A Dynamical Systems Approach to Predicting Patient Outcome after Cardiac Arrest

TL;DR

This study introduces a novel dynamical systems approach using reconstructed phase space and machine learning to predict patient outcomes after cardiac arrest, aiming to improve prognostic accuracy.

Contribution

It applies dynamical systems embedding theorems to EEG signals and combines RPS-GMM with XGBoost for outcome prediction, a new integration in this context.

Findings

Achieved a competition score of 0.426

Ranked 24th out of 36 teams

Demonstrated potential of RPS-GMM-XGBoost method

Abstract

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.