title: Early detection of epileptic seizures based on parameter identification of neural mass model.
abstract: Physiologically based models are attractive for seizure detection, as their parameters can be explicitly related to neurological mechanisms. We propose an early seizure detection algorithm based on parameter identification of a neural mass model. The occurrence of a seizure is detected by analysing the time shift of key model parameters. The algorithm was evaluated against the manual scoring of a human expert on intracranial EEG samples from 16 patients suffering from different types of epilepsy. Results suggest that the algorithm is best suited for patients suffering from temporal lobe epilepsy (sensitivity was 95.0% ± 10.0% and false positive rate was 0.20 ± 0.22 per hour).
authors.affiliation: Laboratories of Image, Signal processing and Acoustics (LISA), CP165/51, Université Libre de Bruxelles (ULB), Avenue F.D. Roosevelt 50, B1050 Brussels, Belgium.
authors.forename: Gatien
authors.initials: G
authors.lastname: Hocepied
authors.forename: Benjamin
authors.initials: B
authors.lastname: Legros
authors.forename: Patrick
authors.initials: P
authors.lastname: Van Bogaert
authors.forename: Francis
authors.initials: F
authors.lastname: Grenez
authors.forename: Antoine
authors.initials: A
authors.lastname: Nonclercq
mesh_terms: D000293:Adolescent; D000328:Adult; D000465:Algorithms; D002648:Child; D002675:Child, Preschool; D004569:Electroencephalography / Q000379:methods*; D004827:Epilepsy / Q000175:diagnosis* / Q000503:physiopathology*; D006801:Humans; D008875:Middle Aged; D008959:Models, Neurological; D012372:ROC Curve; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D055815:Young Adult
keywords: Brain modelling; Epilepsy; Nonlinear systems; Nonstationarity; Seizure detection