Permutation Entropy Analysis to Intracranial Hypertension from a Porcine Model

POSE, FERNANDO; CIARROCCHI, NICOLAS; VIDELA, CARLOS; REDELICO, FRANCISCO O.

ENTROPY

MOLECULAR DIVERSITY PRESERVATION INTERNATIONAL-MDPI

Año: 2023 vol. 25

1099-4300

Intracranial pressure (ICP) monitoring is commonly used in the follow-up of patients in the intensive care unit, but only a small part of the information available in ICP time series is exploited. One of the most important features to guide patient follow-up and treatment is intracranial compliance. We propose to use permutation entropy (PE) as a method to extract non-obvious information from the ICP curve. We have analyzed the results of a pig experiment with sliding windows of 3,600 samples and 1,000 displacement samples and estimated their respective PE, their associated probability distribution, and the number of missing patterns (NMP). We have observed that the behavior of PE is inverse to that of ICP, in addition to the fact that NMP appears as a surrogate for intracranial compliance. In lesion-free periods, PE is usually greater than 0.3, normalized NMP is less than $90%$ and $p(s1) > p(s720)$. Any deviation from these values could be a possible warning of altered neurophysiology. In the terminal phases of the lesion the normalized NMP is higher than $95%$, PE is not sensitive to changes in ICP and $p(s720) > p(s1)$. The results show that it could be used for real-time patient monitoring or as input for a machine learning tool