The First International Collaborative Workshop on Seizure Prediction was held at the Department of Epileptology, University of Bonn, in Bonn, Germany on April 24-28, 2002. Organized by the Universities of Pennsylvania and Bonn, and funded by grants from the American Epilepsy Society, the German Section of the International League against Epilepsy, and the German Section of the International Fed...

s are listed alphabetically by title within topics. Poster Topic Page Number Numbers 1-16 Seizure Prediction and Detection ............................................. Page 30 17-26 Neural Models and Mechanisms .............................................. Page 42 27-30 Epilepsy Connectome .............................................................. Page 50 31-35 Seizure Intervention .........

PURPOSE A subset of patients with epilepsy successfully self-predicted seizures in a paper diary study. We conducted an e-diary study to ensure that prediction precedes seizures, and to characterize the prodromal features and time windows that underlie self-prediction. METHODS Subjects 18 or older with localization-related epilepsy (LRE) and ≥3 seizures per month maintained an e-diary, report...

A statistical method for finding the optimal preictal period to be used in epileptic seizure prediction algorithms is presented. As supervised machine learning methods need labeled training samples, the adequate selection of preictal period plays a key role in the training of an efficient classifier employed in seizure prediction. The proposed method uses amplitude distribution histograms of a ...

s With great interest we read the article of haovalitwongse et al. (2005) concerning the perormance of an automated seizure warning system ASWS) based on concepts from nonlinear dynamcs. To assess the performance of their algorithm, the uthors divided long-term intracranial EEG data from 0 patients into training and test data sets. For the trainng data, the authors reported a high prediction pe...

Many studies have focused on the nonlinear analysis of electroencephalography (EEG) mainly for the characterization of epileptic brain states. It is assumed that at least two states of the epileptic brain are possible: the interictal state characterized by a normal apparently random, steady-state EEG ongoing activity; and the ictal state that is characterized by paroxysmal occurrence of synchro...

PURPOSE In recent years, a variety of methods developed in the field of linear and nonlinear time series analysis have been used to obtain reliable predictions of epileptic seizures. Because individual methods for seizure prediction so far have shown statistical significance but insufficient performance for clinical applications, we investigated possible improvements by combining algorithms cap...

Despite the unpredictability of epileptic seizures, many patients report that they can anticipate seizure occurrence. Using certain alert symptoms (i.e., auras, prodromes, precipitant factors), patients can adopt behaviors to avoid injury during and after the seizure or may implement spontaneous cognitive and emotional strategies to try to control the seizure itself. From the patient's view poi...

Epilepsy is the second most common neurological disorder, affecting 0.6-0.8% of the world's population. In this neurological disorder, abnormal activity of the brain causes seizures, the nature of which tend to be sudden. Antiepileptic Drugs (AEDs) are used as long-term therapeutic solutions that control the condition. Of those treated with AEDs, 35% become resistant to medication. The unpredic...

From the very beginning the seizure prediction community faced problems concerning evaluation, standardization, and reproducibility of its studies. One of the main reasons for these shortcomings was the lack of access to high-quality long-term electroencephalography (EEG) data. In this article we present the EPILEPSIAE database, which was made publicly available in 2012. We illustrate its conte...