Entropy analyses of spatiotemporal synchronizations in brain signals from patients with focal epilepsies

TL;DR

This study uses entropy analysis of intracerebral EEG data from epileptic patients to predict seizure onsets and investigate brain signal synchronization patterns across different brain regions.

Contribution

It introduces the use of time-dependent Shannon entropy for seizure prediction and analyzes spatiotemporal synchronization in brain signals from epileptic patients.

Findings

Entropy can predict seizure onsets in epileptogenic zones.

Synchronization patterns differ between ictal and inter-ictal intervals.

Spatial and temporal entropy differences reveal brain signal dynamics.

Abstract

The electroencephalographic (EEG) data intracerebrally recorded from 20 epileptic humans with different brain origins of focal epilepsies or types of seizures, ages and sexes are investigated (nearly 700 million data). Multi channel univariate amplitude analyses are performed and it is shown that time dependent Shannon entropies can be used to predict focal epileptic seizure onsets in different epileptogenic brain zones of different patients. Formations or time evolutions of the synchronizations in the brain signals from epileptogenic or non epileptogenic areas of the patients in ictal interval or inter-ictal interval are further investigated employing spatial or temporal differences of the entropies.