Atrial fibrillation detection in outpatient electrocardiogram monitoring: An algorithmic crowdsourcing approach

<h4>Background</h4> Atrial fibrillation (AFib) is the most common cardiac arrhythmia associated with stroke, blood clots, heart failure, coronary artery disease, and/or death. Multiple methods have been proposed for AFib detection, with varying performances, but no single approach appear...

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Autores principales:	Ali Bahrami Rad, Conner Galloway, Daniel Treiman, Joel Xue, Qiao Li, Reza Sameni, Dave Albert, Gari D. Clifford
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/7389bed69b414fe7adae2393cf20ecec
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spelling	oai:doaj.org-article:7389bed69b414fe7adae2393cf20ecec2021-11-25T06:13:56ZAtrial fibrillation detection in outpatient electrocardiogram monitoring: An algorithmic crowdsourcing approach1932-6203https://doaj.org/article/7389bed69b414fe7adae2393cf20ecec2021-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8594842/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Background</h4> Atrial fibrillation (AFib) is the most common cardiac arrhythmia associated with stroke, blood clots, heart failure, coronary artery disease, and/or death. Multiple methods have been proposed for AFib detection, with varying performances, but no single approach appears to be optimal. We hypothesized that each state-of-the-art algorithm is appropriate for different subsets of patients and provides some independent information. Therefore, a set of suitably chosen algorithms, combined in a weighted voting framework, will provide a superior performance to any single algorithm. <h4>Methods</h4> We investigate and modify 38 state-of-the-art AFib classification algorithms for a single-lead ambulatory electrocardiogram (ECG) monitoring device. All algorithms are ranked using a random forest classifier and an expert-labeled training dataset of 2,532 recordings. The seven top-ranked algorithms are combined by using an optimized weighting approach. <h4>Results</h4> The proposed fusion algorithm, when validated on a separate test dataset consisting of 4,644 recordings, resulted in an area under the receiver operating characteristic (ROC) curve of 0.99. The sensitivity, specificity, positive-predictive-value (PPV), negative-predictive-value (NPV), and F1-score of the proposed algorithm were 0.93, 0.97, 0.87, 0.99, and 0.90, respectively, which were all superior to any single algorithm or any previously published. <h4>Conclusion</h4> This study demonstrates how a set of well-chosen independent algorithms and a voting mechanism to fuse the outputs of the algorithms, outperforms any single state-of-the-art algorithm for AFib detection. The proposed framework is a case study for the general notion of crowdsourcing between open-source algorithms in healthcare applications. The extension of this framework to similar applications may significantly save time, effort, and resources, by combining readily existing algorithms. It is also a step toward the democratization of artificial intelligence and its application in healthcare.Ali Bahrami RadConner GallowayDaniel TreimanJoel XueQiao LiReza SameniDave AlbertGari D. CliffordPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Ali Bahrami Rad Conner Galloway Daniel Treiman Joel Xue Qiao Li Reza Sameni Dave Albert Gari D. Clifford Atrial fibrillation detection in outpatient electrocardiogram monitoring: An algorithmic crowdsourcing approach
description	<h4>Background</h4> Atrial fibrillation (AFib) is the most common cardiac arrhythmia associated with stroke, blood clots, heart failure, coronary artery disease, and/or death. Multiple methods have been proposed for AFib detection, with varying performances, but no single approach appears to be optimal. We hypothesized that each state-of-the-art algorithm is appropriate for different subsets of patients and provides some independent information. Therefore, a set of suitably chosen algorithms, combined in a weighted voting framework, will provide a superior performance to any single algorithm. <h4>Methods</h4> We investigate and modify 38 state-of-the-art AFib classification algorithms for a single-lead ambulatory electrocardiogram (ECG) monitoring device. All algorithms are ranked using a random forest classifier and an expert-labeled training dataset of 2,532 recordings. The seven top-ranked algorithms are combined by using an optimized weighting approach. <h4>Results</h4> The proposed fusion algorithm, when validated on a separate test dataset consisting of 4,644 recordings, resulted in an area under the receiver operating characteristic (ROC) curve of 0.99. The sensitivity, specificity, positive-predictive-value (PPV), negative-predictive-value (NPV), and F1-score of the proposed algorithm were 0.93, 0.97, 0.87, 0.99, and 0.90, respectively, which were all superior to any single algorithm or any previously published. <h4>Conclusion</h4> This study demonstrates how a set of well-chosen independent algorithms and a voting mechanism to fuse the outputs of the algorithms, outperforms any single state-of-the-art algorithm for AFib detection. The proposed framework is a case study for the general notion of crowdsourcing between open-source algorithms in healthcare applications. The extension of this framework to similar applications may significantly save time, effort, and resources, by combining readily existing algorithms. It is also a step toward the democratization of artificial intelligence and its application in healthcare.
format	article
author	Ali Bahrami Rad Conner Galloway Daniel Treiman Joel Xue Qiao Li Reza Sameni Dave Albert Gari D. Clifford
author_facet	Ali Bahrami Rad Conner Galloway Daniel Treiman Joel Xue Qiao Li Reza Sameni Dave Albert Gari D. Clifford
author_sort	Ali Bahrami Rad
title	Atrial fibrillation detection in outpatient electrocardiogram monitoring: An algorithmic crowdsourcing approach
title_short	Atrial fibrillation detection in outpatient electrocardiogram monitoring: An algorithmic crowdsourcing approach
title_full	Atrial fibrillation detection in outpatient electrocardiogram monitoring: An algorithmic crowdsourcing approach
title_fullStr	Atrial fibrillation detection in outpatient electrocardiogram monitoring: An algorithmic crowdsourcing approach
title_full_unstemmed	Atrial fibrillation detection in outpatient electrocardiogram monitoring: An algorithmic crowdsourcing approach
title_sort	atrial fibrillation detection in outpatient electrocardiogram monitoring: an algorithmic crowdsourcing approach
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/7389bed69b414fe7adae2393cf20ecec
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Atrial fibrillation detection in outpatient electrocardiogram monitoring: An algorithmic crowdsourcing approach

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