A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2021; you can also visit the original URL.
The file type is application/pdf
.
Machine Learning Algorithms and Quantitative Electroencephalography Predictors for Outcome Prediction in Traumatic Brain Injury: A Systematic Review
2020
IEEE Access
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
doi:10.1109/access.2020.2998934
fatcat:ltce5rfdszgnvcydw2tilwyrcm