癫痫患者脑内EEG的关联维数图
Correlation Dimension Maps of EEG from Epileptic Absences
摘要：
目的：对大脑活动的理解，特别是对癫痫发作等事件的理解，取决于对神经网络动力学的表征。非线性动力学理论提供了信号分析技术，可以提供有关这种网络行为的新信息。方法：对3例患者共7次失神发作的19导EEG数据进行关联维数图计算。在癫痫发作之前、期间和之后分析信号。阶段随机替代数据被用来测试混沌。结果如下：在两名患者的癫痫发作中，我们可以区分大脑皮层上的两个动态区域，一个似乎表现出混乱，而另一个似乎表现出噪音。所示的模式对于由过度换气触发的癫痫发作基本相同，但对于由闪光触发的癫痫发作不同。人们似乎观察到的混沌动力学是由少量变量决定的，复杂性低。另一方面，在另一个病人的癫痫发作中，没有发现混乱的区域，在所有病例中，癫痫发作前和发作期间也没有发现混乱。结论：非线性信号分析的应用揭示了与失神发作相关的空间动力学差异的存在。这将有助于对癫痫发作的认识和临床诊断。
英文摘要：
Purpose: The understanding of brain activity, and in particular events such as epileptic seizures, lies on the characterisation of the dynamics of the neural networks. The theory of non-linear dynamics provides signal analysis techniques which may give new information on the behaviour of such networks. Methods: We calculated correlation dimension maps for 19-channel EEG data from 3 patients with a total of 7 absence seizures. The signals were analysed before, during and after the seizures. Phase randomised surrogate data was used to test chaos. Results: In the seizures of two patients we could distinguish two dynamical regions on the cerebral cortex, one that seemed to exhibit chaos whereas the other seemed to exhibit noise. The pattern shown is essentially the same for seizures triggered by hyperventilation, but differ for seizures triggered by light flashes. The chaotic dynamics that one seems to observe is determined by a small number of variables and has low complexity. On the other hand, in the seizures of another patient no chaotic region was found. Before and during the seizures no chaos was found either, in all cases. Conclusions: The application of non-linear signal analysis revealed the existence of differences in the spatial dynamics associated to absence seizures. This may contribute to the understanding of those seizures and be of assistance in clinical diagnosis.
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