Detection of EEG burst-suppression in neurocritical care patients using an unsupervised machine learning algorithm.

Journal: Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
PMID: 34454277

Abstract

OBJECTIVE: The burst suppression pattern in clinical electroencephalographic (EEG) recordings is an important diagnostic tool because of its association with comas of various etiologies, as with hypoxia, drug related intoxication or deep anesthesia. The detection of bursts and the calculation of burst/suppression ratio are often used to monitor the level of anesthesia during treatment of status epilepticus. However, manual counting of bursts is a laborious process open to inter-rater variation and motivates a need for automatic detection.

Authors

  • G Narula
    Neurocritical Care Unit, Department of Neurosurgery and Institute of Intensive Care Medicine, University Hospital Zürich, Zürich, Switzerland. Electronic address: gagan.narula@uzh.ch.
  • M Haeberlin
    Department of Epileptology, Neurology Clinic, University Hospital Zürich, Zürich, Switzerland.
  • J Balsiger
    Department of Epileptology, Neurology Clinic, University Hospital Zürich, Zürich, Switzerland.
  • C Strässle
    Neurocritical Care Unit, Department of Neurosurgery and Institute of Intensive Care Medicine, University Hospital Zürich, Zürich, Switzerland.
  • L L Imbach
    Department of Epileptology, Neurology Clinic, University Hospital Zürich, Zürich, Switzerland.
  • E Keller
    Neurocritical Care Unit, Department of Neurosurgery and Institute of Intensive Care Medicine, University Hospital Zürich, Zürich, Switzerland.

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