MemoryInception: Predicting Neurological Recovery from EEG Using Recurrent Inceptions

Cardiac arrest (CA) may cause severe brain damage, cognitive impairments and death. Monitoring neurological recovery after hospitalization is critical to provide suitable treatment. In this study, we aim to develop an algorithm to aid in neurological recovery classification. The dataset used for this purpose includes 1020 patients and contains both continuous sensor measurements, taken from 0 to 72 hours after the CA, and structured data. More specifically the dataset is split into a training set (60%), validation set (10%) and undisclosed test set (30%). The developed model uses a one-dimensional convolutional neural network to extract features from 5-minute time series segments, fed into a recurrent neural network, to capture temporal information and provide adaptability in recording length. The output features are then merged with embedded patient metadata in a fully connected layer, for the final classification of neurological outcome. The project is part of the George B. Moody PhysioNet Challenge 2023, where our team (EEG-Attackers) achieved a challenge score and rank on the undisclosed test dataset of 0.16 (30), 0.13 (33), 0.11 (34) and 0.12 (35) using recordings from the first 12, 24, 48 and 72 hours after CA.