title: A Realistic Seizure Prediction Study Based on Multiclass SVM.
abstract: A patient-specific algorithm, for epileptic seizure prediction, based on multiclass support-vector machines (SVM) and using multi-channel high-dimensional feature sets, is presented. The feature sets, combined with multiclass classification and post-processing schemes aim at the generation of alarms and reduced influence of false positives. This study considers 216 patients from the European Epilepsy Database, and includes 185 patients with scalp EEG recordings and 31 with intracranial data. The strategy was tested over a total of 16,729.80[Formula: see text]h of inter-ictal data, including 1206 seizures. We found an overall sensitivity of 38.47% and a false positive rate per hour of 0.20. The performance of the method achieved statistical significance in 24 patients (11% of the patients). Despite the encouraging results previously reported in specific datasets, the prospective demonstration on long-term EEG recording has been limited. Our study presents a prospective analysis of a large heterogeneous, multicentric dataset. The statistical framework based on conservative assumptions, reflects a realistic approach compared to constrained datasets, and/or in-sample evaluations. The improvement of these results, with the definition of an appropriate set of features able to improve the distinction between the pre-ictal and nonpre-ictal states, hence minimizing the effect of confounding variables, remains a key aspect.
authors.affiliation: 1 Institute for Biomedical Imaging and Life Sciences, Faculty of Medicine, University of Coimbra Coimbra, Portugal.
authors.forename: Bruno
authors.initials: B
authors.lastname: Direito
authors.affiliation: 2 Department of Informatics Engineering, University of Coimbra, Portugal.
authors.forename: César A
authors.initials: CA
authors.lastname: Teixeira
authors.affiliation: 3 University Centre Hospitals of Coimbra, Coimbra, Portugal.
authors.forename: Francisco
authors.initials: F
authors.lastname: Sales
authors.affiliation: 1 Institute for Biomedical Imaging and Life Sciences, Faculty of Medicine, University of Coimbra Coimbra, Portugal.
authors.forename: Miguel
authors.initials: M
authors.lastname: Castelo-Branco
authors.affiliation: 2 Department of Informatics Engineering, University of Coimbra, Portugal.
authors.forename: António
authors.initials: A
authors.lastname: Dourado
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D001921:Brain / Q000000981:diagnostic imaging* / Q000503:physiopathology; D002648:Child; D002675:Child, Preschool; D016208:Databases, Factual; D004569:Electroencephalography / Q000379:methods*; D004827:Epilepsy / Q000000981:diagnostic imaging* / Q000503:physiopathology; D005060:Europe; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D057285:Precision Medicine / Q000379:methods; D011379:Prognosis; D011446:Prospective Studies; D012640:Seizures / Q000000981:diagnostic imaging* / Q000503:physiopathology; D012680:Sensitivity and Specificity; D060388:Support Vector Machine; D055815:Young Adult
keywords: Epilepsy; machine learning; prospective; seizure prediction