A deep learning framework for arrhythmia classification: mitigating data leakage and class imbalance in ECG analysis.
Accurate and timely identification of cardiac abnormalities remains essential for reducing mortality associated with life-threatening cardiac arrhythmias. However, automated diagnostic systems often struggle with imbalanced clinical data and limited generalizability. In this work, we propose an efficient deep learning...