Time irreversibility and its application in epileptic brain electrical activities

TL;DR

This study investigates time irreversibility in epileptic brain EEGs using novel probabilistic measures, revealing differing nonlinear characteristics during seizure-free intervals across datasets, with implications for understanding epileptic dynamics.

Contribution

It introduces a simplified, validated method for measuring time irreversibility in EEGs and applies it to epileptic data, uncovering dataset-dependent nonlinear features.

Findings

NJGH epileptic EEGs show lower irreversibility during seizure-free periods compared to controls.

Bonn epileptic EEGs exhibit higher nonlinearity than healthy brain activities.

Multi-scale analysis suggests circadian rhythms influence epileptic nonlinearity.

Abstract

Time irreversibility (temporal asymmetry) is one of fundamental properties that characterize the nonlinearity of complex dynamical processes, and our brain is a typical complex dynamical system manifested with nonlinearity. Two subtraction-based parameters, Ys and X2, are employed to measure the probabilistic differences of permutations instead of raw vectors for the simplified quantification of time irreversibility, which is validated by chaotic and reversible processes and the surrogate data. We show that it is equivalent to quantify time irreversibility by measuring probabilistic difference between the forward and its backward processes and between the symmetric permutations. And we detect time irreversibility of two groups of epileptic EEGs, from the Nanjing General Hospital (NJGH) and from the public Bonn epileptic database. In our contribution, NJGH epileptic EEGs during seizure-free intervals of have lower time irreversibility than the control data while those of the Bonn data sets have higher nonlinearity than the healthy brain electrical activities. For the inconsistent results, we conduct multi-scale analysis and elucidate from the circadian rhythms in epileptic nonlinearity, however, more targeted researches are needed to verify our assumptions or to determine if there are other reasons leading to the inconsistency.