title: A comparison of non-linear non-parametric models for epilepsy data.
abstract: EEG spike and wave (SW) activity has been described through a non-parametric stochastic model estimated by the Nadaraya-Watson (NW) method. In this paper the performance of the NW, the local linear polynomial regression and support vector machines (SVM) methods were compared. The noise-free realizations obtained by the NW and SVM methods reproduced SW better than as reported in previous works. The tuning parameters had to be estimated manually. Adding dynamical noise, only the NW method was capable of generating SW similar to training data. The standard deviation of the dynamical noise was estimated by means of the correlation dimension.
authors.affiliation: The Graduate University for Advanced Studies, 4-6-7 Minami Azabu, Minato-ku 106-0047, Tokyo, Japan. miwake1@ism.ac.jp
authors.forename: F
authors.initials: F
authors.lastname: Miwakeichi
authors.forename: R
authors.initials: R
authors.lastname: Ramirez-Padron
authors.forename: P A
authors.initials: PA
authors.lastname: Valdes-Sosa
authors.forename: T
authors.initials: T
authors.lastname: Ozaki
mesh_terms: D003627:Data Interpretation, Statistical; D004569:Electroencephalography / Q000706:statistics & numerical data*; D004827:Epilepsy / Q000503:physiopathology*; D006801:Humans; D016014:Linear Models; D008959:Models, Neurological; D017711:Nonlinear Dynamics; D012984:Software; D013269:Stochastic Processes