A Dual-Scale Lead-Separated Transformer for ECG Classification.

Abstract

This work proposes a novel dual-scale lead-separated transformer for the auxiliary diagnosis of 12-lead electrocardiograms (ECGs). We added a new structure design on the basis of traditional ECG signal processing, which led to our model with only 2.6M parameters. The output of the system is the classification results. The fixed 0.5 second ECG segments of each lead are interpreted as independent patches. Together with the reduced dimension signal, patches form a dual-scale representation. As a method to reduce interference from segments with low correlation, a lead-orthogonal attention module is proposed. Experimental results show the effectiveness and scalability of our model.Clinical relevance- Our method improves the scores of clinical 12-lead ECG classification and shows generalization ability. Our model is suitable for single-label and multi-label classification tasks on clinical 12-lead ECG and is compatible with single lead classification. The integration of clinical information can further improve the effectiveness of the model.