Early prediction of epileptic seizures using a long-term recurrent convolutional network.

Journal: Journal of neuroscience methods
Published Date:

Abstract

BACKGROUND: A seizure prediction system can detect seizures prior to their occurrence and allow clinicians to provide timely treatment for patients with epilepsy. Research on seizure prediction has progressed from signal processing analyses to machine learning. However, most prediction methods are hand-engineered and have high computational complexity, increasing the difficulty of obtaining real-time predictions. Some forecasting and early warning methods have achieved good results in the short term but have low applicability in practical situations over the long term.

Authors

  • Xiaoyan Wei
    Department of Biomedical Engineering, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, 510080, Guangdong Province, China.
  • Lin Zhou
    Guangdong Province Key Laboratory for Biotechnology Drug Candidates, School of Biosciences and Biopharmaceutics, Guangdong Pharmaceutical University Guangzhou 510006 People's Republic of China zhoulin@gdpu.edu.cn +86-20-39352151 +86-20-39352151.
  • Zhen Zhang
    School of Pharmacy, Jining Medical University, Rizhao, Shandong, China.
  • Ziyi Chen
    Department of Neurology, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080, Guangdong Province, China.
  • Yi Zhou
    Eye Center of Xiangya Hospital, Central South University, Changsha, Hunan, China.