Variation Trends of Fractal Dimension in Epileptic EEG Signals

Abstract

Epileptic diseases take EEG as an important basis for clinical judgment, and fractal algorithms were often used to analyze electroencephalography (EEG) signals. However, the variation trends of fractal dimension (D) were opposite in the literature, i.e., both D decreasing and increasing were reported in previous studies during seizure status relative to the normal status, undermining the feasibility of fractal algorithms for EEG analysis to detect epileptic seizures. In this study, two algorithms with high accuracy in the D calculation, Higuchi and roughness scaling extraction (RSE), were used to study D variation of EEG signals with seizures. It was found that the denoising operation had an important influence on D variation trend. Moreover, the D variation obtained by RSE algorithm was larger than that by Higuchi algorithm, because the non-fractal nature of EEG signals during normal status could be detected and quantified by RSE algorithm. The above findings in this study could be promising to make more understandings of the nonlinear nature and scaling behaviors of EEG signals.

Fractal Analysis on Surface Topography of Thin Films: A Review

“…Zuo et al [97] found that the linearity of the scaling region increased with the sampling length, and the minimum sampling length was greater for a smooth profile than that for a rough profile. The advantage of scaling region interception to improve accuracy was also found in a fractal analysis of EEG signals [98].…”