应用非线性动力学指标分析大鼠癫痫模型脑电信息变化(英文)

Changes in electroencephalogram in rat epilepsy model via nonlinear dynamical approach

背景:脑电活动的动力学特征,是在癫痫发作前数分钟至数十分钟脑电活动的混沌分维数、相关维数、Lyapunov指数、混沌复杂度和自由度等指标显著减少,脑电趋于同步和周期化,预示癫痫发作。研究表明非线性动力学指标寻找表征脑电混沌状态的特征参数具有可行性。目的:探索应用非线性动力学指标近似熵和相关维,分析大鼠癫痫发作过程的整个脑电信号特征。设计:以动物为研究对象,观察、验证性研究。单位:解放军第二炮兵总医院医学工程科和消化内科及解放军第四军医大学生物医学工程系物理教研室。材料:实验于2001-09/2002-01在解放军第四军医大学生物医学工程系复杂性实验室完成。选择雄性SD大鼠6只,体质量150～200g。干预:雄性SD大鼠,腹腔注射水合氯醛0.5mL,处于深度麻醉状态,脑电平稳后,将贝美格注射液稀释一倍,腹腔注射0.5mL。一段时间后大鼠开始身体抽搐,并伴有低沉叫声的癫痫发作,连续记录整个过程。根据实验记录大鼠脑电波形与试验过程中的发作症状,分别抽取未发作、发作前、发作中和发作后四个阶段大鼠脑电波进行非线性分析。计算近似熵与相关维的变化。主要观察指标:未发作、发作前、发作中和发作后四个阶段近似熵与相关维的变化。结果:6只大鼠进入实验分析。癫痫发作中时,脑电信号的近似熵和相关维(0.447±0.126,2.166±0.377)明显低于发作前(0.807±0.182,4.773±0.319)和发作后(1.241±0.125,6.042±0.373)(t=-3.984～17.902,P<0.01)。其中发作前,发作中与未发作时脑电信号近似熵和相关维(1.313±0.090,6.405±0.694)的差异比较,t=-5.228～12.740,P<0.01。结论:非线性动力学方法近似熵和相关维数据变化,揭示了大鼠癫痫发作期和发作前后脑电信息活动特征性及其差异,表明了癫痫发作过程脑电信号复杂度的变化规律。

BACKGROUND:The dynamic characteristics of electroencephalogram(EEG) include a decrease in the chaotic dimension,the correlation dimension,the Lyapunov exponent, the chaotic complexity,the freedom of EEG and an enhanced synchronization and periodicity of the EEG from several minutes to tens of minutes before epileptic seizures.All these characteristics prefigure the forthcoming seizures.Some studies have proven that the nonlinear dynamical system can be used as a feasible approach to explore the potential variables for describing the chaos portrait of EEG.OBJECTIVE:To analyze the electric characteristics of EEG signal in the epileptic seizures in rat model by investigating the nonlinear dynamical variables,such as the approximate entropy(ApEn) and correlation dimension.DESIGN:Observational and experimental study based on animals.SETTING:Department of Medical Engineering,Department of Gastroenterology,Second Artilleryman General Hospital of Chinese PLA;Department of Physics,Faulty of Biomedical Engineering,Fourth Military Medical University of Chinese PLA.MATERIALS:From September 2001 to January 2002, this study was conducted at the Complexity Laboratory of the Biomedical Department of the Fourth Military Medical University of Chinese PLA. Six male SD rats,weighing 150-200 g,were selected.INTERVENTIONS:After intraperitoneal injection of chloral hydrate (0.5 mL),the male SD rats were deeply anesthetized.When their EEG signal became stable, bemegride injection was diluted at 1:1 with saline and was given on a volume of 0.5 mL to the rats intraperitoneally.After a while,the epileptic seizures started marked by a spasm with a deep roar.The entire epileptic seizures were recorded.According to the shape of EEG waves and the corresponding symptoms of the rats during their seizures,data of the four phases,referring to normal condition, preictal phase,ictal phase and postictal phases of epileptic seizures,were selected for nonlinear analysis.The variations of the ApEn and the correlation dimension were calculated.MAIN OUTCOME MEASURES:In the four phases of the seizures, before seizures, preictal phase,ictal phase and postictal phases, the changes in the ApEn and correlation dimension were observed.RESULTS:All the 6 rats entered the statistical procedure. During epilepsy,the ApEn and correlation dimension of the EEG signal in ictal phases(0.447±0.126, 2.166±0.377) decreased significantly while those in preictal phases(0.807±0.182,4.773±0.319) and postictal phases(1.241±0.125,6.042±0.373)(t=-3.984 to 17.902,P< 0.01). The ApEn and the correlation dimension of the EEG signal in preictal and ictal phases had significant difference with those observed under normal conditions(1.313±0.090,6.405±0.694)(t=-5.228 to 12.740,P< 0.01).CONCLUSION:The changes in ApEn and correlation dimension showed by nonlinear dynamical approach in this study reflect the characteristics of EEG signals in preictal time,ictal time and postictal time of the epileptic seizures and the differences among them. Additionally,they also reveal the laws in the changes of the complex ictal EEG signal.