Differentiation of linearly correlated noise from chaos in a biologic system using surrogate data

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Abstract
Experimental time-series of human H-reflexes were analyzed for the presence of fractal structure or deterministic chaos. Surrogate data sets consisting of stochastic time-series with preservation of selected properties of the experimental time-series were used as mathematical controls. Artifacts generated during the analysis of the experimental data are identified, and shown to be due to linear correlation in the original time-series. The method is simple and generally applicable to the non-linear analysis of time-series from any experimental system.