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Effect of data length and bin numbers on distribution entropy (DistEn) measurement in analyzing healthy aging
conference contribution
posted on 2015-01-01, 00:00 authored by Radhagayathri Krishnavilas Udhayakumar, Chandan KarmakarChandan Karmakar, P Li, M PalaniswamiComplexity analysis of a given time series is executed using various measures of irregularity, the most commonly used being Approximate entropy (ApEn), Sample entropy (SampEn) and Fuzzy entropy (FuzzyEn). However, the dependence of these measures on the critical parameter of tolerance `r' leads to precarious results, owing to random selections of r. Attempts to eliminate the use of r in entropy calculations introduced a new measure of entropy namely distribution entropy (DistEn) based on the empirical probability distribution function (ePDF). DistEn completely avoids the use of a variance dependent parameter like r and replaces it by a parameter M, which corresponds to the number of bins used in the histogram to calculate it. When tested for synthetic data, M has been observed to produce a minimal effect on DistEn as compared to the effect of r on other entropy measures. Also, DistEn is said to be relatively stable with data length (N) variations, as far as synthetic data is concerned. However, these claims have not been analyzed for physiological data. Our study evaluates the effect of data length N and bin number M on the performance of DistEn using both synthetic and physiologic time series data. Synthetic logistic data of `Periodic' and `Chaotic' levels of complexity and 40 RR interval time series belonging to two groups of healthy aging population (young and elderly) have been used for the analysis. The stability and consistency of DistEn as a complexity measure as well as a classifier have been studied. Experiments prove that the parameters N and M are more influential in deciding the efficacy of DistEn performance in the case of physiologic data than synthetic data. Therefore, a generalized random selection of M for a given data length N may not always be an appropriate combination to yield good performance of DistEn for physiologic data.
History
Event
IEEE Engineering in Medicine and Biology Society. Conference (37th : 2015 : Milan, Italy)Series
IEEE Engineering in Medicine and Biology SocietyPagination
7877 - 7880Publisher
IEEELocation
Milan, ItalyPlace of publication
Piscataway, N.J.Publisher DOI
Start date
2015-08-25End date
2016-08-29ISSN
1094-687XISBN-13
9781424492695Language
engPublication classification
E Conference publication; E1 Full written paper - refereedCopyright notice
2015, IEEEEditor/Contributor(s)
[Unknown]Title of proceedings
EMBC 2015: Proceedings of the 37th IEEE Engineering in Medicine and Biology Society 2015 conferenceUsage metrics
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