The Schema of the European EPILEPSIAE database for seizure prediction
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1
University Hospital Freiburg, Epilepsy Center, Germany
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2
University of Freiburg, FDM, Germany
Purpose:
With a prevalence of about 1%, epilepsy is considered to be one of the most common serious brain disorders with profound physical, psychological and social consequences. Characteristic symptoms are seizures caused by abnormal neuronal activity that can lead to temporary impairments of motor functions, perception, speech, memory or consciousness. The possibility to predict the occurrence of epileptic seizures, typically by recording and monitoring the electroencephalographic activity (EEG), could enable new therapeutic strategies for the considerable fraction of epilepsy patients that are not treatable by any state of the art therapeutics like anticonvulsive medication or brain surgery.
Funded by the European Union, the EPILEPSIAE project, a 7th Framework Programme with seven clinical, academical and industrial partners in Portugal, Germany, France and Italy, was established to develop and advance prediction algorithms and to deploy these algorithms on a small transportable alarming device. So far, the main concern for the development of prediction algorithms have been limitations in the quality and duration of long-term EEG data that are important for the statistical evaluation of prediction methods. Accordingly, the EPILEPSIAE project is currently gathering the largest and most comprehensive epilepsy database existing worldwide to collect and organize a substantial amount of characteristic patient data for research on seizure prediction methods.
Method:
In contrast to previously existing, by orders of magnitude smaller EEG data collections, the EPILEPSIAE database is a relational database, designed for efficient data organization and access and offering extensive searching capabilities.
Therefore, the 250 surface and 50 intracranial epileptic patients datasets are collected and integrated into the database as common effort of all clinical partners. The datasets comprehend multimodal data including raw data as well as different types of metadata:
* raw EEG data recorded from all clinical partners during long-term monitoring of epilepsy patients
* feature data: features derived from the EEG recordings, which are important for the development and testing of prediction algorithms.
* magnetic resonance imaging (MRI) data (skull stripped for reasons of data anonymization)
* standardized EEG annotations covering information visually perceived by experts about the EEG, e.g. about seizures and interictal events
* clinical metadata: information about patients and their diseases including medical history, imaging findings, anti-epileptic therapy, seizure semiology as well as information about EEG recordings and therefore used electrodes
* feature metadata: supplementary information about the feature algorithms and calculations
Results:
Currently, there are working databases at the partner's sites, all in sync with a replicated content of already more than 150 datasets. This makes the EPILEPSIAE database already the by far largest epilepsy database. Although all project partners use an Oracle database, the schema, as it is presented here, is agnostic of the underlying database system and can easily be adapted to other relational databases. We here only outline the general structure of the schema of the EPILEPSIAE database in figure 1. The complete schema will be visually presented in full detail on the poster.
Conclusions:
Although still work in progress, the EPILEPSIAE database is already the most comprehensive and complete epilepsy database currently existing. Interest for access to the database as well as participation requests from all over the world already give evidence of the emerging acceptance of our database as the de facto standard for databases in the field of epilepsy. This acceptance of our database schema and content is probably the most important impact of the EPILEPSIAE database.
Conference:
Neuroinformatics 2010 , Kobe, Japan, 30 Aug - 1 Sep, 2010.
Presentation Type:
Oral Presentation
Topic:
Computational Neuroscience
Citation:
Ihle
M,
Feldwisch
H,
Schelter
B,
Timmer
J and
Schulze-Bonhage
A
(2010). The Schema of the European EPILEPSIAE database for seizure prediction.
Front. Neurosci.
Conference Abstract:
Neuroinformatics 2010 .
doi: 10.3389/conf.fnins.2010.13.00056
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Received:
11 Jun 2010;
Published Online:
11 Jun 2010.
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Correspondence:
Matthias Ihle, University Hospital Freiburg, Epilepsy Center, Freiburg, Germany, matthias.ihle@uniklinik-freiburg.de