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C2: Prediction of epileptic seizures by modelling and analyzing abnormal synchronization in cortical networks

Andreas Schulze-BonhageI and Jens TimmerC,M


I
= Epilepsy Centre, University Medical Center
C = Department of Physics
M = Center for Data Analysis and Modeling (FDM)


Scientific background

Epilepsy, one of the most common CNS diseases (800.000 patients in Germany), is characterized by spontaneous seizures due to abnormal synchronization of cortical neural activity. Presently, the disease can be controlled in 2/3 of the patients, in the remainder the unpredictability of seizures leads to major impairments with inherent physical risks of loss of consciousness and motor control. During the last ten years, attempts have been made to predict seizures from EEG data using linear and non-linear time series analysis. However, the significance of results regarding clinical applicability and neurophysiological adequacy remains uncertain. Most studies, including ours on intracranial long term EEG recordings from patients undergoing presurgical evaluation, applied univariate measures of cortical dynamics, not taking into account the neuronal synchronization underlying the development of ictal activity16. Thus, the investigation of synchronizing non-linear dynamical systems has become a major focus in recent studies on time series analysis and applying these methods for analyzing data from such systems.


Objectives

The project works on identifying measures reflecting the dynamics of abnormal synchronization of cortical activity which provide sufficient sensitivity and specificity to be applied in seizure prediction in a clinical setting. This involves the establishment of a well-documented, high quality data base for human long term clinical EEG data. In parallel, we investigate mathematical models which capture the synchronizing dynamics leading to the manifestation of ictal activity. We develop data analysis tools able to identify and predict epileptic-like activity in the mathematical models. Promising measures are evaluated for clinical applicability on the human EEG data base.

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Basic operation of a prediction method during an interictal (between seizure) and a preictal (before a seizure) period. Seizure onset is marked by vertical lines. (a) Examples of EEG recordings and (b) exemplary time course of a feature extracted by a seizure prediction algorithm. The solid, horizontal line indicates the threshold for raising alarms. Alarm events and two consecutive time intervals characterizing a prediction, the seizure prediction horizon SPH
and seizure occurrence period SOP are illustrated in(c).

 

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Basic functioning of a future closed-loop intervention device: The long-term goal of the project is to develop algorithms that are able to predict epileptic seizures with high sensitivity and specificity. These prediction algorithms could be utilized in a "brain defibrillator", in analogy to cardiac defibrillators. A prediction of seizures at an early stage could trigger an intervention to suppress the upcoming seizure by for instance electrical stimulation. Alternatively, a seizure warning device could be invented that enables behavioral adjustments.

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