Frontiers in Molecular Biosciences (Apr 2023)

Automatic interictal epileptiform discharge (IED) detection based on convolutional neural network (CNN)

  • Ling Zhang,
  • Ling Zhang,
  • Xiaolu Wang,
  • Jun Jiang,
  • Naian Xiao,
  • Naian Xiao,
  • Jiayang Guo,
  • Jiayang Guo,
  • Kailong Zhuang,
  • Kailong Zhuang,
  • Ling Li,
  • Houqiang Yu,
  • Tong Wu,
  • Ming Zheng,
  • Duo Chen

DOI
https://doi.org/10.3389/fmolb.2023.1146606
Journal volume & issue
Vol. 10

Abstract

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Clinical diagnosis of epilepsy significantly relies on identifying interictal epileptiform discharge (IED) in electroencephalogram (EEG). IED is generally interpreted manually, and the related process is very time-consuming. Meanwhile, the process is expert-biased, which can easily lead to missed diagnosis and misdiagnosis. In recent years, with the development of deep learning, related algorithms have been used in automatic EEG analysis, but there are still few attempts in IED detection. This study uses the currently most popular convolutional neural network (CNN) framework for EEG analysis for automatic IED detection. The research topic is transferred into a 4-labels classification problem. The algorithm is validated on the long-term EEG of 11 pediatric patients with epilepsy. The computational results confirm that the CNN-based model can obtain high classification accuracy, up to 87%. The study may provide a reference for the future application of deep learning in automatic IED detection.

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