A multi-context learning approach for EEG epileptic seizure detection.

Journal: BMC systems biology
Published Date: Nov 22, 2018

Abstract

BACKGROUND: Epilepsy is a neurological disease characterized by unprovoked seizures in the brain. The recent advances in sensor technologies allow researchers to analyze the collected biological records to improve the treatment of epilepsy. Electroencephalogram (EEG) is the most commonly used biological measurement to effectively capture the abnormalities of different brain areas during the EEG seizures. To avoid manual visual inspection from long-term EEG readings, automatic epileptic EEG seizure detection has become an important research issue in bioinformatics.

Authors

  • Ye Yuan
    School of Artificial Intelligence and Automation, MOE Key Lab of Intelligent Control and Image Processing, Huazhong University of Science and Technology, Wuhan 430074, China.
  • Guangxu Xun
    Department of Computer Science and Engineering, SUNY at Buffalo, Buffalo, USA. guangxux@buffalo.edu.
  • Kebin Jia
    College of Information and Communication Engineering, Beijing University of Technology, Beijing, China. kebinj@bjut.edu.cn.
  • Aidong Zhang
    Department of Computer Science and Engineering, SUNY at Buffalo, Buffalo, USA.

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