Atrial fibrillation (AF) is one of the most prevalent and clinically significant cardiac arrhythmias, and electrocardiography (ECG) is widely used for its detection. However, existing models often exhibit performance degradation when applied to unseen data due to dataset-specific biases and distributional shifts. This limited generalization remains a major obstacle to reliable clinical deployment. To address this, we propose a multi-dataset ECG classification framework designed to improve cross-dataset robustness. The model employs supervised contrastive learning and layer-wise normalization to stabilize training and mitigate the influence of domain-specific variations. The proposed approach was evaluated under a Leave-One-Dataset-Out setting, achieving an average accuracy of 97.5% and an F1-score of 89.3%. It consistently demonstrated superior performance compared with single-dataset training and naïve multi-dataset aggregation. These results indicate that the proposed framework can contribute to more stable automated AF detection across diverse clinical environments.
Kim et al. (Fri,) studied this question.