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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 kinetic characteristics of electroencephalogram (EEG) are the chaotic fractal dimension of EEG activity from minutes to tens of minutes before epileptic seizure. The correlation dimension, Lyapunov exponent, complexity of chaos and degrees of freedom are significantly reduced. EEG Tending to synchronize and cyclical, predicts seizures. The research shows that it is feasible to look for the characteristic parameters of nonlinear dynamic model to describe the state of EEG chaos. OBJECTIVE: To explore the characteristics of the whole EEG signal during the process of seizure in rats by using approximate entropy and correlation dimension of nonlinear kinetic parameters. Design: animal as the research object, observation, confirmatory research. Unit: PLA Second Artillery General Hospital, Department of Medical Engineering and Gastroenterology and Fourth Military Medical University, Department of Biomedical Engineering Physics. MATERIALS: Experiments were performed at the Complex Laboratory of Biomedical Engineering, Fourth Military Medical University, People’s Liberation Army from September 2001 to January 2002. Six male SD rats were selected and their body weight was 150 ~ 200g. Intervention: male Sprague-Dawley rats were intraperitoneally injected with 0.5 mL of chloral hydrate, and under deep anesthesia. After the electroencephalogram was stable, Beomei’s injection was diluted by one time and intraperitoneally injected by 0.5 mL. After a period of time, the rats started to have body twitching and seizures accompanied by squeaking sounds, recording the entire process continuously. According to the experimental record of EEG waveform and the onset of symptoms in the course of the experiment, non-seizure, pre-seizure, seizure and post-seizure episodes in the four stages of brain wave were non-linear analysis. Calculate the change of approximate entropy and related dimension. MAIN OUTCOME MEASURES: Approximate entropy and related dimensions of changes in the four stages of episodes, episodes, episodes and episodes of episodes. Results: Six rats entered the experimental analysis. In epileptic seizures, the approximate entropy and correlation dimension of EEG (0.447 ± 0.126,2.166 ± 0.377) were significantly lower than those before the onset (0.807 ± 0.182, 4.773 ± 0.319) and after the onset (1.241 ± 0.125, 6.042 ± 0.373) t = -3.984 ~ 17.902, P <0.01). Among them, the difference of approximate entropy and related dimension (1.313 ± 0.090, 6.405 ± 0.694) of EEG before and after seizure and seizure were compared, t = -5.228 ~ 12.740, P <0.01. CONCLUSION: Approximate entropy and correlation dimension data changes of nonlinear dynamic methods reveal the characteristics and differences of EEG activity before and after seizures and seizures in rats, indicating the variation of EEG complexity in the process of seizures.