Variational Autoencoders for Electroencephalogram Feature Extraction in Patients with Coma After Cardiac Arrest

Many survivors of cardiac arrest subsequently end up in a coma state, and these patients will go onto achieve varying levels of neurological recovery, ranging from brain death to full recovery. Electroencephalogram (EEG) analysis can be used to predict the neurological outcome of a cardiac arrest patient, but the patterns are complex and human analysis is time-consuming. We trained a variational autoencoder to extract features from EEGs and used those features in a random forest classifier to predict neurological outcome after cardiac arrest. The resulting model was able to differentiate between good neurological outcome, defined as Cerebral Performance Category (CPC) of 1 or 2, versus poor neurological outcome, defined as CPC of 3–5. The final model had a true positive rate of 0.257 and a false positive rate of 0.05. These results demonstrate that it is possible to use variational autoencoders to extract EEG features that are useful for downstream tasks. This opens the door to more interpretable models for EEG analysis in the future. This article was part of ‘Predicting Neurological Recovery from Coma After Cardiac Arrest: The George B. Moody PhysioNet Challenge 2023’.