title: [Nonlinear analysis on the EEG information of rat epileptic model].
abstract: The aim of this study was to develop a new method of epileptic prediction using nonlinear dynamic theory. When rat was falling sickness, its EEG was researched by using approximate entropy and correlation dimension. The results showed the approximate entropy and correlation dimension during epileptic seizure are obviously lower than those before seizure and after seizure. The span of time before seizure is a special phase. Before the seizure symptom appeared, the complexity of EEG had begun declining. Thus, the outbreak of epilepsy could be predicted in short time using nonlinear dynamic methods.
authors.affiliation: Department of Medical Engineering, Second Artillery General Hospital, Beijing 100088.
authors.forename: Minguang
authors.initials: M
authors.lastname: Xu
authors.forename: Kaiping
authors.initials: K
authors.lastname: Long
authors.forename: Zhong
authors.initials: Z
authors.lastname: Jian
authors.forename: Xiuzhen
authors.initials: X
authors.lastname: Dong
authors.forename: Jiqing
authors.initials: J
authors.lastname: Yang
authors.forename: Sheng
authors.initials: S
authors.lastname: Han
authors.forename: Wen
authors.initials: W
authors.lastname: Jiang
mesh_terms: D000465:Algorithms; D000818:Animals; D004195:Disease Models, Animal; D004569:Electroencephalography; D019277:Entropy; D004827:Epilepsy / Q000175:diagnosis / Q000503:physiopathology*; D008297:Male; D017711:Nonlinear Dynamics; D051381:Rats; D017207:Rats, Sprague-Dawley; D012815:Signal Processing, Computer-Assisted