Nonlinear Analysis of Clinical Epileptic EEG by Approximate Entropy
摘要
By the means of computing approximate entropy (ApEn) of video-EEG from some clinical epileptic, ApEn of EEG with epileptiform discharges is found significantly different from that of EEG without epileptiform discharges, (p=0.002). Meanwhile, dynamic ApEn shows consistent change of EEG signal with discharges of epileptic waves inside. These results suggest that ApEn may be a useful tool for automatic recognition and detection of epileptic activity and for understanding epileptogenic mechanism. Epilepsy is a chronic condition characterized by the occurrence of seizures, transient alterations in consciousness resulted from abnormal electrical activity in the brain. Brain activity is supposed to be chaos with strong non-stability. Recent years, development of nonlinear dynamic brings new points for studying electro-encephalography (EEG) information. Study of nonlinear dynamics for the time series focus on statistical quantities such as correlation dimension (D2), Lyapunov exponent (LE), Metric entropy and complexity (1) . Previous studies have shown that those