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This study aimed to evaluate whether quantitative analysis of wrist photoplethysmography (PPG) could detect atrial fibrillation (AF). Continuous electrocardiograms recorded using an electrophysiology recording system and PPG obtained using a wrist-worn smartwatch were simultaneously collected from patients undergoing catheter ablation or electrical cardioversion. PPG features were extracted from 10, 25, 40, and 80 heartbeats of the split segments. Machine learning with a support vector machine and random forest approach were used to detect AF. A total of 116 patients were evaluated. We annotated &gt; 117 h of PPG. A total of 6475 and 3957 segments of 25-beat pulse-to-pulse intervals (PPIs) were annotated as AF and sinus rhythm, respectively. The accuracy of the 25 PPIs yielded a test area under the receiver operating characteristic curve (AUC) of 0.9676, which was significantly better than the AUC for the 10 PPIs (0.9453; P &lt; .001). PPGs obtained from another 38 patients with frequent premature ventricular/atrial complexes (PVCs/PACs) were used to evaluate the impact of other arrhythmias on diagnostic accuracy. The new AF detection algorithm achieved an AUC of 0.9680. The appropriate data length of PPG for optimizing the PPG analytics program was 25 heartbeats. Algorithm modification using a machine learning approach shows robustness to PVCs/PACs.

Impact of recording length and other arrhythmias on atrial fibrillation detection from wrist photoplethysmogram using smartwatches