Abstract
The seizure prediction problem has been addressed by many researchers from very different fields for more than three decades. The vision of an implantable seizure prediction device may become reality soon: the first clinical study of such a device has been realized very recently and other realizations are not far behind. Cellular Nonlinear Networks (CNN) were firstly introduced by Chua and Yang in 1988 and later extended to an inherently parallel processing framework called the CNN Universal Machine (CNN-UM). This framework combines high computational power with low power consumption and miniaturized design—making it a very promising basis for the realization of a seizure warning device. In this contribution, we compare the seizure prediction performance of an eigenvalue based PCA-preprocessing followed by a nonlinear CNN signal prediction to the performance of a linear signal prediction approach followed by a level-crossing behavior analysis as well as to the performance of a combination of the two methods.
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