Objective
Pharmacological therapy with antiepileptic drugs (AEDs) is still the fundamental means for treating epilepsy. In spite of progress in the therapy which has markdly benefited many patients, one third of the patients continue to suffer from uncontrolled seizures, neurotoxic side effects or episodes of both of these events. Therefore, reliable objec- tive criteria for the evaluation of AED efficacy are very important for clinical practice. The aim of this study is to find parameters of the EEG which are able to discern the effects of AEDs. Therefore, EEG parameters have to be found which discriminate best between re- sponders and non-responders and which indicate best the response to treatment (e.g. reduction of spikes, reduction of spike amplitudes).
The working hypothesis is that by means of advanced methods of EEG signal processing and pattern recognition such parameters might be found. These methods have been developed by the partners who are responsible for analysis (in co-operation with the clinical partners). Such advanced methods are neural clustering algorithms, new types of artificial Neural Networks and algorithms for dynamic high-resolution spectral analysis. The clinical examinations will be based on a prospective open-label monotherapy crossover design, where patients with childhood absence epilepsy (CAE) will be included.
The advantage of the suggested study design is the elimination of the effects of drug interactions which cannot be calculated. The disadvantage is the inclusion criteria (i.e. time factor): it will be difficult to recruit a representative number of new patients. But this may be a strong argument in favour of multicentre studies.
Topic(s)
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07740 Jena
Germany