Open AccessQuantitative Electroencephalogram Trends Predict Recovery in Hypoxic-Ischemic Encephalopathy*
Author(s) -
Mohammad M. Ghassemi,
Edilberto Amorim,
Tuka Alhanai,
Jong W. Lee,
Susan T. Herman,
Adithya Sivaraju,
Nicolas Gaspard,
Lawrence J. Hirsch,
Benjamin M. Scirica,
Siddharth Biswal,
Valdery Moura,
Sydney S. Cash,
Emery N. Brown,
Roger G. Mark,
M. Brandon Westover
Publication year - 2019
Publication title -
critical care medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.002
H-Index - 271
eISSN - 1530-0293
pISSN - 0090-3493
DOI - 10.1097/ccm.0000000000003840
Subject(s) - medicine , receiver operating characteristic , logistic regression , electroencephalography , random forest , encephalopathy , statistics , artificial intelligence , computer science , mathematics , psychiatry
Electroencephalogram features predict neurologic recovery following cardiac arrest. Recent work has shown that prognostic implications of some key electroencephalogram features change over time. We explore whether time dependence exists for an expanded selection of quantitative electroencephalogram features and whether accounting for this time dependence enables better prognostic predictions.