Electroencephalogram-based machine learning models to predict neurologic outcome after cardiac arrest: A systematic review.

ABSTRACT:

Aim of the review

The primary aim of this systematic review was to investigate the most common electroencephalogram (EEG)-based machine learning (ML) model with the highest Area Under Receiver Operating Characteristic Curve (AUC) in two ML categories, conventional ML and Deep Neural Network (DNN), to predict the neurologic outcomes after cardiac arrest; the secondary aim was to investigate common EEG features applied to ML models.

Methods

Systematic search of medical literature from PubMed and engineering literature from Compendex up to June 2, 2023. One reviewer screened studies that used EEG-based ML models to predict the neurologic outcomes after cardiac arrest. Four reviewers validated that the studies met selection criteria. Nine variables were manually extracted. The t

SUBMITTER: Chen CC 

PROVIDER: S-EPMC11023717 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature