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

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.

Déclaration de conflit d'intérêts

I have read the journal’s policy and the authors of this manuscript have the following competing interests: D.A. is the Founder & Chief Medical Officer of AliveCor Inc. C.G, D.T., and J.X. are employees of AliveCor. G.C. is an advisor to AliveCor and holds significant stock. A.B.R., Q.L., and R.S. have no conflicts of interest. AliveCor provides unrestricted funds to Emory’s Department of Biomedical Informatics. This does not alter our adherence to PLOS ONE policies on sharing data and materials.