BackgroundAtrial fibrillation (AFib) is the most common cardiac arrhythmia associated with stroke, blood clots, read more 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.MethodsWe 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.ResultsThe 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.
ConclusionThis 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 read more 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.