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Epilepsy and Seizures: Overview

Epilepsy and Seizures: Overview

Epilepsy is a chronic neurological disease marked by recurrent, unpredictable seizures. These seizures are caused by abnormal electrical discharges in the brain, leading to behavior, sensation, or consciousness alterations. They can also cause transient impairment of awareness, interfering with daily activities.
Various factors can trigger epilepsy, including genetic factors, brain damage, metabolic causes, and unknown etiology. Diagnosis of epilepsy involves electroencephalography (EEG), which...

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Author Spotlight: Advancing Pediatric Epilepsy Surgery in Children Through Novel Biomarkers and Enhanced Localization

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Permutation entropy-derived parameters to estimate the epileptogenic zone network.

Ionuț-Flavius Bratu^1,2, Julia Makhalova^1,2,3, Elodie Garnier²

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

|December 2, 2023

Summary

The Permutation Entropy Index (PEI) effectively identifies the epileptogenic zone network (EZN) by analyzing postictal brain activity. This novel method shows promise for improving epilepsy surgery outcomes.

Keywords:

SEEG permutation entropy index postictal signal complexity

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Network Analysis of Foramen Ovale Electrode Recordings in Drug-resistant Temporal Lobe Epilepsy Patients

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Published on: December 18, 2016

Area of Science:

Neuroscience
Signal Processing
Epilepsy Research

Background:

Quantifying the epileptogenic zone network (EZN) typically relies on seizure onset analysis.
The postictal period, however, presents significant EZN changes offering valuable diagnostic information.
Permutation entropy (PE) measures signal complexity and can analyze these peri-ictal stereoelectroencephalography (SEEG) signal changes.

Purpose of the Study:

To identify the optimal PE-derived parameter (PEDP) for pinpointing the EZN.
To evaluate the efficacy of different PEDPs in characterizing the EZN during the postictal phase.

Main Methods:

Retrospective analysis of SEEG-recorded seizures from 86 patients with drug-resistant epilepsy.
Computation of various PEDPs, including ratios of postictal to ictal and preictal entropy.
Performance assessment using receiver-operating characteristic and precision-recall curves against clinically defined EZN (EZNc).

Main Results:

The ratio of maximum postictal to minimum ictal entropy, termed Permutation Entropy Index (PEI), was the best-performing PEDP.
PEI achieved high accuracy at both contact (AUC 0.72, F1 0.39) and region (AUC 0.78, F1 0.47) levels.
PEI demonstrated higher performance in patients with slow seizure-onset patterns and correlated with surgical outcomes.

Conclusions:

PEI is a robust and easily implementable tool for enhancing EZN delineation in clinical settings.
PEI shows high sensitivity for seizures lacking fast activity at onset.
This method offers a promising approach to improve surgical planning and outcomes in epilepsy patients.