Complexity-based graph convolutional neural network for epilepsy diagnosis in normal, acute, and chronic stages.

Zheng, Shiming; Zhang, Xiaopei; Song, Panpan; Hu, Yue; Gong, Xi; Peng, Xiaoling

Zheng, Shiming; Zhang, Xiaopei; Song, Panpan; Hu, Yue; Gong, Xi; Peng, Xiaoling.

Afiliação

Zheng S; Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science, BNU-HKBU United International College, Zhuhai, China.
Zhang X; Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science, BNU-HKBU United International College, Zhuhai, China.
Song P; Department of Neurology, Children's Hospital of Chongqing Medical University, Chongqing, China.
Hu Y; Department of Neurology, Children's Hospital of Chongqing Medical University, Chongqing, China.
Gong X; Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science, BNU-HKBU United International College, Zhuhai, China.
Peng X; Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science, BNU-HKBU United International College, Zhuhai, China.

Front Comput Neurosci ; 17: 1211096, 2023.

Article em En | MEDLINE | ID: mdl-37841676

Palavras-chave

EEG complexity measures; chronic stage; entropy; epilepsy diagnosis; graph convolutional neural network

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Front Comput Neurosci Ano de publicação: 2023 Tipo de documento: Article País de afiliação: China País de publicação: Suíça

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