0000000000281152

AUTHOR

He Zhao

showing 2 related works from this author

Time-Varying Surrogate Data to Assess Nonlinearity in Nonstationary Time Series: Application to Heart Rate Variability

2009

We propose a method to extend to time-varying (TV) systems the procedure for generating typical surrogate time series, in order to test the presence of nonlinear dynamics in potentially nonstationary signals. The method is based on fitting a TV autoregressive (AR) model to the original series and then regressing the model coefficients with random replacements of the model residuals to generate TV AR surrogate series. The proposed surrogate series were used in combination with a TV sample entropy (SE) discriminating statistic to assess nonlinearity in both simulated and experimental time series, in comparison with traditional time-invariant (TIV) surrogates combined with the TIV SE discrimin…

AdultTime FactorsComputer scienceRestBiomedical EngineeringSurrogate dataHeart RateStatisticsHumansHeart rate variabilityEntropy (information theory)Computer SimulationNonstationarityEntropy (energy dispersal)Time seriesEntropy (arrow of time)StatisticModels StatisticalEntropy (statistical thermodynamics)RespirationNonlinear dynamicModels CardiovascularComplexitySample entropyNonlinear systemNonlinear DynamicsAutoregressive modelSettore ING-INF/06 - Bioingegneria Elettronica E InformaticaSurrogate dataTime-varying (TV) autoregressive (AR) modelHeart rate variability (HRV)AlgorithmsEntropy (order and disorder)IEEE Transactions on Biomedical Engineering
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Parametric and nonparametric methods to generate time-varying surrogate data.

2009

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 …

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 AnalysisAlgorithmAlgorithms
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