ABSTRACT
Epilepsy is a neurological condition characterized by intermittent periods of abnormal states or seizures that in most cases strike abruptly and without clear warning. In some cases, it is possible to localize the source of the abnormal behavior in the brain (localization-related epilepsy); in others, this does not appear to be feasible. The possibility to predict these dynamic transitions both in time and location in the brain may lead to a new realm of epilepsy treatment, hopefully including the treatment of those patients where medication has failed. For a review on the possible impact of seizure prediction we refer to Litt and Lehnertz (2002). Here, we note that one of the most realistic future therapies, namely the control of epileptic seizures using electrical stimulation, is still in the early stages of development. Existing clinical trials utilize intracranial electrical stimulation consisting of periodic high-frequency (100-to 200-Hz) bursts that are administered at regular intervals (Velasco et al. 2007). Such schemes are referred to as open-loop paradigms as they do not take into account the state of the neuronal system since they simply interfere with the ongoing activity of the tissue that is being stimulated. More advanced, state-dependent schemes called closed-loop paradigms are in development (Osorio et al. 2005). Their success relies on the possibility to either foresee an impending seizure or to detect its occurrence very early and only then apply electrical stimulation. A special class of these closed-loop approaches uses properties of the measured EEG signal to determine the exact timing of the stimulation (Osorio and Frei 2009) or even to generate an appropriate seizure suppression stimulation waveform (Ullah and Schiff 2009). We refer to the
12.1 Introduction .......................................................................................................................... 175 12.2 Computer Models and Phenomenological Validation for the Generic Scenarios of
Transition from “Normal” to Epileptic States ...................................................................... 176 12.2.1 General Classi–cation ............................................................................................... 176 12.2.2 Metaphoric and Realistic Models ............................................................................. 179
12.3 Prediction and Predictability ................................................................................................ 184 12.3.1 De–nition .................................................................................................................. 184 12.3.2 Relative Phase Clustering Index ............................................................................... 185 12.3.3 Computer Model Explanation of rPCI ...................................................................... 189
12.4 State-Reactive Control of Model Seizures in Multistable Systems ...................................... 192 12.5 Conclusions and Discussion .................................................................................................. 196 References ...................................................................................................................................... 197
latter as state-reactive control paradigms. For a recent survey on the current approaches for seizure control by electrical stimulation and the ongoing clinical trials see Jobst (2009).
