Biomaterial Database

A comparison of non-linear non-parametric models for epilepsy data. 2001

F Miwakeichi, and R Ramirez-Padron, and P A Valdes-Sosa, and T Ozaki

The Graduate University for Advanced Studies, 4-6-7 Minami Azabu, Minato-ku 106-0047, Tokyo, Japan. miwake1@ism.ac.jp

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.

UI	MeSH Term	Description	Entries
D008959	Models, Neurological	Theoretical representations that simulate the behavior or activity of the neurological system, processes or phenomena; includes the use of mathematical equations, computers, and other electronic equipment.	Neurologic Models,Model, Neurological,Neurologic Model,Neurological Model,Neurological Models,Model, Neurologic,Models, Neurologic
D003627	Data Interpretation, Statistical	Application of statistical procedures to analyze specific observed or assumed facts from a particular study.	Data Analysis, Statistical,Data Interpretations, Statistical,Interpretation, Statistical Data,Statistical Data Analysis,Statistical Data Interpretation,Analyses, Statistical Data,Analysis, Statistical Data,Data Analyses, Statistical,Interpretations, Statistical Data,Statistical Data Analyses,Statistical Data Interpretations
D004569	Electroencephalography	Recording of electric currents developed in the brain by means of electrodes applied to the scalp, to the surface of the brain, or placed within the substance of the brain.	EEG,Electroencephalogram,Electroencephalograms
D004827	Epilepsy	A disorder characterized by recurrent episodes of paroxysmal brain dysfunction due to a sudden, disorderly, and excessive neuronal discharge. Epilepsy classification systems are generally based upon: (1) clinical features of the seizure episodes (e.g., motor seizure), (2) etiology (e.g., post-traumatic), (3) anatomic site of seizure origin (e.g., frontal lobe seizure), (4) tendency to spread to other structures in the brain, and (5) temporal patterns (e.g., nocturnal epilepsy). (From Adams et al., Principles of Neurology, 6th ed, p313)	Aura,Awakening Epilepsy,Seizure Disorder,Epilepsy, Cryptogenic,Auras,Cryptogenic Epilepsies,Cryptogenic Epilepsy,Epilepsies,Epilepsies, Cryptogenic,Epilepsy, Awakening,Seizure Disorders
D006801	Humans	Members of the species Homo sapiens.	Homo sapiens,Man (Taxonomy),Human,Man, Modern,Modern Man
D012984	Software	Sequential operating programs and data which instruct the functioning of a digital computer.	Computer Programs,Computer Software,Open Source Software,Software Engineering,Software Tools,Computer Applications Software,Computer Programs and Programming,Computer Software Applications,Application, Computer Software,Applications Software, Computer,Applications Softwares, Computer,Applications, Computer Software,Computer Applications Softwares,Computer Program,Computer Software Application,Engineering, Software,Open Source Softwares,Program, Computer,Programs, Computer,Software Application, Computer,Software Applications, Computer,Software Tool,Software, Computer,Software, Computer Applications,Software, Open Source,Softwares, Computer Applications,Softwares, Open Source,Source Software, Open,Source Softwares, Open,Tool, Software,Tools, Software
D013269	Stochastic Processes	Processes that incorporate some element of randomness, used particularly to refer to a time series of random variables.	Process, Stochastic,Stochastic Process,Processes, Stochastic
D016014	Linear Models	Statistical models in which the value of a parameter for a given value of a factor is assumed to be equal to a + bx, where a and b are constants. The models predict a linear regression.	Linear Regression,Log-Linear Models,Models, Linear,Linear Model,Linear Regressions,Log Linear Models,Log-Linear Model,Model, Linear,Model, Log-Linear,Models, Log-Linear,Regression, Linear,Regressions, Linear
D017711	Nonlinear Dynamics	The study of systems which respond disproportionately (nonlinearly) to initial conditions or perturbing stimuli. Nonlinear systems may exhibit "chaos" which is classically characterized as sensitive dependence on initial conditions. Chaotic systems, while distinguished from more ordered periodic systems, are not random. When their behavior over time is appropriately displayed (in "phase space"), constraints are evident which are described by "strange attractors". Phase space representations of chaotic systems, or strange attractors, usually reveal fractal (FRACTALS) self-similarity across time scales. Natural, including biological, systems often display nonlinear dynamics and chaos.	Chaos Theory,Models, Nonlinear,Non-linear Dynamics,Non-linear Models,Chaos Theories,Dynamics, Non-linear,Dynamics, Nonlinear,Model, Non-linear,Model, Nonlinear,Models, Non-linear,Non linear Dynamics,Non linear Models,Non-linear Dynamic,Non-linear Model,Nonlinear Dynamic,Nonlinear Model,Nonlinear Models,Theories, Chaos,Theory, Chaos