Algorithms for Automatic Analysis and Classification of Heart Sounds–A Systematic Review

Cardiovascular diseases currently pose the highest threat to human health around the world. Proper investigation of the abnormalities in heart sounds is known to provide vital clinical information that can assist in the diagnosis and management of cardiac conditions. However, despite significant adv...

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Main Authors: Amit Krishna Dwivedi, Syed Anas Imtiaz, Esther Rodriguez-Villegas
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/8586788/
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spelling doaj-c9c924983bc14240bf4a76d362357a372021-03-29T22:50:56ZengIEEEIEEE Access2169-35362019-01-0178316834510.1109/ACCESS.2018.28894378586788Algorithms for Automatic Analysis and Classification of Heart Sounds–A Systematic ReviewAmit Krishna Dwivedi0https://orcid.org/0000-0002-8517-0775Syed Anas Imtiaz1Esther Rodriguez-Villegas2Department of Electrical and Electronic Engineering, Imperial College London, London, U.K.Department of Electrical and Electronic Engineering, Imperial College London, London, U.K.Department of Electrical and Electronic Engineering, Imperial College London, London, U.K.Cardiovascular diseases currently pose the highest threat to human health around the world. Proper investigation of the abnormalities in heart sounds is known to provide vital clinical information that can assist in the diagnosis and management of cardiac conditions. However, despite significant advances in the development of algorithms for automated classification and analysis of heart sounds, the validity of different approaches has not been systematically reviewed. This paper provides an in-depth systematic review and critical analysis of all the existing approaches for automatic identification and classification of the heart sounds. All statements on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2009 Checklist were followed and addressed thoroughly to maintain the quality of the accounted systematic review. Out of 1347 research articles available in the academic databases from 1963 to 2018, 117 peer-reviewed articles were found to fall under the search and selection criteria of this paper. Amongst them: 53 articles are focused on segmentation, 72 of the studies are related to the feature extraction approaches and 88 to classification, and 56 reported on the databases and heart sounds acquisition. From this review, it is clear that, although a lot of research has been done in the field of automated analysis, there is still some work to be done to develop robust methods for identification and classification of various events in the cardiac cycle so that this could be effectively used to improve the diagnosis and management of cardiovascular diseases in combination with the wearable mobile technologies.https://ieeexplore.ieee.org/document/8586788/Segmentationfeature extractionclassificationheart sounds databaseswearable cardiac monitoringheart sounds analysis
collection DOAJ
language English
format Article
sources DOAJ
author Amit Krishna Dwivedi
Syed Anas Imtiaz
Esther Rodriguez-Villegas
spellingShingle Amit Krishna Dwivedi
Syed Anas Imtiaz
Esther Rodriguez-Villegas
Algorithms for Automatic Analysis and Classification of Heart Sounds–A Systematic Review
IEEE Access
Segmentation
feature extraction
classification
heart sounds databases
wearable cardiac monitoring
heart sounds analysis
author_facet Amit Krishna Dwivedi
Syed Anas Imtiaz
Esther Rodriguez-Villegas
author_sort Amit Krishna Dwivedi
title Algorithms for Automatic Analysis and Classification of Heart Sounds–A Systematic Review
title_short Algorithms for Automatic Analysis and Classification of Heart Sounds–A Systematic Review
title_full Algorithms for Automatic Analysis and Classification of Heart Sounds–A Systematic Review
title_fullStr Algorithms for Automatic Analysis and Classification of Heart Sounds–A Systematic Review
title_full_unstemmed Algorithms for Automatic Analysis and Classification of Heart Sounds–A Systematic Review
title_sort algorithms for automatic analysis and classification of heart sounds–a systematic review
publisher IEEE
series IEEE Access
issn 2169-3536
publishDate 2019-01-01
description Cardiovascular diseases currently pose the highest threat to human health around the world. Proper investigation of the abnormalities in heart sounds is known to provide vital clinical information that can assist in the diagnosis and management of cardiac conditions. However, despite significant advances in the development of algorithms for automated classification and analysis of heart sounds, the validity of different approaches has not been systematically reviewed. This paper provides an in-depth systematic review and critical analysis of all the existing approaches for automatic identification and classification of the heart sounds. All statements on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses 2009 Checklist were followed and addressed thoroughly to maintain the quality of the accounted systematic review. Out of 1347 research articles available in the academic databases from 1963 to 2018, 117 peer-reviewed articles were found to fall under the search and selection criteria of this paper. Amongst them: 53 articles are focused on segmentation, 72 of the studies are related to the feature extraction approaches and 88 to classification, and 56 reported on the databases and heart sounds acquisition. From this review, it is clear that, although a lot of research has been done in the field of automated analysis, there is still some work to be done to develop robust methods for identification and classification of various events in the cardiac cycle so that this could be effectively used to improve the diagnosis and management of cardiovascular diseases in combination with the wearable mobile technologies.
topic Segmentation
feature extraction
classification
heart sounds databases
wearable cardiac monitoring
heart sounds analysis
url https://ieeexplore.ieee.org/document/8586788/
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