Deep learning-based classification and segmentation of interictal epileptiform discharges using multichannel electroencephalography.

Journal: Epilepsia
Published Date: May 24, 2025

Abstract

OBJECTIVE: This study was undertaken to develop a deep learning framework that can classify and segment interictal epileptiform discharges (IEDs) in multichannel electroencephalographic (EEG) recordings with high accuracy, preserving both spatial information and interchannel interactions.

Authors

  • Yulin Sun
    School of Economics and Management, Beijing Jiaotong University, 100091, Beijing, China. Electronic address: l24125595@bjtu.edu.cn.
  • Min Guan
    Department of Neurology, First Affiliated Hospital of Jinan University, Guangzhou, China.
  • Xun Chen
    Department of Electronic Science and Technology, University of Science and Technology of China, Hefei 230027, China. xunchen@ece.ubc.ca.
  • Fengling Feng
    Department of Neurology, First Affiliated Hospital of Jinan University, Guangzhou, China.
  • Runnan He
    Harbin Institute of Technology, Harbin, China.
  • Lian Huang
    Department of Neurology, First Affiliated Hospital of Jinan University, Guangzhou, China.
  • Xiaoguang Tong
    Department of Neurosurgery, Tianjin Huanhu Hospital, Tianjin, China.
  • Huan Zhou
    The School of Big Data and Artificial Intelligence, Anhui Xinhua University, Hefei, China.
  • Xiuyun Liu
  • Dong Ming
    Institute of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.

Keywords

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