Estimating the distribution of dynamic invariants: illustrated with an application to human photo-plethysmographic time series |
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Authors: | Michael Small |
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Institution: | (1) Department of Electronic and Information Engineering, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong |
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Abstract: | Dynamic invariants are often estimated from experimental time series with the aim of differentiating between different physical
states in the underlying system. The most popular schemes for estimating dynamic invariants are capable of estimating confidence
intervals, however, such confidence intervals do not reflect variability in the underlying dynamics. We propose a surrogate
based method to estimate the expected distribution of values under the null hypothesis that the underlying deterministic dynamics
are stationary. We demonstrate the application of this method by considering four recordings of human pulse waveforms in differing
physiological states and show that correlation dimension and entropy are insufficient to differentiate between these states.
In contrast, algorithmic complexity can clearly differentiate between all four rhythms. |
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Keywords: | |
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