Using Embedding Extractor and Transformer Encoder for Predicting Neurological Recovery from Coma After Cardiac Arrest.

This research presents a deep-learning framework designed to forecast neurological recovery following a cardiac arrest-induced coma. The framework is created by the team ISIBrno-AIMT as part of the Predicting Neurological Recovery from Coma After Cardiac Arrest: The George B. Moody PhysioNet Challenge 2023. Our approach involves a two-stage model: initially, the model derives low-dimensional embeddings from short electroencephalogram (EEG) segments (5 minutes), and subsequently, it combines the temporal progression (72 hours) of these embeddings to yield a comprehensive likelihood assessment of recovery outcomes. Regrettably, our submission was not evaluated in the ranking phase due to issues with the Docker pipeline.