  The widespread emergence of smart devices for ECG has sparked demand for
intelligent single-lead ECG-based diagnostic systems. However, it is
challenging to develop a single-lead-based ECG interpretation model for
multiple diseases diagnosis due to the lack of some key disease information. In
this work, we propose inter-lead Multi-View Knowledge Transferring of ECG
(MVKT-ECG) to boost single-lead ECG's ability for multi-label disease
diagnosis. This training strategy can transfer superior disease knowledge from
multiple different views of ECG (e.g. 12-lead ECG) to single-lead-based ECG
interpretation model to mine details in single-lead ECG signals that are easily
overlooked by neural networks. MVKT-ECG allows this lead variety as a
supervision signal within a teacher-student paradigm, where the teacher
observes multi-lead ECG educates a student who observes only single-lead ECG.
Since the mutual disease information between the single-lead ECG and muli-lead
ECG plays a key role in knowledge transferring, we present a new disease-aware
Contrastive Lead-information Transferring(CLT) to improve the mutual disease
information between the single-lead ECG and muli-lead ECG. Moreover, We modify
traditional Knowledge Distillation to multi-label disease Knowledge
Distillation (MKD) to make it applicable for multi-label disease diagnosis. The
comprehensive experiments verify that MVKT-ECG has an excellent performance in
improving the diagnostic effect of single-lead ECG.
