  Aim: Approximately six million people suffer cardiac arrests worldwide per
year with very low survival rates (<1%). Thus, the aim of this study is to
estimate the probability of a poor outcome after cardiac arrest. Accurate
outcome predictions avoid removing care too soon for patients with potentially
good outcomes or continuing care for patients with likely poor outcomes.
Method: The method is based on dynamical systems embedding theorems that show
that a reconstructed phase space (RPS) topologically equivalent to an
underlying system can be constructed from measured signals. Here the underlying
system is the human brain after a cardiac arrest, and the signals are the EEG
channels. We model the RPS with a Gaussian mixture model (GMM) and ensemble the
output of the RPS-GMM with clinical data via XGBoost. Results: As team Blue and
Gold in the Predicting Neurological Recovery from Coma After Cardiac Arrest:
The George B. Moody PhysioNet Challenge 2023, our RPS-GMM-XGBoost method
obtained a test set competition score of 0.426 and rank of 24/36.
