Generalized Hurst exponent estimates differentiate EEG signals of healthy and epileptic patients

- GHE is employed to characterize normal and EEG signals with seizure free intervals.
- Robust statistical tests are performed at each scale to check existence of differences.
- Short and long variations of normal and seizure free interval signals are obviously altered.
- There is strong evidence that GHE estimates differentiate EEG of healthy and unhealthy patients.
- Multifractals explain dynamics of normal and epileptic signals with epochs free of seizure.

The aim of our current study is to check whether multifractal patterns of the electroencephalographic (EEG) signals of normal and epileptic patients are statistically similar or different. In this regard, the generalized Hurst exponent (GHE) method is used for robust estimation of the multifractals in each type of EEG signals, and three powerful statistical tests are performed to check existence of differences between estimated GHEs from healthy control subjects and epileptic patients. The obtained results show that multifractals exist in both types of EEG signals. Particularly, it was found that the degree of fractal is more pronounced in short variations of normal EEG signals than in short variations of EEG signals with seizure free intervals. In contrary, it is more pronounced in long variations of EEG signals with seizure free intervals than in normal EEG signals. Importantly, both parametric and nonparametric statistical tests show strong evidence that estimated GHEs of normal EEG signals are statistically and significantly different from those with seizure free intervals. Therefore, GHEs can be efficiently used to distinguish between healthy and patients suffering from epilepsy.