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

ABSTRACT:

Objective

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.

Methods

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

SUBMITTER: Amorim E 

PROVIDER: S-EPMC6751020 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature