March 23, 2023IEEE Transactions on Systems Man and Cybernetics Systems43 citations

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

Q: What is the clinical evidence from this study?

Study design: Other. Population: Cardiovascular diseases. Intervention: Attentive spatio-temporal learning network (ASTLNet) vs. State-of-the-art methods. Primary outcome: Diagnostic performance.

DJDebasish JyotishiIndian Institute of Technology Guwahati SDSamarendra DandapatIndian Institute of Technology Guwahati

Key Result

The proposed attentive spatio-temporal learning network (ASTLNet) effectively learned the spatio-temporal variation of multilead ECG signals and demonstrated superior diagnostic performance compared to state-of-the-art methods.

Structured PICO

Population

Multilead ECG signals from three publicly available datasets (PTB, PTB-XL, and CPSC-2018) for cardiovascular disease diagnosis

Intervention

Attentive spatio-temporal learning network (ASTLNet)

Comparator

State-of-the-art methods

Outcome

Diagnostic performance

The proposed ASTLNet deep learning model improves automated cardiovascular disease diagnosis from multilead ECGs by exploiting concurrent spatio-temporal variations.

Abstract

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.

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