Objective:To analyze the accuracy of a single-lead electrocardiogram (iECG) algorithm based on intelligent wristwatch in identifying sinus tachycardia and atrial fibrillation (AF) with rapid ventricular rate.Methods:In this non-randomized control trial, 642 patients aged ≥18 years were enrolled in the General Hospital of Chinese PLA between December 15, 2020 and May 30, 2022, with sinus tachycardia or rapid ventricular rate of AF (ranging from 111 to 145 beats/min for sinus tachycardia, from 110 to 150 beats/min for rapid ventricular rate of AF, respectively). The patients wore Huawei Watch GT2 Pro smartwatches on their left wrists, and the physiological signals detected by the smartwatches in a relaxed state were used as the measured data. The iECG algorithm developed by Huawei was used for identification. Simultaneously, 12-lead electrocardiograms (12L-ECG) were performed, and two cardiologists served as the gold standard for interpretation. Three participants who did not meet the inclusion criteria were excluded based on the detection results, and a total of 639 participants were included in the study. The accuracy of the algorithm in identifying sinus tachycardia and rapid ventricular rate AF was evaluated using metrics such as recall rate, precision rate, macro F1 score for multi-class classification.Results:Among 639 subjects, there were 469 males and 170 females. There were 389 cases of sinus tachycardia and 250 cases of rapid ventricular rate AF, with a mean age of (46.53±13.32) years. The recall rate, precision rate, and F1 value of iECG algorithm in identifying sinus tachycardia was 98.7%, 99.2% and 99.0%, respectively, while it was 98.8%, 98.0% and 98.4%, respectively for AF with rapid ventricular rate. The macro F1 of AF with rapid ventricular rate and sinus tachycardia was 98.7%. The iECG based on the intelligent wristwatch showed good consistency with the corresponding 12L-ECG waveforms.Conclusion:The intelligent wristwatch-based iECG algorithm can effectively identify sinus tachycardia and rapid ventricular rate AF, demonstrating good accuracy.

Atrial fibrillation (AF) is the most common sustained cardiac arrhythmia. Early diagnosis and effective management are important to reduce atrial fibrillation-related adverse events. Photoplethysmography (PPG) is often used to assist wearables for continuous electrocardiograph monitoring, which shows its unique value. The development of PPG has provided an innovative solution to AF management. Serial studies of mobile health technology for improving screening and optimized integrated care in atrial fibrillation have explored the application of PPG in screening, diagnosing, early warning, and integrated management in patients with AF. This review summarizes the latest progress of PPG analysis based on artificial intelligence technology and mobile health in AF field in recent years, as well as the limitations of current research and the focus of future research.
Humans ; Atrial Fibrillation/therapy* ; Photoplethysmography ; Artificial Intelligence ; Electrocardiography ; Biomedical Technology