CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Optimal RR-Interval Data Length for Entropy based Heart Rate Variability Analysis

by Manjit Singh, Butta Singh, Gurpreet Singh
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 123 - Number 14
Year of Publication: 2015
Authors: Manjit Singh, Butta Singh, Gurpreet Singh
10.5120/ijca2015905667

Manjit Singh, Butta Singh, Gurpreet Singh . Optimal RR-Interval Data Length for Entropy based Heart Rate Variability Analysis. International Journal of Computer Applications. 123, 14 ( August 2015), 39-42. DOI=10.5120/ijca2015905667

@article{ 10.5120/ijca2015905667,
author = { Manjit Singh, Butta Singh, Gurpreet Singh },
title = { Optimal RR-Interval Data Length for Entropy based Heart Rate Variability Analysis },
journal = { International Journal of Computer Applications },
issue_date = { August 2015 },
volume = { 123 },
number = { 14 },
month = { August },
year = { 2015 },
issn = { 0975-8887 },
pages = { 39-42 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume123/number14/22029-2015905667/ },
doi = { 10.5120/ijca2015905667 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:13:11.527377+05:30
%A Manjit Singh
%A Butta Singh
%A Gurpreet Singh
%T Optimal RR-Interval Data Length for Entropy based Heart Rate Variability Analysis
%J International Journal of Computer Applications
%@ 0975-8887
%V 123
%N 14
%P 39-42
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Heart Rate Variability (HRV) is defined as the variations between consecutive instantaneous heart rates that occur in the heart as a consequence of a complex internal dynamic balance. Nonlinear analysis of HRV is helpful to assess the cardiac health noninvasively. Approximate Entropy and Sample Entropy are mathematical algorithms to measure the predictability or repeatability with in a time series. This paper compares the approximate entropy and sample entropy on different data lengths, which are 20 minutes, 10 minutes, 5 minutes, 3 minutes and 2 minutes respectively. In addition it has been observed that the measuring time of sample entropy can be reducing beyond 5 minutes.

References
  1. Afonso, V. X. 1993. ECG QRS detection. Biomedical Digital Signal Processing, chapter 12, 237-264. Prentice Hall, New Jersey.
  2. Baselli, G., Cerutti, S., Civardi, S., Lombardi, F., Malliani, A., Merri, M., Pagani, M., and Rizzz, G. 1987. Heart rate variability signal processing: a quantitative approach as an aid to diagnosis in cardiovascular pathologies. International Journal of Bio-Medical Computing, vol. 20, 51-70.
  3. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. 1996. Heart rate variability. Standards of measurement, physiological interpretation, and clinical use. Circulation 93:1043–65.
  4. Pichon, A., Roulaud, M., Antoine-Jonville, S., de Bisschop, C., Denjean. 2006. A. Spectral analysis of heart rate variability: Interchangeability between autoregressive analysis and fast Fourier transform. Electrocardio 39:31-7.
  5. Pincus, S. M. 1991. Approximate entropy as a measure of system complexity. In Proc. Nat. Acad. Sci. U.S.A., vol. 88, 2297–2301.
  6. Haitham, M., Al-Angari* and Sahakian, A.V. 2007. Use of Sample Entropy Approach to Study Heart Rate Variability in Obstructive Sleep Apnea Syndrome. IEEE Transactions on Biomedical Engineering, vol. 54, 10.
  7. Brennan, M., Palaniswami, M., and Kamen, P. 2001. Do Existing Measures ofPoincaré Plot Geometry Reflect Nonlinear Features of Heart Rate Variability. IEEE Transactions on Biomedical Engineering, vol. 48, no. 11.
  8. Lin, G. H., Chang, Y. H., and Lin, K. P. Comparison of Heart Rate Variability Measured by ECG in Different Signal Lengths,” Journal of Medical and Biological Engineering, 25(2): 67-71
  9. http://www.physionet.org/physiobank/database/mitdb/
  10. available at, MIT-BIH Arrhythmia Database.
  11. http://www.physionet.org/physiobank/annotations.shtml
  12. available at, PhysioBank Annotations.
  13. Pincus, S. M., and Huang, W. M. 1992. Approximate entropy: Statistical properties and applications. Commun. Statist. Theory Meth, vol. 21, 3061–3077.
  14. Richman, J. S. and Moorman J. R. 2000. Physiological time-series analysis using approximate entropy and sample entropy. Am. J. Physiol., vol. 278, H2039–H2049.
  15. Tarvainen, M. P., Nishkanen, J. P., Lipponen, J. A., Ranta-aho, P. O., and Karjalainen, P. A. 2014. Kubious HRV- Heart rate variability analysis software. Comp Methods and Prog in Biomed. 113:210-220.
Index Terms

Computer Science
Information Sciences

Keywords

Heart Rate Variability ECG Approximate Entropy Sample Entropy Data Lengths.

Powered by PhDFocusTM