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Spatial-dependence recurrence sample entropy
Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-4255-5130
2017 (English)In: Physica A: Statistical Mechanics and its Applications, ISSN 0378-4371Article in journal (Refereed) Accepted
Abstract [en]

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.

Place, publisher, year, edition, pages
Elsevier, 2017.
Keyword [en]
Time series; Irregularity; Sample entropy; Recurrence plot; Binary-level co-occurrence matrix; Spatial dependence
National Category
Other Medical Engineering Other Physics Topics
Identifiers
URN: urn:nbn:se:liu:diva-143423DOI: 10.1016/j.physa.2017.12.015OAI: oai:DiVA.org:liu-143423DiVA: diva2:1163229
Available from: 2017-12-06 Created: 2017-12-06 Last updated: 2017-12-11

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Pham, Tuan
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Citation style
  • apa
  • harvard1
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  • Other style
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  • de-DE
  • en-GB
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  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf