Abstract 14207: Mobile ECG Atrial Fibrillation Prediction Enhancement Powered by Standard ECG Algorithm With Artificial Intelligence

Abstract

Introduction: Artificial intelligence (AI) using electrocardiogram (ECG) enabled to predict atrial fibrillation (AF) in patients without documented AF. It needs 12-lead ECG with tremendous data learning. Mobile 1-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. Hypothesis: We aimed to enhance mobile ECG AF prediction AI algorithm integrated with 12-lead ECG deep learning model. Methods: Based on 552,372 12-lead ECG data of 318,321 patients, a statistical AF prediction model employing a deep-learning approach was constituted. The raw data of 6,792 1-lead mobile ECGs were acquired from 6,792 patients for about 1 minute at 250Hz. A statistical AF prediction model with mobile ECG employing a deep-learning approach was constituted. Resnet structure was utilized to distinguish subtle changes of the vicinity of P-wave. Both 12-lead ECG and mobile 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 1-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 1-lead mobile ECG. The area under the curve (AUC) value for predicting AF was 0.910 with 12-lead ECG, and 0.721 with mobile ECG . The predictive performance of mobile ECG was 79.7% in accuracy, 47.5% in sensitivity and 50.3% in F1-score. The AUC value of mobile ECG after applying transfer learning based on 12-lead ECG for AF prediction was increased to 0.905 with accuracy of 89.1%, sensitivity of 71.3% and F1-score of 57.7%. Conclusions: Integration with deep learning algorithm of standard 12-lead ECG significantly improved the model performance of mobile ECG AF prediction model compared to 1-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.