Epileptic seizure predictors based on computational intelligence techniques: a comparative study with 278 patients.

Details

Serval ID
serval:BIB_B0A44EECFCB6
Type
Article: article from journal or magazin.
Collection
Publications
Title
Epileptic seizure predictors based on computational intelligence techniques: a comparative study with 278 patients.
Journal
Computer methods and programs in biomedicine
Author(s)
Alexandre Teixeira C., Direito B., Bandarabadi M., Le Van Quyen M., Valderrama M., Schelter B., Schulze-Bonhage A., Navarro V., Sales F., Dourado A.
ISSN
1872-7565 (Electronic)
ISSN-L
0169-2607
Publication state
Published
Issued date
05/2014
Peer-reviewed
Oui
Volume
114
Number
3
Pages
324-336
Language
english
Notes
Publication types: Comparative Study ; Journal Article ; Multicenter Study ; Research Support, Non-U.S. Gov't
Publication Status: ppublish
Abstract
The ability of computational intelligence methods to predict epileptic seizures is evaluated in long-term EEG recordings of 278 patients suffering from pharmaco-resistant partial epilepsy, also known as refractory epilepsy. This extensive study in seizure prediction considers the 278 patients from the European Epilepsy Database, collected in three epilepsy centres: Hôpital Pitié-là-Salpêtrière, Paris, France; Universitätsklinikum Freiburg, Germany; Centro Hospitalar e Universitário de Coimbra, Portugal. For a considerable number of patients it was possible to find a patient specific predictor with an acceptable performance, as for example predictors that anticipate at least half of the seizures with a rate of false alarms of no more than 1 in 6 h (0.15 h⁻¹). We observed that the epileptic focus localization, data sampling frequency, testing duration, number of seizures in testing, type of machine learning, and preictal time influence significantly the prediction performance. The results allow to face optimistically the feasibility of a patient specific prospective alarming system, based on machine learning techniques by considering the combination of several univariate (single-channel) electroencephalogram features. We envisage that this work will serve as benchmark data that will be of valuable importance for future studies based on the European Epilepsy Database.
Keywords
Adult, Aged, Algorithms, Computer Simulation, Databases, Factual, Diagnosis, Computer-Assisted, Electrodes, Electroencephalography/methods, Epilepsy/diagnosis, Epilepsy/physiopathology, False Positive Reactions, Female, Humans, Infant, Newborn, Male, Middle Aged, Neural Networks, Computer, Prospective Studies, Signal Processing, Computer-Assisted, Support Vector Machine, Young Adult, Artificial neural networks, EPILEPSIAE project, Epileptic seizure prediction, European Epilepsy Database, Support vector machines
Pubmed
Web of science
Create date
06/07/2021 15:28
Last modification date
04/05/2024 7:07
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