Improved generalized fractal dimensions in the discrimination between Healthy and Epileptic EEG Signals

Recently, Fractal Analysis is the well developed theory in the Data Analysis of non-linear time series. Especially Multifractal Analysis, based on Generalized Fractal Dimensions (GFD), is a powerful mathematical tool for modeling many physical and biological time signals with high complexity and irregularity. Epilepsy is the main fatal neurological disorder in our brain, which is analyzed by the biomedical signal called Electroencephalogram (EEG). GFD is the measure to compute the complexity, irregularity and the chaotic nature of the EEG Signals. This paper proposes an improved method of GFD in order to discriminate the Healthy and the Epileptic EEGs. Finally we conclude that there are significant differences between the Healthy and Epileptic Signals in the designed method than the GFD through graphical and statistical tools. The improved multifractal measure is very efficient technique to analyze the EEG Signals and to compute the state of illness of the Epileptic patients.

Research highlights
► Epilepsy is the fatal neurological disorder in our brain, which is analyzed by EEG.
► GFD measures the complexity, irregularity and the chaotic nature of the EEG Signals.
► An improved method of GFD discriminates the Healthy and the Epileptic EEGs with high accuracy.