Predicting neurological outcome in comatose patients after cardiac arrest with multiscale deep neural networks

Electroencephalography (EEG) is an important tool for neurological outcome prediction after cardiac arrest. However, the complexity of continuous EEG data limits timely and accurate interpretation by clinicians. We develop a deep neural network (DNN) model to leverage complex EEG trends for early and accurate assessment of cardiac arrest coma recovery likelihood.We developed a multiscale DNN combining convolutional neural networks (CNN) and...