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

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.