title: Epileptic seizure predictors based on computational intelligence techniques: a comparative study with 278 patients.
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.
authors.affiliation: Centre for Informatics and Systems, University of Coimbra, Portugal. Electronic address: cteixei@gmail.com.
authors.forename: César
authors.initials: C
authors.lastname: Alexandre Teixeira
authors.affiliation: Centre for Informatics and Systems, University of Coimbra, Portugal.
authors.forename: Bruno
authors.initials: B
authors.lastname: Direito
authors.affiliation: Centre for Informatics and Systems, University of Coimbra, Portugal.
authors.forename: Mojtaba
authors.initials: M
authors.lastname: Bandarabadi
authors.affiliation: Centre de Recherche de l'Institut du Cerveau et de la Moelle épinière (CRICM), Paris, France.
authors.forename: Michel
authors.initials: M
authors.lastname: Le Van Quyen
authors.affiliation: Centre de Recherche de l'Institut du Cerveau et de la Moelle épinière (CRICM), Paris, France.
authors.forename: Mario
authors.initials: M
authors.lastname: Valderrama
authors.affiliation: Freiburg Center for Data Analysis and Modeling, University of Freiburg, Freiburg, Germany.
authors.forename: Bjoern
authors.initials: B
authors.lastname: Schelter
authors.affiliation: Epilepsy Center, University Hospital of Freiburg, Freiburg, Germany.
authors.forename: Andreas
authors.initials: A
authors.lastname: Schulze-Bonhage
authors.affiliation: Epilepsy Unit, Groupe Hospitalier Pitié-Salpêtrière, Paris, France.
authors.forename: Vincent
authors.initials: V
authors.lastname: Navarro
authors.affiliation: Centro Hospitalar e Universitário de Coimbra, Coimbra, Portugal.
authors.forename: Francisco
authors.initials: F
authors.lastname: Sales
authors.affiliation: Centre for Informatics and Systems, University of Coimbra, Portugal.
authors.forename: António
authors.initials: A
authors.lastname: Dourado
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D003198:Computer Simulation; D016208:Databases, Factual; D003936:Diagnosis, Computer-Assisted; D004566:Electrodes; D004569:Electroencephalography / Q000379:methods*; D004827:Epilepsy / Q000175:diagnosis* / Q000503:physiopathology*; D005189:False Positive Reactions; D005260:Female; D006801:Humans; D007231:Infant, Newborn; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D011446:Prospective Studies; D012815:Signal Processing, Computer-Assisted; D060388:Support Vector Machine; D055815:Young Adult
keywords: Artificial neural networks; EPILEPSIAE project; Epileptic seizure prediction; European Epilepsy Database; Support vector machines