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Article

Entropy Profiling: A Reduced—Parametric Measure of Kolmogorov—Sinai Entropy from Short-Term HRV Signal

1
School of Information Technology, Deakin University, Geelong VIC 3216, Australia
2
Department of Electrical & Electronic Engineering, The University of Melbourne, Parkville VIC 3010, Australia
*
Author to whom correspondence should be addressed.
Entropy 2020, 22(12), 1396; https://doi.org/10.3390/e22121396
Received: 23 September 2020 / Revised: 8 December 2020 / Accepted: 8 December 2020 / Published: 10 December 2020
(This article belongs to the Special Issue Symbolic Entropy Analysis and Its Applications II)
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. View Full-Text
Keywords: entropy profiling; heart rate variability; short-term HRV time series; irregularity analysis; complexity analysis; tolerance; non-parametric K-S entropy entropy profiling; heart rate variability; short-term HRV time series; irregularity analysis; complexity analysis; tolerance; non-parametric K-S entropy
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MDPI and ACS Style

Karmakar, C.; Udhayakumar, R.; Palaniswami, M. Entropy Profiling: A Reduced—Parametric Measure of Kolmogorov—Sinai Entropy from Short-Term HRV Signal. Entropy 2020, 22, 1396. https://doi.org/10.3390/e22121396

AMA Style

Karmakar C, Udhayakumar R, Palaniswami M. Entropy Profiling: A Reduced—Parametric Measure of Kolmogorov—Sinai Entropy from Short-Term HRV Signal. Entropy. 2020; 22(12):1396. https://doi.org/10.3390/e22121396

Chicago/Turabian Style

Karmakar, Chandan, Radhagayathri Udhayakumar, and Marimuthu Palaniswami. 2020. "Entropy Profiling: A Reduced—Parametric Measure of Kolmogorov—Sinai Entropy from Short-Term HRV Signal" Entropy 22, no. 12: 1396. https://doi.org/10.3390/e22121396

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