Quantitative analysis of EEG reactivity for neurological prognostication after cardiac arrest

Abstract

ObjectiveTo test whether 1) quantitative analysis of EEG reactivity (EEG-R) using machine learning (ML) is superior to visual analysis, and 2) combining quantitative analyses of EEG-R and EEG background pattern increases prognostic value for prediction of poor outcome after cardiac arrest (CA). MethodsSeveral types of ML models were trained with twelve quantitative features derived from EEG-R and EEG background data of 134 adult CA patients. Poor outcome was a Cerebral Performance Category score of 3–5 within 6 months. ResultsThe Random Forest (RF) trained on EEG-R showed the highest AUC of 83% (95-CI 80–86) of tested ML classifiers, predicting poor outcome with 46% sensitivity (95%-CI 40–51) and 89% specificity (95%-CI 86–92). Visual analysis of EEG-R had 80% sensitivity and 65% specificity. The RF was also the best classifier for EEG background (AUC 85%, 95%-CI 83–88) at 24 h after CA, with 62% sensitivity (95%-CI 57–67) and 84% specificity (95%-CI 79–88). Combining EEG-R and EEG background RF classifiers reduced the number of false positives. ConclusionsQuantitative EEG-R using ML predicts poor outcome with higher specificity, but lower sensitivity compared to visual analysis of EEG-R, and is of some additional value to ML on EEG background data. SignificanceQuantitative EEG-R using ML is a promising alternative to visual analysis and of some added value to ML on EEG background data.