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. Attached Files Name Description MIMEType Size Downloads Title Entropy profiling: A reduced—parametric measure of kolmogorov—sinai entropy from short-term hrv signal Author(s) 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 Document type: Journal Article Collections: Faculty of Science, Engineering and Built Environment School of Information Technology Open Access Collection Related Links Link Description Link to full-text (open access)     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. 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. Versions Version Filter Type Tue, 05 Jan 2021, 13:01:21 EST Wed, 06 Jan 2021, 04:01:33 EST Wed, 06 Jan 2021, 05:06:17 EST Sat, 16 Jan 2021, 01:30:48 EST Mon, 22 Feb 2021, 11:46:15 EST Mon, 22 Feb 2021, 11:47:57 EST Mon, 22 Feb 2021, 11:53:17 EST Filtered Full Citation counts:   Cited 0 times in TR Web of Science Cited 0 times in Scopus Search Google Scholar Access Statistics: 11 Abstract Views, 0 File Downloads  -  Detailed Statistics Created: Tue, 05 Jan 2021, 13:01:21 EST

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