用心电信号特征相关性构建图结构，通过图神经网络实现心律失常分类。
Arrhythmia Classification Using Graph Neural Networks Based on Correlation Matrix
- 基于提取特征的相关性矩阵生成图邻接矩阵，输入图神经网络
- 所有心律失常类别的精确率和召回率均超50%
- 适合对医学信号图建模感兴趣的开发者或临床研究者
随着图神经网络的发展，其在心电图（ECG）信号分析中的应用日益受到关注。本研究利用提取特征的相关性矩阵生成邻接矩阵，并将其输入图神经网络进行心律失常分类。所提出的方法与文献中现有方法进行了对比。结果表明，所有心律失常类别下的精确率和召回率均超过50%，说明该方法可作为心律失常分类的一种有效途径。
原文摘要 · Abstract (English)
With the advancements in graph neural network, there has been increasing interest in applying this network to ECG signal analysis. In this study, we generated an adjacency matrix using correlation matrix of extracted features and applied a graph neural network to classify arrhythmias. The proposed model was compared with existing approaches from the literature. The results demonstrated that precision and recall for all arrhythmia classes exceeded 50%, suggesting that this method can be considered an approach for arrhythmia classification.
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