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Stability, consistency and performance of distribution entropy in analysing short length heart rate variability (HRV) signal

Karmakar, Chandan, Udhayakumar, Radhagayathri K., Li, Peng, Venkatesh, Svetha and Palaniswami, Marimuthu 2017, Stability, consistency and performance of distribution entropy in analysing short length heart rate variability (HRV) signal, Frontiers in Physiology, vol. 8, pp. 720-734, doi: 10.3389/fphys.2017.00720.

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Title Stability, consistency and performance of distribution entropy in analysing short length heart rate variability (HRV) signal
Author(s) Karmakar, ChandanORCID iD for Karmakar, Chandan orcid.org/0000-0003-1814-0856
Udhayakumar, Radhagayathri K.
Li, Peng
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
Palaniswami, Marimuthu
Journal name Frontiers in Physiology
Volume number 8
Start page 720
End page 734
Total pages 14
Publisher Frontiers Research Foundation
Place of publication Lausanne, Switzerland
Publication date 2017-09-20
ISSN 1664-042X
Keyword(s) aging
approximate entropy
arrhythmia
distribution entropy
heart rate variability
sample entropy
short-term analysis
Science & Technology
Life Sciences & Biomedicine
Physiology
PHYSIOLOGICAL TIME-SERIES
AUTONOMIC DYSFUNCTION
COMPLEXITY
DISTEN
Summary Distribution entropy (DistEn) is a recently developed measure of complexity that is used to analyse heart rate variability (HRV) data. Its calculation requires two input parameters-the embedding dimension m, and the number of bins M which replaces the tolerance parameter r that is used by the existing approximation entropy (ApEn) and sample entropy (SampEn) measures. The performance of DistEn can also be affected by the data length N. In our previous studies, we have analyzed stability and performance of DistEn with respect to one parameter (m or M) or combination of two parameters (N and M). However, impact of varying all the three input parameters on DistEn is not yet studied. Since DistEn is predominantly aimed at analysing short length heart rate variability (HRV) signal, it is important to comprehensively study the stability, consistency and performance of the measure using multiple case studies. In this study, we examined the impact of changing input parameters on DistEn for synthetic and physiological signals. We also compared the variations of DistEn and performance in distinguishing physiological (Elderly from Young) and pathological (Healthy from Arrhythmia) conditions with ApEn and SampEn. The results showed that DistEn values are minimally affected by the variations of input parameters compared to ApEn and SampEn. DistEn also showed the most consistent and the best performance in differentiating physiological and pathological conditions with various of input parameters among reported complexity measures. In conclusion, DistEn is found to be the best measure for analysing short length HRV time series.
Language eng
DOI 10.3389/fphys.2017.00720
Field of Research 080109 Pattern Recognition and Data Mining
090609 Signal Processing
Socio Economic Objective 920203 Diagnostic Methods
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2017, The Authors
Free to Read? Yes
Use Rights Creative Commons Attribution licence
Persistent URL http://hdl.handle.net/10536/DRO/DU:30104226

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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.