Quantitative EEG reactivity and machine learning for prognostication in hypoxic-ischemic brain injury.

Journal: Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
PMID: 31419742

Abstract

OBJECTIVE: Electroencephalogram (EEG) reactivity is a robust predictor of neurological recovery after cardiac arrest, however interrater-agreement among electroencephalographers is limited. We sought to evaluate the performance of machine learning methods using EEG reactivity data to predict good long-term outcomes in hypoxic-ischemic brain injury.

Authors

  • 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.
  • Michelle van der Stoel
    University of Twente, Enschede, Netherlands.
  • Sunil B Nagaraj
    1Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA.2Department of Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA.3Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA.4Department of Electronics, Informatics and Bioengineering, Politecnico di Milano, Milan, Italy.
  • Mohammad M Ghassemi
  • Jin Jing
    Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.
  • Una-May O'Reilly
    Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Benjamin M Scirica
    Accelerator for Clinical Transformation, Brigham and Women's Hospital, Boston, MA.
  • Jong Woo Lee
    Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA.
  • Sydney S Cash
    Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, United States of America.
  • M Brandon Westover
    Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts.

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