Phonocardiogram Signal Based Multi-Class Cardiac Diagnostic Decision Support System
A Phonocardiogram (PCG) signal represents murmurs and sounds signals made by vibrations caused for the period of a cardiac cycle. Acoustic wave generated through the beat of the cardiac cycle propagates through the chest wall. It can be easily recorded by a low-cost small handheld digital device cal...
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doaj-40c797c1fd1f421e8ccef24bce25bc132021-08-12T23:00:22ZengIEEEIEEE Access2169-35362021-01-01911071011072210.1109/ACCESS.2021.31033169508967Phonocardiogram Signal Based Multi-Class Cardiac Diagnostic Decision Support SystemShamik Tiwari0https://orcid.org/0000-0002-5987-7101Anurag Jain1https://orcid.org/0000-0001-5155-022XAkhilesh Kumar Sharma2https://orcid.org/0000-0002-7308-7800Khaled Mohamad Almustafa3https://orcid.org/0000-0003-2129-7686Department of Virtualization, School of Computer Science, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, IndiaDepartment of Virtualization, School of Computer Science, University of Petroleum and Energy Studies, Dehradun, Uttarakhand, IndiaDepartment of Information Technology, Manipal University Jaipur, Jaipur, Rajasthan, IndiaDepartment of Information Systems, College of Computer and Information Science, Prince Sultan University, Riyadh, Saudi ArabiaA Phonocardiogram (PCG) signal represents murmurs and sounds signals made by vibrations caused for the period of a cardiac cycle. Acoustic wave generated through the beat of the cardiac cycle propagates through the chest wall. It can be easily recorded by a low-cost small handheld digital device called a stethoscope. It provides information like heart rate, intensity, tone, quality, frequency, and location of various components of cardiac sound. Due to these characteristics, phonocardiogram signals can be used to detect heart status at an early stage in a non-invasive manner. In previous studies, the Convolutional Neural Network (ConvNet) is the most studied architecture, which was fed by features, namely Mel Frequency Cepstral (MFC), Chroma Energy Normalized Statistics (CENS), and Constant-Q Transform (CQT). This work has proposed a ConvNet model trained by Hybrid Constant-Q Transform (HCQT) for heart sound beat classification. CQT, Variable-Q Transform (VQT), and HCQT are extracted from each phonocardiogram signal as the acoustic features, including the dominant MFCC features, feed into five-layer regularized ConvNets. After analyzing the literature in the same domain, it can be stated that this is the first time HCQT is being utilized for PCG signals. The findings of the experiments demonstrate that HCQT is more effective than standard CQT and other variants. Also, the accuracies of the system proposed in this work on the validation datasets are 96% in multi-class classification, which outperforms the proposed work relative to other models significantly. The source code is available on the Github repository <uri>https://github.com/shamiktiwari/</uri> PCG-signal-Classification-using-Hybrid-Constant-Q-Transform to support the research community.https://ieeexplore.ieee.org/document/9508967/Cardiovascular diseaseconvolutional neural networkdecision support system deep learningmulti-class classificationphonocardiogram signal |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Shamik Tiwari Anurag Jain Akhilesh Kumar Sharma Khaled Mohamad Almustafa |
spellingShingle |
Shamik Tiwari Anurag Jain Akhilesh Kumar Sharma Khaled Mohamad Almustafa Phonocardiogram Signal Based Multi-Class Cardiac Diagnostic Decision Support System IEEE Access Cardiovascular disease convolutional neural network decision support system deep learning multi-class classification phonocardiogram signal |
author_facet |
Shamik Tiwari Anurag Jain Akhilesh Kumar Sharma Khaled Mohamad Almustafa |
author_sort |
Shamik Tiwari |
title |
Phonocardiogram Signal Based Multi-Class Cardiac Diagnostic Decision Support System |
title_short |
Phonocardiogram Signal Based Multi-Class Cardiac Diagnostic Decision Support System |
title_full |
Phonocardiogram Signal Based Multi-Class Cardiac Diagnostic Decision Support System |
title_fullStr |
Phonocardiogram Signal Based Multi-Class Cardiac Diagnostic Decision Support System |
title_full_unstemmed |
Phonocardiogram Signal Based Multi-Class Cardiac Diagnostic Decision Support System |
title_sort |
phonocardiogram signal based multi-class cardiac diagnostic decision support system |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2021-01-01 |
description |
A Phonocardiogram (PCG) signal represents murmurs and sounds signals made by vibrations caused for the period of a cardiac cycle. Acoustic wave generated through the beat of the cardiac cycle propagates through the chest wall. It can be easily recorded by a low-cost small handheld digital device called a stethoscope. It provides information like heart rate, intensity, tone, quality, frequency, and location of various components of cardiac sound. Due to these characteristics, phonocardiogram signals can be used to detect heart status at an early stage in a non-invasive manner. In previous studies, the Convolutional Neural Network (ConvNet) is the most studied architecture, which was fed by features, namely Mel Frequency Cepstral (MFC), Chroma Energy Normalized Statistics (CENS), and Constant-Q Transform (CQT). This work has proposed a ConvNet model trained by Hybrid Constant-Q Transform (HCQT) for heart sound beat classification. CQT, Variable-Q Transform (VQT), and HCQT are extracted from each phonocardiogram signal as the acoustic features, including the dominant MFCC features, feed into five-layer regularized ConvNets. After analyzing the literature in the same domain, it can be stated that this is the first time HCQT is being utilized for PCG signals. The findings of the experiments demonstrate that HCQT is more effective than standard CQT and other variants. Also, the accuracies of the system proposed in this work on the validation datasets are 96% in multi-class classification, which outperforms the proposed work relative to other models significantly. The source code is available on the Github repository <uri>https://github.com/shamiktiwari/</uri> PCG-signal-Classification-using-Hybrid-Constant-Q-Transform to support the research community. |
topic |
Cardiovascular disease convolutional neural network decision support system deep learning multi-class classification phonocardiogram signal |
url |
https://ieeexplore.ieee.org/document/9508967/ |
work_keys_str_mv |
AT shamiktiwari phonocardiogramsignalbasedmulticlasscardiacdiagnosticdecisionsupportsystem AT anuragjain phonocardiogramsignalbasedmulticlasscardiacdiagnosticdecisionsupportsystem AT akhileshkumarsharma phonocardiogramsignalbasedmulticlasscardiacdiagnosticdecisionsupportsystem AT khaledmohamadalmustafa phonocardiogramsignalbasedmulticlasscardiacdiagnosticdecisionsupportsystem |
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1721209173197193216 |