Mobile single-lead ECG atrial fibrillation prediction enhancement integrated by standard ECG algorithm with Deep learning model

Abstract Background Artificial intelligence (AI) using electrocardiogram (ECG) enabled to predict atrial fibrillation (AF) in patients without documented AF. Mobile single-lead ECG is more convenient to surveil cardiac rhythm with simple measurement. However, AI-enabled arrhythmia predictability by mobile ECG is limited due to single channel utilization and longer duration for arrhythmia diagnosis. We aimed to enhance mobile single-lead ECG AF prediction AI algorithm integrated with 12-lead ECG using deep learning model. Method Based on 552,372 12-lead ECG data of 318,321 patients, a statistical AF prediction model employing a deep-learning approach was constituted. Out of single-lead 13,509 ECGs from a total of 6,719 patients, we utilized a total of 10,287 normal sinus rhythm ECGs from 5,170 patients. Resnet structure was utilized to distinguish subtle changes of the vicinity of P-wave. Single-lead mobile ECGs were adjusted with noise filtering and segmented every 10 seconds. A random under-sampling was applied to reduce bias from data imbalance. Both 12-lead ECG and single-lead ECG were allocated to training, validation, testing datasets in a 6:2:2 ratio. Then, we conducted transfer learning using the standard 12-lead ECG’s deep learning model to improve performance of single-lead mobile ECG deep learning model. Results AF was annotated in 26,541 (4.8%) with 12-lead ECG whereas 1,443 (21.2%) with single-lead mobile ECG. The area under the curve (AUC) value for predicting AF was 0.910 with 12-lead ECG, and 0.742 with mobile ECG. The predictive performance of mobile ECG was 67.5% in accuracy, 64.2% in sensitivity and 66.8% in F1-score. The AUC value of mobile ECG after applying transfer learning based on 12-lead ECG for AF prediction was increased to 0.790 with accuracy of 73.8%, sensitivity of 65.5% and F1-score of 71.0%. Conclusion Integration with deep learning algorithm of standard 12-lead ECG significantly improved the model performance of single-lead ECG AF prediction model compared to single-lead mobile ECG only based model. Easy application of mobile ECG with enhanced AF predictability might serve a more convenient method as a pre-emptive assistive tool to provide probabilistic prediction for PAF screening rather than 12-lead ECG.