An Attentive Spatio-Temporal Learning-Based Network for Cardiovascular Disease Diagnosis
提出一种注意力时空学习网络ASTLNet，通过同时学习多导联心电图的时空变化来提升心血管疾病自动诊断的准确性，在三个公开数据集上优于现有方法。
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.