Multi-Dimensional Enhanced Seizure Prediction Framework Based on Graph Convolutional Network

Xin Chen; Yuanjie Zheng; Changxu Dong; Sutao Song

doi:10.3389/fninf.2021.605729

Multi-Dimensional Enhanced Seizure Prediction Framework Based on Graph Convolutional Network

Front Neuroinform. 2021 Aug 19:15:605729. doi: 10.3389/fninf.2021.605729. eCollection 2021.

Authors

Xin Chen^{1

2}, Yuanjie Zheng^{1

3

4

5}, Changxu Dong¹, Sutao Song^{1

3

4

5}

Affiliations

¹ School of Information Science and Engineering at Shandong Normal University, Jinan, China.
² School of Computer Science and Engineering at Southeast University, Nanjing, China.
³ Key Lab of Intelligent Computing and Information Security in Universities of Shandong, Shandong Normal University, Jinan, China.
⁴ Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology, Shandong Normal University, Jinan, China.
⁵ Institute of Biomedical Sciences, Shandong Normal University, Jinan, China.

Abstract

In terms of seizure prediction, how to fully mine relational data information among multiple channels of epileptic EEG? This is a scientific research subject worthy of further exploration. Recently, we propose a multi-dimensional enhanced seizure prediction framework, which mainly includes information reconstruction space, graph state encoder, and space-time predictor. It takes multi-channel spatial relationship as breakthrough point. At the same time, it reconstructs data unit from frequency band level, updates graph coding representation, and explores space-time relationship. Through experiments on CHB-MIT dataset, sensitivity of the model reaches 98.61%, which proves effectiveness of the proposed model.

Keywords: epilepsy EEG signal; graph convolutional network; multichannel relationship; seizures prediction; space-time prediction.