Output regularization of SVM seizure predictors: Kalman Filter versus the "Firing Power" method.
Two methods for output regularization of support vector machines (SVMs) classifiers were applied for seizure prediction in 10 patients with long-term annotated data. The output of the classifiers were regularized by two methods: one based on the Kalman Filter (KF) and other based on a measure called the "Firing Power" (FP). The FP is a quantification of the rate of the classification in the preictal class in a past time window. In order to enable the application of the KF, the classification problem was subdivided in a two two-class problem, and the real-valued output of SVMs was considered. The results point that the FP method raise less false alarms than the KF approach. However, the KF approach presents an higher sensitivity, but the high number of false alarms turns their applicability negligible in some situations.
Centre for Informatics and Systems, Polo II, University of Coimbra, 3030-290 Coimbra, Portugal. cteixei@dei.uc.pt
Cesar
C
Teixeira
Bruno
B
Direito
Mojtaba
M
Bandarabadi
António
A
Dourado
D004566:Electrodes; D004569:Electroencephalography / Q000379:methods; D004827:Epilepsy / Q000175:diagnosis* / Q000503:physiopathology; D005189:False Positive Reactions; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012640:Seizures / Q000175:diagnosis* / Q000503:physiopathology*; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted; D012984:Software; D060388:Support Vector Machine; D013997:Time Factors