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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Main Authors: Shamik Tiwari, Anurag Jain, Akhilesh Kumar Sharma, Khaled Mohamad Almustafa
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/9508967/
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spelling 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&#x0025; 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&#x0025; 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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