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- W114927478 abstract "Abstract—Testing for nonlinearity is one of the most importantpreprocessing steps in nonlinear time series analysis. Typically,this is done by means of the linear surrogate data methods.But it is a known fact that the validity of the results heavilydepends on the stationarity of the time series. Since mostphysiological signals are non-stationary, it is easy to falsely detectnonlinearity using the linear surrogate data methods. In thisdocument, we propose a methodology to extend the procedurefor generating constrained surrogate time series in order to assessnonlinearity in non-stationary data. The method is based onthe band-phase-randomized surrogates, which consists (contraryto the linear surrogate data methods) in randomizing only aportion of the Fourier phases in the high frequency band.Analysis of simulated time series showed that in comparisonto the linear surrogate data method, our method is able todiscriminate between linear stationarity, linear non-stationaryand nonlinear time series. When applying our methodology toheart rate variability (HRV) time series that present spikesand other kinds of nonstationarities, we where able to obtainsurrogate time series that look like the data and preserves linearcorrelations, something that is not possible to do with the existingsurrogate data methods.Index Terms—Computational methods in statistical physicsand nonlinear dynamics, hypothesis testing, surrogate data, heartrate variability." @default.
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- W114927478 date "2011-01-31" @default.
- W114927478 modified "2023-10-17" @default.
- W114927478 title "Band-phase-randomized Surrogates to assess nonlinearity in non-stationary time series" @default.
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