Evaluation of an algorithm‐guided photoplethysmography for atrial fibrillation burden using a smartwatch

Abstract Background Wearable devices based on the PPG algorithm can detect atrial fibrillation (AF) effectively. However, further investigation of its application on long‐term, continuous monitoring of AF burden is warranted. Method The performance of a smartwatch with continuous photoplethysmography (PPG) and PPG‐based algorithms for AF burden estimation was evaluated in a prospective study enrolling AF patients admitted to Beijing Anzhen Hospital for catheter ablation from September to November 2022. A continuous Electrocardiograph patch (ECG) was used as the reference device to validate algorithm performance for AF detection in 30‐s intervals. Results A total of 578669 non‐overlapping 30‐s intervals for PPG and ECG each from 245 eligible patients were generated. An interval‐level sensitivity of PPG was 96.3% (95% CI 96.2%–96.4%), and specificity was 99.5% (95% CI 99.5%–99.6%) for the estimation of AF burden. AF burden estimation by PPG was highly correlated with AF burden calculated by ECG via Pearson correlation coefficient (R2 = 0.996) with a mean difference of ‐0.59 (95% limits of agreement, ‐7.9% to 6.7%). The subgroup study showed the robust performance of the algorithm in different subgroups, including heart rate and different hours of the day. Conclusion Our results showed the smartwatch with an algorithm‐based PPG monitor has good accuracy and stability in continuously monitoring AF burden compared with ECG patch monitors, indicating its potential for diagnosing and managing AF.