Department of Biostatistics, School of Health, Kermanshah University of Medical Sciences, Kermanshah 6715847141, Iran.
Department of Computer Engineering and Information Technology, Razi University, Kermanshah 6714967346, Iran.
Int J Environ Res Public Health. 2022 Aug 28;19(17):10707. doi: 10.3390/ijerph191710707.
Cardiovascular diseases, like arrhythmia, as the leading causes of death in the world, can be automatically diagnosed using an electrocardiogram (ECG). The ECG-based diagnostic has notably resulted in reducing human errors. The main aim of this study is to increase the accuracy of arrhythmia diagnosis and classify various types of arrhythmias in individuals (suffering from cardiovascular diseases) using a novel graph convolutional network (GCN) benefitting from mutual information (MI) indices extracted from the ECG leads. In this research, for the first time, the relationships of 12 ECG leads measured using MI as an adjacency matrix were illustrated by the developed GCN and included in the ECG-based diagnostic method. Cross-validation methods were applied to select both training and testing groups. The proposed methodology was validated in practice by applying it to the large ECG database, recently published by Chapman University. The GCN-MI structure with 15 layers was selected as the best model for the selected database, which illustrates a very high accuracy in classifying different types of rhythms. The classification indicators of sensitivity, precision, specificity, and accuracy for classifying heart rhythm type, using GCN-MI, were computed as 98.45%, 97.89%, 99.85%, and 99.71%, respectively. The results of the present study and its comparison with other studies showed that considering the MI index to measure the relationship between cardiac leads has led to the improvement of GCN performance for detecting and classifying the type of arrhythmias, in comparison to the existing methods. For example, the above classification indicators for the GCN with the identity adjacency matrix (or GCN-Id) were reported to be 68.24%, 72.83%, 95.24%, and 92.68%, respectively.
心血管疾病，如心律失常，作为世界上的主要死亡原因，可以通过心电图（ECG）自动诊断。基于 ECG 的诊断显著减少了人为错误。本研究的主要目的是提高心律失常诊断的准确性，并使用受益于从 ECG 导联提取的互信息（MI）指数的新型图卷积网络（GCN）对个体（患有心血管疾病）中的各种类型的心律失常进行分类。在这项研究中，首次使用 MI 作为邻接矩阵来表示 12 个 ECG 导联之间的关系，开发的 GCN 将其包括在基于 ECG 的诊断方法中。交叉验证方法用于选择训练组和测试组。该方法应用于查普曼大学最近发布的大型 ECG 数据库进行实践验证。选择具有 15 层的 GCN-MI 结构作为选定数据库的最佳模型，该模型在分类不同类型的节律方面表现出非常高的准确性。使用 GCN-MI 对心律类型进行分类的灵敏度、精度、特异性和准确性的分类指标分别计算为 98.45%、97.89%、99.85%和 99.71%。本研究的结果及其与其他研究的比较表明，与现有的方法相比，考虑 MI 指数来测量心脏导联之间的关系，可提高 GCN 检测和分类心律失常类型的性能。例如，对于具有身份邻接矩阵的 GCN（或 GCN-Id），上述分类指标分别报告为 68.24%、72.83%、95.24%和 92.68%。
