Abstract
In this work, we describe the creation of our machine- learning-based solution for coma prognosis after cardiac arrest using longitudinal EEG and ECG recordings for the ”Predicting Neurological Recovery from Coma After Car- diac Arrest: The George B. Moody PhysioNet Challenge 2023”. Our team, “ComaToast”, had its best submission ranked 28 out of 36 teams selected worldwide, with a chal- lenge score of 0.381 on the official leaderboard for the hid- den test set. We use a combination of age and signal fea- tures from EEG and ECG recordings. Frequency domain features, specifically mean power spectral density from 4 different bands of frequencies (Delta, Theta, Alpha and Beta) and mean Burst Suppression Ratio, were extracted from pre-processed EEG recordings from the first and last available recording for a given patient. Features like mean and standard deviations were extracted along channels for ECG recordings. After imputing missing values, these fea- tures are fed to an XGBoost classifier for the final binary classification of the outcome prediction task. The features are fed to a random forest regressor to predict the CPC outcome for every patient. A solution like ours, which uses a simple model and training technique, may be more viable than deep-learning solutions in general use cases. In our final model, our approach achieved a 5-fold cross- validation score of 0.34 on the public train set.