Photoplethysmography based atrial fibrillation detection: a review

Abstract Atrial fibrillation (AF) is a cardiac rhythm disorder associated with increased morbidity and mortality. It is the leading risk factor for cardioembolic stroke and its early detection is crucial in both primary and secondary stroke prevention. Continuous monitoring of cardiac rhythm is toda...

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Autores principales:	Tania Pereira, Nate Tran, Kais Gadhoumi, Michele M. Pelter, Duc H. Do, Randall J. Lee, Rene Colorado, Karl Meisel, Xiao Hu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/1626c2a91f4e432c839ebb780840285b
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spelling	oai:doaj.org-article:1626c2a91f4e432c839ebb780840285b2021-12-02T14:29:12ZPhotoplethysmography based atrial fibrillation detection: a review10.1038/s41746-019-0207-92398-6352https://doaj.org/article/1626c2a91f4e432c839ebb780840285b2020-01-01T00:00:00Zhttps://doi.org/10.1038/s41746-019-0207-9https://doaj.org/toc/2398-6352Abstract Atrial fibrillation (AF) is a cardiac rhythm disorder associated with increased morbidity and mortality. It is the leading risk factor for cardioembolic stroke and its early detection is crucial in both primary and secondary stroke prevention. Continuous monitoring of cardiac rhythm is today possible thanks to consumer-grade wearable devices, enabling transformative diagnostic and patient management tools. Such monitoring is possible using low-cost easy-to-implement optical sensors that today equip the majority of wearables. These sensors record blood volume variations—a technology known as photoplethysmography (PPG)—from which the heart rate and other physiological parameters can be extracted to inform about user activity, fitness, sleep, and health. Recently, new wearable devices were introduced as being capable of AF detection, evidenced by large prospective trials in some cases. Such devices would allow for early screening of AF and initiation of therapy to prevent stroke. This review is a summary of a body of work on AF detection using PPG. A thorough account of the signal processing, machine learning, and deep learning approaches used in these studies is presented, followed by a discussion of their limitations and challenges towards clinical applications.Tania PereiraNate TranKais GadhoumiMichele M. PelterDuc H. DoRandall J. LeeRene ColoradoKarl MeiselXiao HuNature PortfolioarticleComputer applications to medicine. Medical informaticsR858-859.7ENnpj Digital Medicine, Vol 3, Iss 1, Pp 1-12 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Tania Pereira Nate Tran Kais Gadhoumi Michele M. Pelter Duc H. Do Randall J. Lee Rene Colorado Karl Meisel Xiao Hu Photoplethysmography based atrial fibrillation detection: a review
description	Abstract Atrial fibrillation (AF) is a cardiac rhythm disorder associated with increased morbidity and mortality. It is the leading risk factor for cardioembolic stroke and its early detection is crucial in both primary and secondary stroke prevention. Continuous monitoring of cardiac rhythm is today possible thanks to consumer-grade wearable devices, enabling transformative diagnostic and patient management tools. Such monitoring is possible using low-cost easy-to-implement optical sensors that today equip the majority of wearables. These sensors record blood volume variations—a technology known as photoplethysmography (PPG)—from which the heart rate and other physiological parameters can be extracted to inform about user activity, fitness, sleep, and health. Recently, new wearable devices were introduced as being capable of AF detection, evidenced by large prospective trials in some cases. Such devices would allow for early screening of AF and initiation of therapy to prevent stroke. This review is a summary of a body of work on AF detection using PPG. A thorough account of the signal processing, machine learning, and deep learning approaches used in these studies is presented, followed by a discussion of their limitations and challenges towards clinical applications.
format	article
author	Tania Pereira Nate Tran Kais Gadhoumi Michele M. Pelter Duc H. Do Randall J. Lee Rene Colorado Karl Meisel Xiao Hu
author_facet	Tania Pereira Nate Tran Kais Gadhoumi Michele M. Pelter Duc H. Do Randall J. Lee Rene Colorado Karl Meisel Xiao Hu
author_sort	Tania Pereira
title	Photoplethysmography based atrial fibrillation detection: a review
title_short	Photoplethysmography based atrial fibrillation detection: a review
title_full	Photoplethysmography based atrial fibrillation detection: a review
title_fullStr	Photoplethysmography based atrial fibrillation detection: a review
title_full_unstemmed	Photoplethysmography based atrial fibrillation detection: a review
title_sort	photoplethysmography based atrial fibrillation detection: a review
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/1626c2a91f4e432c839ebb780840285b
work_keys_str_mv	AT taniapereira photoplethysmographybasedatrialfibrillationdetectionareview AT natetran photoplethysmographybasedatrialfibrillationdetectionareview AT kaisgadhoumi photoplethysmographybasedatrialfibrillationdetectionareview AT michelempelter photoplethysmographybasedatrialfibrillationdetectionareview AT duchdo photoplethysmographybasedatrialfibrillationdetectionareview AT randalljlee photoplethysmographybasedatrialfibrillationdetectionareview AT renecolorado photoplethysmographybasedatrialfibrillationdetectionareview AT karlmeisel photoplethysmographybasedatrialfibrillationdetectionareview AT xiaohu photoplethysmographybasedatrialfibrillationdetectionareview
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Photoplethysmography based atrial fibrillation detection: a review

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