Identification of EEG signals in epilepsy by cell outputs of Reaction-Diffusion Networks

Cellular Nonlinear Networks (CNN) are characterized by local couplings of comparatively simple dynamical systems. In spite their compact structure, CNN exhibit complex phenomena like nonlinear wave propagation or chaotic behavior. The well studied Reaction-Diffusion Systems are widely used to describe phenomena like pattern formation and other processes in the fields of biology, chemistry and physics. By spatial discretization Reaction-Diffusion Partial Differential equations can be mapped to the cellular structures of Reaction-Diffusion Cellular Nonlinear Networks (RD-CNN). In this contribution simple RD-CNN models are determined in numerical optimization procedures in order to approximate short segments of EEG signals. Thereby effects of higher order nonlinear cell couplings are studied. Parameter changes of the RD-CNN models may be used for precursor detection of impending seizures in epilepsy.