title: Real-time epileptic seizure prediction using AR models and support vector machines.
abstract: This paper addresses the prediction of epileptic seizures from the online analysis of EEG data. This problem is of paramount importance for the realization of monitoring/control units to be implanted on drug-resistant epileptic patients. The proposed solution relies in a novel way on autoregressive modeling of the EEG time series and combines a least-squares parameter estimator for EEG feature extraction along with a support vector machine (SVM) for binary classification between preictal/ictal and interictal states. This choice is characterized by low computational requirements compatible with a real-time implementation of the overall system. Moreover, experimental results on the Freiburg dataset exhibited correct prediction of all seizures (100 % sensitivity) and, due to a novel regularization of the SVM classifier based on the Kalman filter, also a low false alarm rate.
authors.affiliation: Department of Systems and Informatics, University of Florence, Florence 50139, Italy. chisci@dsi.unifi.it
authors.forename: Luigi
authors.initials: L
authors.lastname: Chisci
authors.forename: Antonio
authors.initials: A
authors.lastname: Mavino
authors.forename: Guido
authors.initials: G
authors.lastname: Perferi
authors.forename: Marco
authors.initials: M
authors.lastname: Sciandrone
authors.forename: Carmelo
authors.initials: C
authors.lastname: Anile
authors.forename: Gabriella
authors.initials: G
authors.lastname: Colicchio
authors.forename: Filomena
authors.initials: F
authors.lastname: Fuggetta
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003199:Computer Systems; D003627:Data Interpretation, Statistical; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D004827:Epilepsy / Q000175:diagnosis*; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity