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

• Type of Material: Article
• Authors: Ali Bahrami Rad, Emory University; Conner Galloway, AliveCor Inc; Daniel Treiman, AliveCor Inc; Joel Xue, AliveCor Inc; Qiao Li, Emory University; Reza Sameni, Emory University; Dave Albert, AliveCor Inc; Gari Clifford, Emory University
• Language: English
• Date: 2021-11-16
• Publisher: PUBLIC LIBRARY SCIENCE
• Publication Version: Final Published Version
• Copyright Statement: © 2021 Bahrami Rad et al
• License: Creative Commons Attribution 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1371/journal.pone.0259916
• Title of Journal or Parent Work: PLOS ONE
• Volume: 16
• Issue: 11
• Start Page: e0259916
• End Page: e0259916
• Grant/Funding Information: This research was supported by AliveCor through an unrestricted donation. It was also partially supported by the National Institute of Biomedical Imaging and Bioengineering (NIBIB) under NIH grant number R01EB030362. G.C. and Q.L. are also supported by the National Center for Advancing Translational Sciences of the National Institutes of Health under Award Number UL1TR002378. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript with the exception of AliveCor, which provided access to the Kardia algorithm outputs and labeled data used in this study. AliveCor also contributed to review the manuscript to provide clarifications on data and provide feedback on any statements that were unclear, but did not alter the content of the manuscript.
• Keywords: Science & Technology; Multidisciplinary Sciences; Science & Technology - Other Topics; EPIDEMIOLOGY
• Research Categories: Health Sciences, Medicine and Surgery; Health Sciences, Epidemiology

Abstract

Background: 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. Methods: 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. Results: 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. Conclusion: 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.