Prediction of Atrial Fibrillation with Single-Lead Mobile ECG during Normal Sinus Rhythm using Deep Learning

Abstract

Abstract Background Single-lead ECG (electrocardiogram) acquisition from mobile devices provides more convenient way to identify arrhythmia. Prediction of atrial fibrillation (AF) by artificial intelligence facilitates AF screening. However, little is known about the prediction of AF with a single-lead ECG. Therefore, we proposed a method for predicting AF using single-lead mobile ECG during normal sinus rhythm (NSR). Methods Three deep learning models RNN, LSTM, and Resnet50 were used. Out of 13,509 ECGs from a total of 6,719 patients, we utilized a total of 10,287 NSR ECGs from 5,170 patients. 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. Finally, 31,767 ECG segments composed of 15,157 segments labeled as potential AF and 16,610 segments labeled as healthy were entered for final analysis. Results The ResNet50 showed recall of 79.3%, precision of 65.8%, F1-score of 71.9%, accuracy of 70.5%, and AUC of 0.79 for the prediction of AF with NSR ECGs. These scores were higher than the other two models followed by RNN at 0.75 and LSTM at 0.74. For the external validation set, ResNet50 showed an F1-score of 64.1%, recall of 68.9%, precision of 60.0%, accuracy of 63.4%, and AUC of 0.68 for the prediction of AF with NSR ECGs. Conclusion A deep learning model using a single-lead mobile ECG during NSR predicted AF at risk in future. However, further study is needed to improve the performance of deep learning models for applying in the clinical setting.