6533b870fe1ef96bd12cfba1
RESEARCH PRODUCT
Parametric and nonparametric methods to generate time-varying surrogate data.
Luca FaesKi H. ChonGiandomenico NolloHe Zhaosubject
Mathematical optimizationTime FactorsNormal DistributionBiomedical EngineeringBlood PressureHealth InformaticsStatistics NonparametricSurrogate dataNormal distributionsymbols.namesakeHeart RateHumansCoherence (signal processing)Computer Simulation1707MathematicsParametric statisticsFourier AnalysisNonparametric statisticsRegression analysisAutoregressive modelFourier analysisData Interpretation StatisticalSignal ProcessingSettore ING-INF/06 - Bioingegneria Elettronica E InformaticasymbolsRegression AnalysisAlgorithmAlgorithmsdescription
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. 7copy; 2008 IEEE.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2009-01-24 |