Novel drug-independent sedation level estimation based on machine learning of quantitative frontal electroencephalogram features in healthy volunteers.

Journal: British journal of anaesthesia
PMID: 31326088

Abstract

BACKGROUND: Sedation indicators based on a single quantitative EEG (QEEG) feature have been criticised for their limited performance. We hypothesised that integration of multiple QEEG features into a single sedation-level estimator using a machine learning algorithm could reliably predict levels of sedation, independent of the sedative drug used.

Authors

  • Sowmya M Ramaswamy
    Department of Anaesthesiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands. Electronic address: s.muchukunte.ramaswamy@umcg.nl.
  • Merel H Kuizenga
    Department of Anaesthesiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
  • Maud A S Weerink
    Department of Anaesthesiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands.
  • Hugo E M Vereecke
    Department of Anaesthesiology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands; Department of Anaesthesiology and Reanimation, AZ Sint-Jan Brugge-Oostende AV, Brugge, Belgium.
  • 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.
  • 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.

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