Accurate, Automated Detection of Atrial Fibrillation in Ambulatory Recordings

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.