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Although there are numerous studies exploring basic neuronal mechanisms that are likely to be associated with seizures, to date no definite information is available as to how, when, or why a seizure occurs in humans. The fact that seizures occur without warning in the majority of cases is one of the most disabling aspects of epilepsy. If it were possible to identify preictal precursors from the EEG of epilepsy patients, therapeutic possibilities and quality of life could improve dramatically. The last three decades have witnessed a rapid increase in the development of new EEG analysis techniques that appear to be capable of defining seizure precursors. Since the 1970s, studies on seizure prediction have advanced from preliminary descriptions of preictal phenomena and proof of principle studies via controlled studies to studies on continuous multiday recordings. At present, it is unclear whether prospective algorithms can predict seizures. If prediction algorithms are to be used in invasive seizure intervention techniques in humans, they must be proven to perform considerably better than a random predictor. The authors present an overview of the field of seizure prediction, its history, accomplishments, recent controversies, and potential for future development.

We propose a method for estimating nonlinear interdependences between time series using cellular nonlinear networks. Our approach is based on the nonlinear dynamics of interacting nonlinear elements. We apply it to time series of coupled nonlinear model systems and to electroencephalographic time series from an epilepsy patient, and we show that an accurate approximation of symmetric and asymmetric realizations of a nonlinear interdependence measure can be achieved, thus allowing one to detect the strength and direction of couplings.

We investigate the generalization capability of our recently proposed CNN-based approach to measure the strength of generalized synchronization in EEG recordings from epilepsy patients. With an in-sample optimization on short-lasting EEG data taken from two recording sites of a single patient we obtain a CNN with polynomial-type templates that allows us to approximate the strength of generalized synchronization in continuous long-lasting multichannel EEG recordings from this patient at a high accuracy. In an out-of-sample study we use the same CNN to analyze days of multichannel EEG data from other patients and observe that the strength of generalized synchronization between different brain regions in different patients can be approximated with a sufficient accuracy. These inter-and intraindividual generalization properties render CNN highly attractive for the development of miniaturized seizure prediction devices.

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