3D CNN as an Approach to Predict the Cerebral Performance of Comatose Patients

Many patients remain in a comatose state after initially surviving a resuscitation following a cardiac arrest. The prognosis in this state carries the decision of life support withdrawal, thus needing an objective and deterministic guideline. The objective of this study, is to assist this decision by providing a model able to predict the cerebral performance category (CPC) of comatose patients following cardiac arrest from their electroencephalographic (EEG) signal. To achieve this, binary classifiers built with 3D Convolutional Neural Networks (CNNs) followed by Dense Neural Networks (DNN) are used in combination with a “divide and conquer” strategy, thus enabling the automatic extraction of features from the tensors of EEG signals, taking into consideration the spatial relation of the signals according to the electrodes' distribution on the scalp. This work was submitted under the team name “BioITACA_UPV” to “Predicting Neurological Recovery from Coma After Cardiac Arrest: The George B. Moody PhysioNet Challenge 2023”, and while the team did not score in the official phase, results obtained from a held-out subset of the training set demonstrate the capability of the model to classify by CPC from short segments of 5 seconds to long recordings of EEG data. Results show an average accuracy of 0.76 between the CPC classifiers and capability to discern between a good or bad outcome prognosis.