Predicting Neurological Outcome From Electroencephalogram Dynamics in Comatose Patients After Cardiac Arrest With Deep Learning.

Journal: IEEE transactions on bio-medical engineering
PMID: 34962860

Abstract

OBJECTIVE: Most cardiac arrest patients who are successfully resuscitated are initially comatose due to hypoxic-ischemic brain injury. Quantitative electroencephalography (EEG) provides valuable prognostic information. However, prior approaches largely rely on snapshots of the EEG, without taking advantage of temporal information.

Authors

  • Wei-Long Zheng
  • Edilberto Amorim
    Department of Neurology, Massachusetts General Hospital, Boston, MA, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA. Electronic address: edilbertoamorim@gmail.com.
  • Jin Jing
    Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.
  • Ona Wu
  • Mohammad Ghassemi
  • Jong Woo Lee
    Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA.
  • Adithya Sivaraju
  • Trudy Pang
  • Susan T Herman
  • Nicolas Gaspard
    Department of Neurology, Yale University, New Haven, Connecticut.
  • Barry J Ruijter
    Clinical Neurophysiology Group, University of Twente, Enschede, Netherlands.
  • Marleen C Tjepkema-Cloostermans
    Department of Clinical Neurophysiology and Neurology, Medisch Spectrum Twente, Enschede, The Netherlands. Electronic address: m.tjepkema-cloostermans@mst.nl.
  • 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.
  • M Brandon Westover
    Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts.

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