A Wavelet-Chaos Methodology for Analysis of EEGs and EEG Subbands to Detect Seizure and Epilepsy

TLDR

The study introduces a wavelet‑chaos approach to analyze EEG and its delta, theta, alpha, beta, and gamma subbands for seizure and epilepsy detection. The method quantifies nonlinear dynamics using correlation dimension and largest Lyapunov exponent, isolates their changes in each subband via wavelet decomposition, and evaluates their discriminative power across healthy, interictal, and ictal EEG recordings through statistical significance testing. Results show that while original EEG parameters lack significant group differences, combining them with specific subbands reveals distinctions, with correlation dimension separating groups in beta and gamma bands and largest Lyapunov exponent distinguishing groups in the alpha band.

Abstract

A wavelet-chaos methodology is presented for analysis of EEGs and delta, theta, alpha, beta, and gamma subbands of EEGs for detection of seizure and epilepsy. The nonlinear dynamics of the original EEGs are quantified in the form of the correlation dimension (CD, representing system complexity) and the largest Lyapunov exponent (LLE, representing system chaoticity). The new wavelet-based methodology isolates the changes in CD and LLE in specific subbands of the EEG. The methodology is applied to three different groups of EEG signals: 1) healthy subjects; 2) epileptic subjects during a seizure-free interval (interictal EEG); 3) epileptic subjects during a seizure (ictal EEG). The effectiveness of CD and LLE in differentiating between the three groups is investigated based on statistical significance of the differences. It is observed that while there may not be significant differences in the values of the parameters obtained from the original EEG, differences may be identified when the parameters are employed in conjunction with specific EEG subbands. Moreover, it is concluded that for the higher frequency beta and gamma subbands, the CD differentiates between the three groups, whereas for the lower frequency alpha subband, the LLE differentiates between the three groups.