A Machine Learning Approach for Outcome Prediction in Postanoxic Coma Patients Using Frequency Domain Features

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 Cardiac 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 challenge score of 0.381 on the official leaderboard for the hidden test set. We use a combination of age and signal features 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 features 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.