Epilepsy is a chronic neurological disorder that is characterized by recurrent unprovoked seizures. These seizures are due to abnormal, excessive or synchronous neuronal activity in the brain. For a neural network, such as brain, nonlinearity is necessary to descript the complexity of dynamic system. In this study, we compared some nonlinear dynamic indictions, such as Hurst exponent, sample entropy, and detrended fluctuation in time series of epilepsy electroencephalogram regarding different physiological and pathological brain states. We found that The Hurst exponent did not differ between healthy volunteers and intracranial patients (p≫0.05). The sample entropy value did not differ between healthy volunteers and seizure active patients (p≫0.05). In other cases we found statistical significant differences between investigated data sets. We concluded that using nonlinear dynamic indications we could discriminate the electroencephalogram regarding different physiological and pathological brain states of epilepsy patients.
['Ta-Cheng Chen', 'Jiunn-I Shieh', 'Kuei-Jen Lee', 'Jing-Doo Wang', 'Pei-Chun Chang', 'Hsiang-Chuan Liu']
Nonlinear Dynamic Indications in Time Series of Epilepsy Electroencephalogram