期刊
FRONTIERS IN NEUROINFORMATICS
卷 15, 期 -, 页码 -出版社
FRONTIERS MEDIA SA
DOI: 10.3389/fninf.2021.605729
关键词
epilepsy EEG signal; seizures prediction; multichannel relationship; graph convolutional network; space-time prediction
资金
- National Natural Science Foundation of China [61572300, 81871508, 61773246]
- Taishan Scholar Program of Shandong Province of China [TSHW201502038]
- Major Program of Shandong Province Natural Science Foundation [ZR2018ZB0419]
In the context of seizure prediction, a multi-dimensional enhanced framework was proposed to fully explore the relational data information among multiple channels of epileptic EEG. Through experiments on the CHB-MIT dataset, the model achieved a sensitivity of 98.61%, demonstrating the effectiveness of the proposed approach.
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.
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