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 appears...
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oai:doaj.org-article:14aa021610a9489796f1e8ffd80b806e2021-12-02T20:13:00ZAtrial fibrillation detection in outpatient electrocardiogram monitoring: An algorithmic crowdsourcing approach.1932-620310.1371/journal.pone.0259916https://doaj.org/article/14aa021610a9489796f1e8ffd80b806e2021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0259916https://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, p e0259916 (2021) |
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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. |
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<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/14aa021610a9489796f1e8ffd80b806e |
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