Deep learning for detection of focal epileptiform discharges from scalp EEG recordings.

Journal: Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
Published Date: Jul 9, 2018

Abstract

OBJECTIVE: Visual assessment of the EEG still outperforms current computer algorithms in detecting epileptiform discharges. Deep learning is a promising novel approach, being able to learn from large datasets. Here, we show pilot results of detecting epileptiform discharges using deep neural networks.

Authors

  • Marleen C Tjepkema-Cloostermans
    Department of Clinical Neurophysiology and Neurology, Medisch Spectrum Twente, Enschede, The Netherlands. Electronic address: m.tjepkema-cloostermans@mst.nl.
  • Rafael C V de Carvalho
    Department of Clinical Neurophysiology and Neurology, Medisch Spectrum Twente, Enschede, The Netherlands; Department of Clinical Neurophysiology, University of Twente, Enschede, The Netherlands.
  • Michel J A M van Putten
    Department of Clinical Neurophysiology, MIRA-Institute for Biomedical Technology and Technical Medicine, University of Twente & Medisch Spectrum Twente, Enschede, The Netherlands. m.j.a.m.vanputten@utwente.nl.

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