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Entropy profiling: A reduced—parametric measure of kolmogorov—sinai entropy from short-term hrv signal

Karmakar, Chandan, Udhayakumar, Radhagayathri and Palaniswami, Marimuthu 2020, Entropy profiling: A reduced—parametric measure of kolmogorov—sinai entropy from short-term hrv signal, Entropy, vol. 22, no. 12, pp. 1-28, doi: 10.3390/e22121396.

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Title Entropy profiling: A reduced—parametric measure of kolmogorov—sinai entropy from short-term hrv signal
Author(s) Karmakar, ChandanORCID iD for Karmakar, Chandan orcid.org/0000-0003-1814-0856
Udhayakumar, Radhagayathri
Palaniswami, Marimuthu
Journal name Entropy
Volume number 22
Issue number 12
Article ID 1396
Start page 1
End page 28
Total pages 28
Publisher MDPI
Place of publication Basel, Switzerland
Publication date 2020
ISSN 1099-4300
1099-4300
Keyword(s) Science & Technology
Physical Sciences
Physics, Multidisciplinary
Physics
entropy profiling
heart rate variability
short-term HRV time series
irregularity analysis
complexity analysis
tolerance
non-parametric K-S entropy
HEART-RATE-VARIABILITY
PHYSIOLOGICAL TIME-SERIES
APPROXIMATE ENTROPY
SAMPLE ENTROPY
MULTISCALE ENTROPY
NONLINEAR DYNAMICS
COMPLEXITY
HEALTHY
IRREGULARITY
APEN
Summary Entropy profiling is a recently introduced approach that reduces parametric dependence in traditional Kolmogorov-Sinai (KS) entropy measurement algorithms. The choice of the threshold parameter r of vector distances in traditional entropy computations is crucial in deciding the accuracy of signal irregularity information retrieved by these methods. In addition to making parametric choices completely data-driven, entropy profiling generates a complete profile of entropy information as against a single entropy estimate (seen in traditional algorithms). The benefits of using “profiling” instead of “estimation” are: (a) precursory methods such as approximate and sample entropy that have had the limitation of handling short-term signals (less than 1000 samples) are now made capable of the same; (b) the entropy measure can capture complexity information from short and long-term signals without multi-scaling; and (c) this new approach facilitates enhanced information retrieval from short-term HRV signals. The novel concept of entropy profiling has greatly equipped traditional algorithms to overcome existing limitations and broaden applicability in the field of short-term signal analysis. In this work, we present a review of KS-entropy methods and their limitations in the context of short-term heart rate variability analysis and elucidate the benefits of using entropy profiling as an alternative for the same.
Language eng
DOI 10.3390/e22121396
Indigenous content off
Field of Research 01 Mathematical Sciences
02 Physical Sciences
HERDC Research category C1 Refereed article in a scholarly journal
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30146405

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.