Identifying Atrial Fibrillation using Integrated Methods of 12-lead and Single-lead ECG during Normal Sinus Rhythm based on Artificial Intelligence

Abstract The use of artificial intelligence (AI) with electrocardiogram (ECG) data has shown promise in detecting atrial fibrillation (AF). Although single-lead ECGs allow convenient and simple rhythm monitoring, arrhythmias prediction using AI is limited due to single-channel utilization. We aimed to improve the capability of AI algorithms for AF identification in integrated models with 12-lead and single-lead ECG during normal sinus rhythm (NSR). A total of 7,199 single-lead mobile ECGs were acquired from 6,806 patients. Four deep learning models, i.e., EfficientNet-B4, residual neural networks (Restnet-50), Attention Restnet-50, and long short-term memory (LSTM), were employed to analyze the dataset. To develop an integrated model, an LSTM-based generative adversarial network was used to generate 12-lead ECGs from single-lead ECGs. The generated ECGs were then applied to the identification algorithm to predict AF. The integrated ECG-based model achieved an accuracy of 0.974, precision of 0.975, recall of 0.973, and F1-score of 0.974 for the training dataset with EfficientNet-B4. The area under the receiver operating characteristic curve (AUC) value for identifying AF was 0.98 with the integrated model, 0.91 with a 12-lead ECG, and 0.88 with a single-lead ECG. The integrated ECG-based model has the advantage of analyzing both standard 12-lead ECG and single-lead ECG signals. The findings underscore the potential of the integrated model in identifying AF using NSR ECGs without the limitations of relying solely on 12-lead or single-lead data. A GUI format focusing on user convenience may be used to apply the integrated ECG-based model to clinical settings.