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Accurate, Automated Detection of Atrial Fibrillation in Ambulatory Recordings

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Abstract

A highly accurate, automated algorithm would facilitate cost-effective screening for asymptomatic atrial fibrillation. This study analyzed a new algorithm and compared it to existing techniques. The incremental benefit of each step in refinement of the algorithm was measured, and the algorithm was compared to other methods using the Physionet atrial fibrillation and normal sinus rhythm databases. When analyzing segments of 21 RR intervals or less, the algorithm had a significantly higher area under the receiver operating characteristic curve (AUC) than the other algorithms tested. At analysis segment sizes of up to 101 RR intervals, the algorithm continued to have a higher AUC than any of the other methods tested, although the difference from the second best other algorithm was no longer significant, with an AUC of 0.9992 with a 95% confidence interval (CI) of 0.9986–0.9998, vs. 0.9986 (CI 0.9978–0.9994). With identical per-subject sensitivity, per-subject specificity of the current algorithm was superior to the other tested algorithms even at 101 RR intervals, with no false positives (CI 0.0–0.8%) vs. 5.3% false positives for the second best algorithm (CI 3.4–7.9%). The described algorithm shows great promise for automated screening for atrial fibrillation by reducing false positives requiring manual review, while maintaining high sensitivity.

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Acknowledgments

This work was supported in part by the National Heart, Lung and Blood Institute at the National Institutes of Health [R41 HL 090106-01A1].

Conflict of Interest

David Linker holds patents on the methods of automated atrial fibrillation detection described, and is a founder and board member of, stockholder in, and consultant to Cardiac Insight, Inc., a manufacturer of a long-term ambulatory ECG monitor, which has licensed the patents.

Human and Animal Studies

This study used only historical, de-identified ambulatory monitor recordings that are publicly available, and is therefore exempt. No animals were used in this research.

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Correspondence to David T. Linker.

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Associate Editor Ajit P. Yoganathan oversaw the review of this article.

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Linker, D.T. Accurate, Automated Detection of Atrial Fibrillation in Ambulatory Recordings. Cardiovasc Eng Tech 7, 182–189 (2016). https://doi.org/10.1007/s13239-016-0256-z

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  • DOI: https://doi.org/10.1007/s13239-016-0256-z

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