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Modeling Brain Electrical Activity in Epilepsy by Reaction-Diffusion Cellular Neural Networks

机译:通过反应扩散细胞神经网络对癫痫发作中脑电活动进行建模

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Reaction-Diffusion systems can be applied to describe a broad class of nonlinear phenomena, in particular in biological systems and in the propagation of nonlinear waves in excitable media. Especially, pattern formation and chaotic behavior are observed in Reaction-Diffusion systems and can be analyzed. Due to their structure multi-layer Cellular Neural Networks (CNN) are capable of representing Reaction-Diffusion systems effectively. In this contribution Reaction-Diffusion CNN are considered for modeling dynamics of brain activity in epilepsy Thereby the parameters of Reaction-Diffusion systems are determined in a supervised optimization process, and brain electrical activity using invasive multi-electrode EEG recordings is analyzed with the aim to detect of precursors of impending epileptic seizures. A detailed discussion of first results and potentiality of the proposed approach will be given.
机译:反应扩散系统可用于描述各种非线性现象,尤其是在生物系统中以及在可激发介质中非线性波的传播中。特别地,在反应扩散系统中观察到图案形成和混沌行为,并且可以对其进行分析。由于其结构,多层细胞神经网络(CNN)能够有效地表示反应扩散系统。在此贡献中,考虑使用反应扩散CNN来模拟癫痫中脑活动的动力学,从而在有监督的优化过程中确定反应扩散系统的参数,并分析使用侵入性多电极EEG记录的脑电活动,目的是检测即将发作的癫痫发作的前体。将详细讨论初步结果和拟议方法的潜力。

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