Liu Chang-Chia, Pardalos Panos M, Chaovalitwongse W Art, Shiau Deng-Shan, Ghacibeh Georges, Suharitdamrong Wichai, Sackellares J Chris
Department of Industrial and Systems Engineering, Biomedical Engineering, University of Florida, 303 Weil Hall, P.O. Box 116595, Gainesville, FL 32611-6595, USA.
J Comb Optim. 2008 Apr;15(3):276-286. doi: 10.1007/s10878-007-9118-9.
Epilepsy is a brain disorder characterized clinically by temporary but recurrent disturbances of brain function that may or may not be associated with destruction or loss of consciousness and abnormal behavior. Human brain is composed of more than 10 to the power 10 neurons, each of which receives electrical impulses known as action potentials from others neurons via synapses and sends electrical impulses via a sing output line to a similar (the axon) number of neurons. When neuronal networks are active, they produced a change in voltage potential, which can be captured by an electroencephalogram (EEG). The EEG recordings represent the time series that match up to neurological activity as a function of time. By analyzing the EEG recordings, we sought to evaluate the degree of underlining dynamical complexity prior to progression of seizure onset. Through the utilization of the dynamical measurements, it is possible to classify the state of the brain according to the underlying dynamical properties of EEG recordings. The results from two patients with temporal lobe epilepsy (TLE), the degree of complexity start converging to lower value prior to the epileptic seizures was observed from epileptic regions as well as non-epileptic regions. The dynamical measurements appear to reflect the changes of EEG's dynamical structure. We suggest that the nonlinear dynamical analysis can provide a useful information for detecting relative changes in brain dynamics, which cannot be detected by conventional linear analysis.
癫痫是一种脑部疾病，临床上的特征是脑功能出现暂时但反复发作的紊乱，这种紊乱可能与意识丧失和异常行为有关，也可能无关。人类大脑由超过10的10次方个神经元组成，每个神经元通过突触从其他神经元接收被称为动作电位的电脉冲，并通过一条单一输出线向数量相似的神经元（轴突）发送电脉冲。当神经网络活跃时，它们会产生电位变化，这种变化可以通过脑电图（EEG）捕捉到。EEG记录代表了与神经活动随时间变化相匹配的时间序列。通过分析EEG记录，我们试图在癫痫发作进展之前评估潜在动态复杂性的程度。通过利用动态测量，可以根据EEG记录的潜在动态特性对大脑状态进行分类。来自两名颞叶癫痫（TLE）患者的结果显示，在癫痫发作前，癫痫区域和非癫痫区域的复杂性程度开始趋于较低值。动态测量似乎反映了EEG动态结构的变化。我们认为，非线性动态分析可以为检测大脑动态中的相对变化提供有用信息，而这是传统线性分析无法检测到的。
