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

• Published in: Clinical neurophysiology Vol. 130; no. 10; pp. 1908 - 1916
• Main Authors: Amorim, Edilberto, van der Stoel, Michelle, Nagaraj, Sunil B., Ghassemi, Mohammad M., Jing, Jin, O'Reilly, Una-May, Scirica, Benjamin M., Lee, Jong Woo, Cash, Sydney S., Westover, M. Brandon
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.10.2019
• Subjects: Adult; Aged; Cardiac arrest; EEG reactivity; Electroencephalography - methods; Female; Humans; Hypoxia-Ischemia, Brain - diagnosis; Hypoxia-Ischemia, Brain - physiopathology; Hypoxic-ischemic encephalopathy; Machine Learning; Male; Middle Aged; Prognosis; Quantitative EEG; Retrospective Studies; EEG reactivity; Quantitative EEG; Hypoxic-ischemic encephalopathy; Cardiac arrest; Machine learning

•Machine-learning methods using QEEG reactivity data can predict outcomes after cardiac arrest.•A QEEG reactivity detector can provide individual-level predictions of neurological recovery.•A quantitative approach to prognostication may improve objectivity of EEG reactivity interpretation. Electroen...