Comparative study of nonlinear properties of EEG signals of a normal person and an epileptic patient
Md. Nurujjaman, Ramesh Naryanan, A.N. Sekar Iyengar

TL;DR
This study compares nonlinear properties of EEG signals from normal and epileptic individuals using surrogate analysis, probability distribution, and Hurst exponent, revealing distinct long-term correlation patterns in epileptic brains.
Contribution
It introduces the use of Hurst exponent to effectively differentiate between normal and epileptic brain activity in EEG signals.
Findings
Epileptic brain activity is long-term anticorrelated.
Normal brain activity is more stochastic and uncorrelated.
Hurst exponent effectively characterizes brain states.
Abstract
Background: Investigation of the functioning of the brain in living systems has been a major effort amongst scientists and medical practitioners. Amongst the various disorder of the brain, epilepsy has drawn the most attention because this disorder can affect the quality of life of a person. In this paper we have reinvestigated the EEGs for normal and epileptic patients using surrogate analysis, probability distribution function and Hurst exponent. Results: Using random shuffled surrogate analysis, we have obtained some of the nonlinear features that was obtained by Andrzejak \textit{et al.} [Phys Rev E 2001, 64:061907], for the epileptic patients during seizure. Probability distribution function shows that the activity of an epileptic brain is nongaussian in nature. Hurst exponent has been shown to be useful to characterize a normal and an epileptic brain and it shows that the…
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