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arXiv · 2405.08827

A Dynamical Systems Approach to Predicting Patient Outcome after Cardiac Arrest

Richard J Povinelli·Mathew Dupont
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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.

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Richard J Povinelli, Mathew Dupont. 2024-05-13. A Dynamical Systems Approach to Predicting Patient Outcome after Cardiac Arrest. https://doi.org/10.22489/cinc.2023.442

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