (CQT), and Continuous Wavelet Transform (CWT) has been used with Visual Geometry Group with 16-layer deep model architecture (VGG16). Furthermore, we propose a novel Convolutional Recurrent Neural Network (CRNN) architecture called Noise Robust Cardio Net (NRC-Net), which is a lightweight model to classify Mitral Regurgitation, Aortic Stenosis, Mitral Stenosis, Mitral Valve Prolapse, and normal heart sounds using PCG signals contaminated with respiratory and random noises. An attention block is included to extract important temporal and spatial features from the noisy corrupted heart sound. Results: The results of this study indicate that CWT is the optimal transformation method for noisy heart sound signals. When evaluated on the GitHub heart sound dataset, CWT demonstrates an accuracy of 95.69% for VGG16, which is 1.95% better than the second-best CQT transformation technique. Moreover, our proposed NRC-Net with CWT obtained an accuracy of 97.4%, which is 1.71% higher than the VGG16. Conclusion: Based on the outcomes illustrated in the paper, the proposed model is robust to noisy data and can be used in polyclinics and hospitals to detect valvular cardiac diseases accurately.

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