Abstract
AIMS: Smartwatch electrocardiograms (SW ECGs) have been identified as a non-invasive solution to assess abnormal heart rhythm, especially atrial arrhythmias (AAs) that are related to stroke risk. However, the performance of these tools is limited and could be improved with the use of deep neural network (DNN) algorithms, particularly for specific populations encountered in clinical cardiology practice. METHODS AND RESULTS: A total of 400 patients from the electrophysiology department of one tertiary care hospital were included in two similar clinical trials (respectively, 200 patients per study). Simultaneous ECGs were recorded with the watch and a 12-lead recording system during consultation or before and after an electrophysiology procedure if any. The SW ECGs were processed by using the DNN and with the Apple watch ECG software (Apple app). Corresponding 12-lead ECGs (12L ECGs) were adjudicated by an expert electrophysiologist. The performance of the DNN was assessed vs. the expert interpretation of the 12L ECG, and inconclusive rates were reported. Overall, the DNN and the Apple app presented, respectively, a sensitivity of 91% [95% confidence interval (CI) 85-95%] and 61% (95% CI 44-75%) with a specificity of 95% (95% CI 91-97%) and 97% (95% CI 93-99%) when compared with the physician 12L ECG interpretation. The DNN was able to provide a diagnosis on 99% of ECGs, while the Apple app was able to classify only 78% of strips (22% of inconclusive diagnosis). CONCLUSION: In this study, by including patients from a cardiology department, a DNN-based algorithm applied to an SW ECG provided an accurate diagnosis for AA detection on virtually all tracings, outperforming the SW algorithm.