Shannon entropies of the distributions of various electroencephalograms from epileptic humans

TL;DR

This study analyzes EEG signals from epileptic patients using Shannon entropy to distinguish between ictal and inter-ictal states, revealing higher entropy in epileptogenic regions and during seizures.

Contribution

It introduces the analysis of harmonic oscillations in EEG and applies Shannon entropy to differentiate epileptic activity from normal brain states.

Findings

Higher Shannon entropy during ictal intervals compared to inter-ictal intervals.

Epileptogenic brain areas show greater entropy than non-epileptogenic areas.

EEG distributions are mostly Gaussian, indicating dominant oscillation types.

Abstract

1) Harmonic oscillations (HO) in numerous electroencephalograms (EEG) from different humans are introduced. 2) The probability density functions (PDF, p(X)) of the EEG voltages (X) are normal (Gauss) for OO whereas, the plots for the distributions of HO (pure) are convex. Gaussians for OO may turn to be convex as HO become dominant in MO or vice versa. However, distributions of the most of the data are found normal which means that most of the EEG oscillations consist of OO (or MO). 3) Shannon entropies (information measures) of the distributions of the data from different brain regions in the ictal intervals or inter-ictal intervals are calculated for each individual recording and compared. The averages of Shannon entropies over the individual recordings during the ictal intervals come out bigger than those from the inter-ictal intervals. These averages are found to be bigger for the data from epileptogenic brain areas than those recorded from non epileptogenic ones in different intervals.