Generalization challenges in electrocardiogram deep learning: insights from dataset characteristics and attention mechanism.

Deep learning's widespread use prompts heightened scrutiny, particularly in the biomedical fields, with a specific focus on model generalizability. This study delves into the influence of training data characteristics on the generalization performance of models, specifically in cardiac abnormality detection. Leveraging diverse electrocardiogram datasets, models are trained on subsets with varying characteristics and subsequently compared for performance.