Spatial-dependence recurrence sample entropy

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

18 Scopus Citations
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Original languageEnglish
Pages (from-to)581-590
Journal / PublicationPhysica A: Statistical Mechanics and its Applications
Online published8 Dec 2017
Publication statusPublished - 15 Mar 2018


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.

Research Area(s)

  • Binary-level co-occurrence matrix, Irregularity, Recurrence plot, Sample entropy, Spatial dependence, Time series