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Approximate entropy profile: a novel approach to comprehend irregularity of short-term HRV signal
journal contribution
posted on 2017-04-01, 00:00 authored by Radhagayathri Krishnavilas Udhayakumar, Chandan KarmakarChandan Karmakar, M PalaniswamiKolmogorov–Sinai entropy-based irregularity measures such as approximate entropy (ApEn), sample entropy and fuzzy entropy are widely used for short-term heart rate variability analysis. These entropy statistics are estimated for a specific value of the tolerance parameter (r) that is mostly chosen from a common recommended range. Entropy measurement on short-term signals is highly sensitive to the choice of r. An incorrect selection of r results in an inaccurate entropy value, thereby leading to unreliable information retrieval. By addressing this inaccuracy due to r selection, the quality and reliability of information retrieval can be improved. Thus, we hypothesize that generating a complete entropy profile using all potential r values will give a more complete and useful information about signal irregularity in contrast to the case of finding entropy at a single selected value of r. In order to do so, one must be able to accurately select all potential r candidates. In this paper, we use a data-driven algorithm based on cumulative histograms to automatically select potential r values for an individual signal based on its dynamics. An appropriate set of r values is designated by the algorithm for generating a series of ApEn values (ApEn profile) instead of a single value of ApEn. ApEn profile- based secondary measures such as TotalApEn and SDApEn have been used as features to classify sets of synthetic and physiologic data. Our study proves that secondary measures obtained from an ApEn profile are more efficient in indicating irregularity levels in comparison with the traditional measure of ApEn evaluated at a single r value, specially in the case of short length data.
History
Journal
Nonlinear dynamicsVolume
88Issue
2Pagination
823 - 837Publisher
Springer Science+Business MediaLocation
Dordrecht, The NetherlandsPublisher DOI
ISSN
0924-090XeISSN
1573-269XLanguage
engPublication classification
C Journal article; C1 Refereed article in a scholarly journalCopyright notice
2016, Springer Science+Business Media DordrechtUsage metrics
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