Heart sound classification using signal processing and machine learning algorithms

According to global statistics and the world health organization (WHO), about 17.5 million people die each year from cardiovascular disease. In this paper, the heart sounds gathered by a stethoscope are analyzed to diagnose several diseases caused by heart failure. This research’s primary process is...

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Autores principales:	Yasser Zeinali, Seyed Taghi Akhavan Niaki
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2022
Materias:	Heart sound Signal processing algorithms Machine learning algorithms Dimensional reduction algorithms Feature selection Gradient boosting classifier Cybernetics Q300-390 Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/27035dc134944b0a9b008aae1dbd85a8
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spelling	oai:doaj.org-article:27035dc134944b0a9b008aae1dbd85a82021-11-20T05:15:26ZHeart sound classification using signal processing and machine learning algorithms2666-827010.1016/j.mlwa.2021.100206https://doaj.org/article/27035dc134944b0a9b008aae1dbd85a82022-03-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S2666827021001031https://doaj.org/toc/2666-8270According to global statistics and the world health organization (WHO), about 17.5 million people die each year from cardiovascular disease. In this paper, the heart sounds gathered by a stethoscope are analyzed to diagnose several diseases caused by heart failure. This research’s primary process is to identify and classify the data related to the heart sounds categorized in four general groups of S1to S4. The sounds S1and S2are considered as the heart’s normal sounds, and the sounds S3and S4are the abnormal sounds of the heart (heart murmurs), each expressing a specific type of heart disease. In this regard, the desired features are first extracted after retrieving the data by signal processing algorithms. In the next step, feature selection algorithms are used to select the compelling features to reduce the problem’s dimensions and obtain the optimal answer faster. While the existing algorithms in the literature classify the sound into two groups of normal and abnormal, in the final section, some of the most popular classification algorithms are utilized to classify the type of sound into three classes of normal, S3and S4categories. The proposed methodology obtained an accuracy rate of 87.5% and 95% for multiclass data (3 classes) and 98% for binary classification (normal vs. abnormal) problems.Yasser ZeinaliSeyed Taghi Akhavan NiakiElsevierarticleHeart soundSignal processing algorithmsMachine learning algorithmsDimensional reduction algorithmsFeature selectionGradient boosting classifierCyberneticsQ300-390Electronic computers. Computer scienceQA75.5-76.95ENMachine Learning with Applications, Vol 7, Iss , Pp 100206- (2022)
institution	DOAJ
collection	DOAJ
language	EN
topic	Heart sound Signal processing algorithms Machine learning algorithms Dimensional reduction algorithms Feature selection Gradient boosting classifier Cybernetics Q300-390 Electronic computers. Computer science QA75.5-76.95
spellingShingle	Heart sound Signal processing algorithms Machine learning algorithms Dimensional reduction algorithms Feature selection Gradient boosting classifier Cybernetics Q300-390 Electronic computers. Computer science QA75.5-76.95 Yasser Zeinali Seyed Taghi Akhavan Niaki Heart sound classification using signal processing and machine learning algorithms
description	According to global statistics and the world health organization (WHO), about 17.5 million people die each year from cardiovascular disease. In this paper, the heart sounds gathered by a stethoscope are analyzed to diagnose several diseases caused by heart failure. This research’s primary process is to identify and classify the data related to the heart sounds categorized in four general groups of S1to S4. The sounds S1and S2are considered as the heart’s normal sounds, and the sounds S3and S4are the abnormal sounds of the heart (heart murmurs), each expressing a specific type of heart disease. In this regard, the desired features are first extracted after retrieving the data by signal processing algorithms. In the next step, feature selection algorithms are used to select the compelling features to reduce the problem’s dimensions and obtain the optimal answer faster. While the existing algorithms in the literature classify the sound into two groups of normal and abnormal, in the final section, some of the most popular classification algorithms are utilized to classify the type of sound into three classes of normal, S3and S4categories. The proposed methodology obtained an accuracy rate of 87.5% and 95% for multiclass data (3 classes) and 98% for binary classification (normal vs. abnormal) problems.
format	article
author	Yasser Zeinali Seyed Taghi Akhavan Niaki
author_facet	Yasser Zeinali Seyed Taghi Akhavan Niaki
author_sort	Yasser Zeinali
title	Heart sound classification using signal processing and machine learning algorithms
title_short	Heart sound classification using signal processing and machine learning algorithms
title_full	Heart sound classification using signal processing and machine learning algorithms
title_fullStr	Heart sound classification using signal processing and machine learning algorithms
title_full_unstemmed	Heart sound classification using signal processing and machine learning algorithms
title_sort	heart sound classification using signal processing and machine learning algorithms
publisher	Elsevier
publishDate	2022
url	https://doaj.org/article/27035dc134944b0a9b008aae1dbd85a8
work_keys_str_mv	AT yasserzeinali heartsoundclassificationusingsignalprocessingandmachinelearningalgorithms AT seyedtaghiakhavanniaki heartsoundclassificationusingsignalprocessingandmachinelearningalgorithms
_version_	1718419471927869440

Heart sound classification using signal processing and machine learning algorithms

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