A Spatiotemporal Graph Attention Network Based on Synchronization for Epileptic Seizure Prediction-Reference-Cited by-同舟云学术

A Spatiotemporal Graph Attention Network Based on Synchronization for Epileptic Seizure Prediction

Published:2023-02 Issue:2 Volume:27 Page:900-911
ISSN:2168-2194
Container-title:IEEE Journal of Biomedical and Health Informatics
language:
Short-container-title:IEEE J. Biomed. Health Inform.

Author:

Wang Yao¹^ORCID,Shi Yufei²,Cheng Yinlin²,He Zhipeng²,Wei Xiaoyan³^ORCID,Chen Ziyi⁴^ORCID,Zhou Yi²^ORCID

Affiliation:

1. School of Biomedical Engineering, Sun Yat-sen University, Guangzhou, China

2. Department of Medical Informatics and Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China

3. Guangzhou Women and Children's Medical CenterGuangdong Provincial Clinical Research Center for Child Health, Sun Yat-sen University, Guangzhou, China

4. Department of Neurology, First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

Province Natural Science Foundation of Guangdong

Science and Technology Innovation Special Project of Guangdong Province

Science and Technology Planning Project of Guangzhou

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Health Information Management,Electrical and Electronic Engineering,Computer Science Applications,Health Informatics

Link

http://xplorestaging.ieee.org/ielx7/6221020/10036335/09944723.pdf?arnumber=9944723

Reference54 articles.

1. Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients

2. Deep Learning Approach for Epileptic Focus Localization

3. Seizure Prediction Using Undulated Global and Local Features

4. Construction of rules for seizure prediction based on approximate entropy

5. Machine Health Monitoring Using Local Feature-Based Gated Recurrent Unit Networks

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