Estimating the distribution of dynamic invariants: Illustrated with an application to human photo-plethysmographic time series

Research output: Chapter in Book/Conference paperChapter

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, owever, 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.

Original languageEnglish
Title of host publicationModels and Applications of Chaos Theory in Modern Sciences
PublisherCRC Press
Pages473-486
Number of pages14
ISBN (Electronic)9781439883402
ISBN (Print)9781138114852
Publication statusPublished - 7 Sep 2011
Externally publishedYes

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Small, M. (2011). Estimating the distribution of dynamic invariants: Illustrated with an application to human photo-plethysmographic time series. In Models and Applications of Chaos Theory in Modern Sciences (pp. 473-486). CRC Press.