Machine learning algorithms for predicting atrial fibrillation using single-lead data derived from 12-lead ECGs.

Background: Wearable electrocardiogram (ECG) monitoring devices that utilize single-lead ECG technology have become valuable tools for identifying paroxysmal atrial fibrillation (AF). This study aimed to develop a machine learning (ML) algorithm to predict new-onset AF by training it on single-lead data extracted from 12-lead ECG recordings. Methods and results: Patients who underwent 12-lead ECG between January 2010 and December 2021 were classified into two groups based on a review of their medical records and diagnostic codes: the AF group and the normal group. An ML model was created using single-lead ECG data, excluding three augmented leads, and incorporating 60 calculated statistical variables for each of the remaining single leads. The model's performance was assessed using several metrics, including the area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, accuracy, and F1 score. We trained the ML model on 248,612 ECGs collected from 106,606 patients, of whom 11,810 had definite AF. Among the single-lead machine learning models developed from each of the nine individual leads, lead I demonstrated the best performance. The AUROC of the single-lead ECG ML model using lead I was 0.801, while the AUROC of the 12-lead ECG ML model was 0.816. Conclusion: The single-lead ECG ML model has shown promise in predicting new-onset atrial fibrillation (AF), particularly with lead I. Its performance is comparable to that of the 12-lead model.