Predicting Deep Hypnotic State From Sleep Brain Rhythms Using Deep Learning: A Data-Repurposing Approach.

Journal: Anesthesia and analgesia
PMID: 32287128

Abstract

BACKGROUND: Brain monitors tracking quantitative brain activities from electroencephalogram (EEG) to predict hypnotic levels have been proposed as a labor-saving alternative to behavioral assessments. Expensive clinical trials are required to validate any newly developed processed EEG monitor for every drug and combinations of drugs due to drug-specific EEG patterns. There is a need for an alternative, efficient, and economical method.

Authors

  • Sunil Belur Nagaraj
    From the Department of Clinical Pharmacy & Pharmacology.
  • Sowmya M Ramaswamy
    Department of Anaesthesiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands. Electronic address: s.muchukunte.ramaswamy@umcg.nl.
  • Maud A S Weerink
    Department of Anaesthesiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
  • Michel M R F Struys
    Department of Anaesthesiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands; Department of Basic and Applied Medical Sciences, Ghent University, Ghent, Belgium.

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