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
• ISSN: 1388-2457; 1872-8952; 1872-8952
• DOI: 10.1016/j.clinph.2019.07.014

•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. Electroencephalogram (EEG) reactivity is a robust predictor of neurological recovery after cardiac arrest, however interrater-agreement among electroencephalographers is limited. We sought to evaluate the performance of machine learning methods using EEG reactivity data to predict good long-term outcomes in hypoxic-ischemic brain injury. We retrospectively reviewed clinical and EEG data of comatose cardiac arrest subjects. Electroencephalogram reactivity was tested within 72 h from cardiac arrest using sound and pain stimuli. A Quantitative EEG (QEEG) reactivity method evaluated changes in QEEG features (EEG spectra, entropy, and frequency features) during the 10 s before and after each stimulation. Good outcome was defined as Cerebral Performance Category of 1–2 at six months. Performance of a random forest classifier was compared against a penalized general linear model (GLM) and expert electroencephalographer review. Fifty subjects were included and sixteen (32%) had good outcome. Both QEEG reactivity methods had comparable performance to expert EEG reactivity assessment for good outcome prediction (mean AUC 0.8 for random forest vs. 0.69 for GLM vs. 0.69 for expert review, respectively; p non-significant). Machine-learning models utilizing quantitative EEG reactivity data can predict long-term outcome after cardiac arrest. A quantitative approach to EEG reactivity assessment may support prognostication in cardiac arrest.