Does ECGNN improve the accuracy of automated ECG abnormal identification compared to existing methods?
The proposed ECGNN model, which incorporates relationships between leads using a graph neural network, effectively enhances abnormal recognition in 12-lead ECGs.
The 12-lead Electrocardiography (ECG) is one of the most commonly used diagnostic tools for cardiovascular disease. Widely available ECG databases and deep learning algorithms present an opportunity to substantially improve the accuracy and scalability of automated ECG abnormal identification. However, existing methods mainly model leads individually and then aggregate them for prediction, ignoring the relationship between leads, which is an important diagnostic reference for clinicians. In this paper, we propose a novel model, called ECGNN, which main consists a feature extractor backbone and a graph neural network module. The feature extractor backbone is a neural network used to extract features of ECG signal for subsequent prediction and initialization of the graph nodes. Specifically, the proposed graph neural network module combines graph convolution and graph pooling into a unified module to generate hierarchical representations of graphs and can be integrated into various feature extractor backbones. Experimental results on two largescale 12-lead ECG databases demonstrate the effectiveness of our proposed model.
Zhao et al. (Tue,) studied this question.