EEG Tensorization Enhances CNN-Based Outcome Classification in Comatose Patients Following a Cardiac Arrest

Standard diagnostic methods for evaluating the severity of brain injuries resulting from cardiac arrest, such as the Glasgow Coma Scale, exhibit subjective biases that lead to potentially fatal misclassifications, where life-support systems are prematurely withdrawn from patients who might otherwise recover. This study utilizes an open dataset from the International Cardiac Arrest Research Consortium to develop and evaluate a 3D convolutional neural network (CNN) model for classifying outcomes in comatose patients after cardiac arrest. The electroencephalographic (EEG) signals from the dataset are preprocessed by resampling, filtering, and standardizing signal length (10 seconds) and channel count. The model's architecture comprises 3D convolutional neural networks with subsequent layers for vectorization, compression, and further automatic feature extraction. Evaluation metrics focus on the area under the receiver operating characteristic curve, confusion matrix, accuracy, and F1 score. Results show that the 3D-CNN model outperforms existing 2D-CNN models in classifying outcomes for comatose patients, exhibiting a higher area under the receiver operating characteristic curve.