Permutation entropy‐derived parameters to estimate the epileptogenic zone network

Author:

Bratu Ionuț‐Flavius12ORCID,Makhalova Julia123ORCID,Garnier Elodie2ORCID,Villalon Samuel Medina12,Jegou Aude2,Bonini Francesca12,Lagarde Stanislas12ORCID,Pizzo Francesca12,Trébuchon Agnès12ORCID,Scavarda Didier14ORCID,Carron Romain15,Bénar Christian2ORCID,Bartolomei Fabrice12ORCID

Affiliation:

1. APHM, Timone Hospital, Epileptology and Cerebral Rhythmology Marseille France

2. Aix Marseille Univ, INSERM, INS, Inst Neurosci Syst Marseille France

3. APHM, Timone Hospital, CEMEREM Marseille France

4. APHM Paediatric Neurosurgery Department Marseille France

5. APHM Functional Neurosurgery Department Marseille France

Abstract

AbstractObjectiveQuantification of the epileptogenic zone network (EZN) most frequently implies analysis of seizure onset. However, important information can also be obtained from the postictal period, characterized by prominent changes in the EZN. We used permutation entropy (PE), a measure of signal complexity, to analyze the peri‐ictal stereoelectroencephalography (SEEG) signal changes with emphasis on the postictal state. We sought to determine the best PE‐derived parameter (PEDP) for identifying the EZN.MethodsSeveral PEDPs were computed retrospectively on SEEG‐recorded seizures of 86 patients operated on for drug‐resistant epilepsy: mean baseline preictal entropy, minimum ictal entropy, maximum postictal entropy, the ratio between the maximum postictal and the minimum ictal entropy, and the ratio between the maximum postictal and the baseline preictal entropy. The performance of each biomarker was assessed by comparing the identified epileptogenic contacts or brain regions against the EZN defined by clinical analysis incorporating the Epileptogenicity Index and the connectivity epileptogenicity index methods (EZNc), using the receiver‐operating characteristic and precision‐recall.ResultsThe ratio between the maximum postictal and the minimum ictal entropy (defined as the Permutation Entropy Index [PEI]) proved to be the best‐performing PEDP to identify the EZNC. It demonstrated the highest area under the curve (AUC) and F1 score at the contact level (AUC 0.72; F1 0.39) and at the region level (AUC 0.78; F1 0.47). PEI values gradually decreased between the EZN, the propagation network, and the non‐involved regions. PEI showed higher performance in patients with slow seizure‐onset patterns than in those with fast seizure‐onset patterns. The percentage of resected epileptogenic regions defined by PEI was significantly correlated with surgical outcome.SignificancePEI is a promising tool to improve the delineation of the EZN. PEI combines ease and robustness in a routine clinical setting with high sensitivity for seizures without fast activity at seizure onset.

Funder

Agence Nationale de la Recherche

H2020 European Research Council

Publisher

Wiley

Subject

Neurology (clinical),Neurology

Reference40 articles.

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