Enhancing single-lead ECG arrhythmia classification via multi-teacher decomposed feature distillation

When an arrhythmia occurs in the heart, all electrocardiogram (ECG) leads show evidence of it, but it is more prominent in some leads. This medical fact serves as the foundation for the knowledge distillation (KD) model proposed in this paper, which aims to enhance weak leads by leveraging information from stronger ones. The model employs single-lead signals for the student network and twelve-lead signals for the teacher network. Tucker decomposition is used in this KD model to decompose the teacher's feature maps. According to evaluations, the student model achieves an accuracy of 96.48% on the Chapman ECG dataset classification task.