Spatio-temporal dynamics of brain electrical activity in epilepsy: Analysis with Cellular Neural Networks (CNNs)

In this contribution a new procedure is proposed for the analysis of the spatio-temporal dynamics of brain electrical activity in epilepsy. Recent investigations(1-3) have clarified that changes of estimates of the effective correlation dimension D-2 (k, m) from successive data segments allow a characterization of the epileptogenic process. These results provide important information for diagnostical purposes and enable a prediction of seizures in many cases. It will be shown that an accurate approximation of D-2(*) (k, m) can be obtained by Cellular Neural Networks (CNNs),(4,5) which form a unified paradigm. Moreover, the type of CNN presented here is optimized with respect to future implementations as VLSI realizations.(6)