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.