Spatial-dependence recurrence sample entropy

Tuan D. Pham and Hong Yan

Physica A: Statistical Mechanics and its Applications, 2018, vol. 494, issue C, 581-590

Abstract: Measuring complexity in terms of the predictability of time series is a major area of research in science and engineering, and its applications are spreading throughout many scientific disciplines, where the analysis of physiological signals is perhaps the most widely reported in literature. Sample entropy is a popular measure for quantifying signal irregularity. However, the sample entropy does not take sequential information, which is inherently useful, into its calculation of sample similarity. Here, we develop a method that is based on the mathematical principle of the sample entropy and enables the capture of sequential information of a time series in the context of spatial dependence provided by the binary-level co-occurrence matrix of a recurrence plot. Experimental results on time-series data of the Lorenz system, physiological signals of gait maturation in healthy children, and gait dynamics in Huntington’s disease show the potential of the proposed method.

Keywords: Time series; Irregularity; Sample entropy; Recurrence plot; Binary-level co-occurrence matrix; Spatial dependence (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:494:y:2018:i:c:p:581-590

DOI: 10.1016/j.physa.2017.12.015

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