Quantitative analysis of EEG reactivity for neurological prognostication after cardiac arrest. Issue 9 (September 2021)

• Record Type: Journal Article
• Authors: Admiraal, M.M.; Ramos, L.A.; Delgado Olabarriaga, S.; Marquering, H.A.; Horn, J.; van Rootselaar, A.F.
• Is Part Of: Clinical neurophysiology. Volume 132:Issue 9(2021)
• Page Start: 2240
• Page End: 2247
• Publication Date: 2021-09
• Subjects: EEG reactivity -- Cardiac arrest -- Prognostication -- Machine learning -- Predictive modeling; Neurophysiology -- Periodicals; Electroencephalography -- Periodicals; Electromyography -- Periodicals; Neurology -- Periodicals; 612.8
• DOI: 10.1016/j.clinph.2021.07.004
• Languages: English
• ISSNs: 1388-2457

Abstract

Highlights: Quantification of EEG reactivity (EEG-R) using machine learning predicts poor outcome after cardiac arrest as good as visual analysis of EEG-R. Quantitative analysis of EEG background at 24 h alone yields higher prognostic value than quantitative analysis of EEG-R. Quantitative analysis of EEG-R and EEG background combined reduces the number of false positives. Abstract: Objective: To 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). Methods: Several 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. Results: The 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. Conclusions: Quantitative 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. Significance: Quantitative EEG-R using ML is a promising alternative to visual analysis and of some added value to ML on EEG background data.