A computational environment for long-term multi-feature and multi-algorithm seizure prediction.

Abstract

The daily life of epilepsy patients is constrained by the possibility of occurrence of seizures. Until now, seizures cannot be predicted with sufficient sensitivity and specificity. Most of the seizure prediction studies have been focused on a small number of patients, and frequently assuming unrealistic hypothesis. This paper adopts the view that for an appropriate development of reliable predictors one should consider long-term recordings and several features and algorithms integrated in one software tool. A computational environment, based on Matlab (®), is presented, aiming to be an innovative tool for seizure prediction. It results from the need of a powerful and flexible tool for long-term EEG/ECG analysis by multiple features and algorithms. After being extracted, features can be subjected to several reduction and selection methods, and then used for prediction. The predictions can be conducted based on optimized thresholds or by applying computational intelligence methods. One important aspect is the integrated evaluation of the seizure prediction characteristic of the developed predictors.

DOI: 10.1109/IEMBS.2010.5627637

3 Figures and Tables

Cite this paper

@article{Teixeira2010ACE, title={A computational environment for long-term multi-feature and multi-algorithm seizure prediction.}, author={C{\'e}sar Alexandre Teixeira and Bruno Direito and Rafael P. Costa and Mario Valderrama and Hinnerk Feldwisch-Drentrup and Stavros Nikolopoulos and Michel Le Van Quyen and Bj{\"{o}rn Schelter and Ant{\'o}nio Dourado}, journal={Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference}, year={2010}, volume={2010}, pages={6341-4} }