Outcome Prediction in Postanoxic Coma With Deep Learning.

Journal: Critical care medicine
PMID: 31162190

Abstract

OBJECTIVES: Visual assessment of the electroencephalogram by experienced clinical neurophysiologists allows reliable outcome prediction of approximately half of all comatose patients after cardiac arrest. Deep neural networks hold promise to achieve similar or even better performance, being more objective and consistent.

Authors

  • Marleen C Tjepkema-Cloostermans
    Department of Clinical Neurophysiology and Neurology, Medisch Spectrum Twente, Enschede, The Netherlands. Electronic address: m.tjepkema-cloostermans@mst.nl.
  • Catarina da Silva Lourenço
    Department of Clinical Neurophysiology, Technical Medical Centre, University of Twente, Enschede, The Netherlands.
  • Barry J Ruijter
    Clinical Neurophysiology Group, University of Twente, Enschede, Netherlands.
  • Selma C Tromp
    Department of Clinical Neurophysiology, St. Antonius Hospital, Nieuwegein, The Netherlands.
  • Gea Drost
    Department of Neurology and Neurosurgery, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
  • Francois H M Kornips
    Department of Neurology, VieCuri Medical Center, Venlo, The Netherlands.
  • Albertus Beishuizen
    Intensive Care Center, Medisch Spectrum Twente, Enschede, The Netherlands.
  • Frank H Bosch
    Department of Intensive Care, Rijnstate hospital, Arnhem, The Netherlands.
  • Jeannette Hofmeijer
    Clinical Neurophysiology Group, University of Twente, Enschede, Netherlands; Department of Neurology, Rijnstate Hospital, Arnhem, 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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