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.

翻译：随着图神经网络的发展，其在心电图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矩阵生成邻接矩阵，并应用图神经网络进行心律失常分类。所提出的模型与文献中现有方法进行了比较。结果表明，所有心律失常类别的精确率和召回率均超过50%，表明该方法可视为一种有效的心律失常分类途径。