title: Parametric and nonparametric methods to generate time-varying surrogate data.
abstract: We present both nonparametric and parametric approaches to generating time-varying surrogate data. Nonparametric and parametric approaches are based on the use of the short-time Fourier transform and a time-varying autoregressive model, respectively. Time-varying surrogate data (TVSD) can be used to determine the statistical significance of the linear and nonlinear coherence function estimates. Two advantages of the TVSD are that it keeps one from having to make an arbitrary decision about the significance of the coherence value, and it properly takes into account statistical significance levels, which may change with time. Our simulation examples and experimental results on blood pressure and heart rate data demonstrate the efficacy and applicability of the proposed TVSD methods.
authors.affiliation: Department of Biomedical Engineering, Stony Brook University, NY 11794, USA.
authors.forename: He
authors.initials: H
authors.lastname: Zhao
authors.forename: Luca
authors.initials: L
authors.lastname: Faes
authors.forename: Giandomenico
authors.initials: G
authors.lastname: Nollo
authors.forename: Ki H
authors.initials: KH
authors.lastname: Chon
mesh_terms: D000465:Algorithms; D001794:Blood Pressure; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D005583:Fourier Analysis; D006339:Heart Rate; D006801:Humans; D016011:Normal Distribution; D012044:Regression Analysis; D018709:Statistics, Nonparametric; D013997:Time Factors