In previous publications,<sup>1-6</sup> several approaches targeting the problem of seizure prediction<sup>7</sup> in epilepsy<sup>8</sup> have been
proposed. In this contribution recent results based on an EEG-signal prediction algorithm will be presented and
discussed in detail. Therefore segmented data aquired by multi-electrode Stereoelectroencephalography (SEEG)
and Electrocorticography (ECoG) are presented to a delay-type DTCNN with linear weight functions and a 3×1
network topology. This leads to series of signal predictors and according to that to series of prediction errors.
These prediction error series are arranged in a 2 dimensional representation called error profile.<sup>9</sup> This profile
enables the choice of optimal positions for implanting long time electrodes, by means of which perhaps a mostly
effective seizure prediction may become possible. So far data of different patients have been studied in detail
and some distinct electrode points were found showing distinct changes before a seizure onset.
In previous publications it has been shown that the prediction algorithm for multi-layer delay-type DTCNN may be used for the analysis of EEG-signals in order to find precursors of impending epileptic seizures. It has been stated that the application of time efficient training algorithms together with the consideration of symmetric templates lead to a significant decrease of the calculation complexity, allowing the analysis of long-term recordings of EEG-signals. In this contribution EEG-data, covering a total time of 6 days, were studied, applying the BFGS (Broiden-Fletcher-Goldfarb-Shanno) training method. To accomplish a very effective procedure, several symmetries have been tested and template structures leading to higher processing speed and optimal results have been implemented for the long-term studies. Distinct changes occuring before the onsets of impending seizures in the used data set were observed for different prediction parameters.
In 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 nonlinear 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.
0.5% of the world population is suffering from a focal epilepsy. Several actual investigations showed that methods in nonlinear signal processing are important for the derivation of new feature extraction methods to enable the realisation of a portable epilepsy warning system. In this contribution we will present recent results for the pattern detection algorithm which we have proposed in previous investigations. In order to verify our first results we will present results of long time measurements. Furthermore the pattern detection algorithm has been transformed in order to run on the first realization of a possible warning device, which had been presented by Laiho et. al. A detail discussion of the results will be given in the paper.