A Computational Environment for Long-Term Multi-Feature and Multi-Algorithm Seizure Prediction

C. A. Teixeira, B. Direito, R. P. Costa, M. Valderrama, H. Feldwisch-Drentrup, S. Nikolopoulos, M. Le Van Quyen, B. Schelter, A. Dourado

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)


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 (R), 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.

Original languageEnglish
Title of host publication2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
Place of PublicationNew York
PublisherIEEE Press
Number of pages4
ISBN (Print)978-1-4244-4124-2
Publication statusPublished - 2010
Event32nd Annual International Conference of the IEEE Engineering-in-Medicine-and-Biology-Society (EMBC 10) - Buenos Aires
Duration: 30 Aug 20104 Sep 2010


Conference32nd Annual International Conference of the IEEE Engineering-in-Medicine-and-Biology-Society (EMBC 10)
CityBuenos Aires

Cite this