Characterization of healthy and epileptic brain EEG signals by monofractal and multifractal analysis

This paper presents a method based on fractal dimensions to characterize electroencephalogram (EEG) signals, and differentiate between healthy and epileptic EEG data sets. The estimated correlation fractal dimension is considerably lower for intracranial invasive EEG recordings as compared to non-invasive scalp recordings. The epileptic EEG is also shown to have lower correlation dimension than healthy EEG. Multifractal analysis of EEG signal using the Renyi fractal dimension spectrum also demonstrates lower absolute values and variability in the spectrum for seizure activity compared to normal brain activity. Finally, a moving window scheme is utilized to analyze EEG signal prior to epileptic seizures in search for a pattern to predict an impending seizure. The results of the later study are not conclusive at this point yet.