Recent Results on the Prediction of EEG Signals in Epilepsy by Discrete-Time Cellular Neural Networks (DTCNN)
Christian Niederhofer, Ronald Tetzlaff · 2005
In different investigations it has been shown that nonlinear signal processing can contribute to the task of finding precursors of impending epileptic seizures in the case of a focal epilepsy. Various approaches to this feature extraction problem have been made including Volterra-systems, wavelet-analysis and cellular neural networks (CNN). This paper gives a detailed analysis of a recently proposed prediction algorithm based on a multi-layer delay-time DTCNN. The aim of this contribution is to reduce the high computation complexity caused by the permanent application of a supervised optimization procedure for successive data segments of an EEG recording. Thereby, the prediction algorithm is studied by using different optimization procedures, different network topologies and different template symmetries.