An Attentive Spatio-Temporal Learning-Based Network for Cardiovascular Disease Diagnosis

Automated diagnosis of cardiovascular diseases (CVDs) has become an imperative need for remote or in-hospital heart monitoring. This is a challenging task because of the tenuous morphological variation of the electrocardiogram (ECG) signal across different cardiac diseases. Existing works have attempted to learn the diagnostic representation by capturing the lead-specific morphological variation of a multilead ECG signal. In this work, we have developed an attentive spatio-temporal learning network (ASTLNet) that can learn better diagnostic representation by exploiting the concurrent spatio-temporal variation of a multilead ECG signal. The ASTLNet consists of two modules, i.e., spatio-temporal representation learning (STRL) module and attentive spatio-temporal aggregation (ASTA) module. The STRL module is designed to learn the multiscale spatio-temporal representation, and the ASTA module is designed to aggregate the learned representation. Experiments on the three publicly available datasets, i.e., PTB, PTB-XL, and CPSC-2018, demonstrate that the proposed model can effectively learn the spatio-temporal variation of the ECG signal and gives superior performance compared to the state-of-the-art methods.