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Seizure anticipation by patients with focal and generalized epilepsy: a multicentre assessment of premonitory symptoms - PubMed

Multicenter Study

Seizure anticipation by patients with focal and generalized epilepsy: a multicentre assessment of premonitory symptoms

Andreas Schulze-Bonhage et al. Epilepsy Res. 2006 Jul.

Abstract

Purpose: To assess subjective seizure anticipation in patients with focal and generalized epilepsy.

Methods: Five hundred consecutively recruited out-patients (251 male, 249 female, mean age 38.1 year) from three German tertiary epilepsy referral centres filled out questionnaires regarding subjective anticipation of seizures by at least 30 min and to timing and semiologic characteristics of their premonitory symptoms versus those of ictal phenomena. Patients were not regarded as having prodromi if the semiology of symptoms reported long before a seizure was identical to auras.

Results: 6.2% of patients reported that they were able to anticipate seizures. Premonitory symptoms were classified as stereotyped in all but one patient. An intraindividual semiologic analysis showed that the majority of these patients had symptoms, which were distinct from ictal experiences during auras. Seizure anticipation was reported both by patients with focal and idiopathic generalized epilepsy. The median estimated time interval between occurrence of premonitory symptoms and seizure onset was 90 min.

Conclusions: This study gives evidence that both patients with focal and idiopathic generalized epilepsy may subjectively anticipate the occurrence of epileptic seizures. Premonitory symptoms are distinct from auras in terms of semiology and time of occurrence. The lower percentage of patients regarded as having premonitory symptoms as compared to some earlier reports may be related to stricter criteria and to the exclusion of auras, which could directly evolve into seizures, and other ictal events. Premonitory symptoms occur at similar periods prior to seizures as anticipatory EEG-changes have been reported using methods from time series analysis.

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