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首页> 外文会议>International Conference on Computer Vision, Image and Deep Learning >Epilepsy Classification for Mining Deeper Relationships between EEG Channels based on GCN
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Epilepsy Classification for Mining Deeper Relationships between EEG Channels based on GCN

机译：基于GCN的EEG渠道挖掘更深层次关系的癫痫分类

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Epilepsy is a brain disorder caused by abnormal discharges of neurons in brain. It is one of the most commonly studied disorders in neurology. The research of epilepsy electroencephalogram (EEG) has become a hot research topic. We find that in epilepsy EEG detection task, many previous methods focused on directly collecting the data of each channel, but these methods seldom analyse relationships between signals. Therefore, we propose the Epilepsy EEG Graph Convolutional Network EGCN, which makes full use of correlations between channels to deeply mine data information. We specifically design 5-layer graph convolutional network structure for classification of healthy and epileptic patients. The method is applied to public data set (Boon and CHB-MIT) to establish a reasonable classification model. And we compare it with some advanced algorithms. The experimental results show that the E-GCN method is superior to many existing methods in classification accuracy. In brief, the E-GCN method can be effectively used in classification and detection for epilepsy. This provides new ideas for colleagues, who study epilepsy EEG. In addition, this also provides richer experience for diagnosis of epilepsy.
机译：癫痫是由脑中神经元异常引起的脑障碍。它是神经内科最常见的疾病之一。癫痫脑电图（EEG）的研究已成为一个热门研究课题。我们发现，在癫痫脑电图检测任务中，许多以前的方法专注于直接收集每个通道的数据，但这些方法很少分析信号之间的关系。因此，我们提出了癫痫eeg图卷积网络EGCN，它充分利用了信道之间的相关性来深入地挖掘了矿山数据信息。我们专门设计5层图形卷积网络结构，用于健康和癫痫患者的分类。该方法应用于公共数据集（Boon和CHB-MIT）以建立合理的分类模型。我们将其与一些高级算法进行比较。实验结果表明，E-GCN方法优于分类精度的许多现有方法。简而言之，E-GCN方法可以有效地用于癫痫的分类和检测。这为研究癫痫脑电图的同事提供了新的想法。此外，这还提供富含癫痫的诊断体验。

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