Machine Learning Algorithms and Quantitative Electroencephalography Predictors for Outcome Prediction in Traumatic Brain Injury: A Systematic Review

Abstract

Recent developments in the field of machine learning (ML) have led to a renewed interest in the use of electroencephalography (EEG) to predict the outcome after traumatic brain injury (TBI). This systematic review aims to determine how previous studies have taken into consideration the important modeling issues for quantitative EEG (qEEG) predictors in developing prognostic models. A systematic search in the PubMed and Google Scholar databases was performed to identify all predictive models for the extended Glasgow outcome scale (GOSE) and Glasgow outcome scale (GOS) based on EEG data. Fourteen studies were identified that evaluated ML algorithms using qEEG predictors to predict outcome in patients with moderate to severe TBI. In each model, a maximum of five qEEG predictors were selected to determine the association between these parameters, and favorable or unfavorable predicted outcomes. The most common ML technique used was logistic regression, but the algorithms varied depending on the types and numbers of qEEG predictors selected in each model. The qEEG variability for the relative and absolute band powers were the most common qEEG predictors included in the models (46%) followed by total EEG power of all frequency bands (31%), EEG-reactivity (31%) and coherence (15%). Model performance was often quantified by the area under the receiving-operating characteristic curve (AUROC) rather than by accuracy rate. Various ML models have demonstrated great potential, especially using qEEG predictors, to predict outcome in patients with moderate to severe TBI.

Keywords

• Electroencephalography
• machine learning algorithms
• prediction methods
• reviews