SUMMARYIn this paper we present our work analysing electroencephalographic (EEG) signals for the detection of seizure precursors in epilepsy. Volterra systems and Cellular Nonlinear Networks are considered for a multidimensional signal analysis which is called the feature extraction problem throughout this contribution. Recent results obtained by applying a pattern detection algorithm and a non-linear prediction of brain electrical activity will be discussed in detail. The aim of this interdisciplinary project is the realization of an implantable seizure warning and preventing system.
In this contribution a new procedure is proposed for the analysis of the spatio-temporal dynamics of brain electrical activity in epilepsy. Recent investigations1–3 have clarified that changes of estimates of the effective correlation dimension D2(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 [Formula: see text] 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
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