Abstract
Heart rate variability (HRV) is the variation of the time interval between consecutive heartbeats and depends on the extrinsic regulation of the heart rate. It can be quantified using nonlinear methods such as entropy measures, which determine the irregularity of the time intervals.
In this work, approximate entropy (ApEn), sample entropy (SampEn), fuzzy entropy (FuzzyEn) and fuzzy measure entropy (FuzzyMEn) were used to assess the effects of three different cardiac arrhythmia suppressing drugs on the HRV after a myocardial infarction.
The results show that the ability of all four entropy measures to distinguish between pre- and post-treatment HRV data is highly significant (p < 0.01). Furthermore, approximate entropy and sample entropy are able to differentiate significantly (p < 0.05) between the tested arrhythmia suppressing agents.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Rajendra Acharya, U., Paul Joseph, K., Kannathal, N., Lim, C., Suri, J.: Heart rate variability: a review. Med. Biol. Eng. Comput. 44, 1031–1051 (2006)
American Heart Association Inc.: European Society of Cardiology: Guidelines – heart rate variability. Eur. Heart J. 17, 354–381 (1996)
Smith, A.L., Reynolds, K.J., Owen, H.: Correlated Poincaré indices for measuring heart rate variability. Australasian physical & engineering sciences in medicine / supported by the Australasian College of Physical Scientists in Medicine and the Australasian Association of Physical Sciences in Medicine 30, 336–341 (2007)
Holzinger, A., Hörtenhuber, M., Mayer, C., Bachler, M., Wassertheurer, S., Pinho, A.J., Koslicki, D.: On entropy-based data mining. In: Holzinger, A., Jurisica, I. (eds.) Knowledge Discovery and Data Mining. LNCS, vol. 8401, pp. 209–226. Springer, Heidelberg (2014)
Stein, P.K., Kleiger, R.E., Domitrovich, P.P., Schechtman, K.B., Rottman, J.N.: Clinical and demographic determinants of heart rate variability in patients post myocardial infarction: insights from the cardiac arrhythmia suppression trial (cast). Clinical Cardiology 23, 187–194 (2000)
Goldberger, A.L., Amaral, L.A., Glass, L., Hausdorff, J.M., Ivanov, P.C., Mark, R.G., Mietus, J.E., Moody, G.B., Peng, C.K., Stanley, H.E.: PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals. Circulation 101, E215–E220 (2000)
Epstein, A.E., Bigger, J.T., Wyse, D.G., Romhilt, D.W., Reynolds-Haertle, R.A., Hallstrom, A.P.: Events in the cardiac arrhythmia suppression trial (cast): mortality in the entire population enrolled. Journal of the American College of Cardiology 18, 14–19 (1991)
Pincus, S.M.: Approximate entropy as a measure of system complexity. Proc. Natl. Acad. Sci. U.S.A. 88, 2297–2301 (1991)
Richman, J.S., Moorman, J.R.: Physiological time-series analysis using approximate entropy and sample entropy. Am. J. Physiol. Heart Circ. Physiol. 278, H2039–H2049 (2000)
Chen, W., Zhuang, J., Yu, W., Wang, Z.: Measuring complexity using FuzzyEn, ApEn, and SampEn. Med. Eng. Phys. 31, 61–68 (2009)
Liu, C., Li, K., Zhao, L., Liu, F., Zheng, D., Liu, C., Liu, S.: Analysis of heart rate variability using fuzzy measure entropy. Comput. Biol. Med. 43, 100–108 (2013)
Pincus, S.M.: Approximate entropy as a measure of system complexity. Proceedings of the National Academy of Sciences 88, 2297–2301 (1991)
Pincus, S.: Approximate entropy (apen) as a complexity measure. Chaos: An Interdisciplinary Journal of Nonlinear Science 5, 110–117 (1995)
Richman, J.S., Moorman, J.R.: Physiological time-series analysis using approximate entropy and sample entropy. Am. J. Physiol. Heart Circ. Physiol. 278, H2039–H2049 (2000)
Chen, W., Wang, Z., Xie, H., Yu, W.: Characterization of surface emg signal based on fuzzy entropy. IEEE Transactions on Neural Systems and Rehabilitation Engineering 15, 266–272 (2007)
Liu, C., Li, K., Zhao, L., Liu, F., Zheng, D., Liu, C., Liu, S.: Analysis of heart rate variability using fuzzy measure entropy. Comput. Biol. Med. 43, 100–108 (2013)
Mayer, C., Bachler, M., Hörtenhuber, M., Stocker, C., Holzinger, A., Wassertheurer, S.: Selection of entropy-measure parameters for knowledge discovery in heart rate variability data (in press)
Sethuraman, G., Ryan, K.L., Rickards, C.A., Convertino, V.A.: Ectopy in trauma patients: cautions for use of heart period variability in medical monitoring. Aviation, Space, and Environmental Medicine 81, 125–129 (2010)
Mateo, J., Laguna, P.: Analysis of heart rate variability in the presence of ectopic beats using the heart timing signal. IEEE Transactions on Biomedical Engineering 50, 334–343 (2003)
Beckers, F., Verheyden, B., Aubert, A.E.: Aging and nonlinear heart rate control in a healthy population. American Journal of Physiology-Heart and Circulatory Physiology 290, H2560–H2570 (2006)
Mäkikallio, T.H., Seppännen, T., Niemelä, M., Airaksinen, K.J., Tulppo, M., Huikuri, H.V.: Abnormalities in beat to beat complexity of heart rate dynamics in patients with a previous myocardial infarction1. Journal of the American College of Cardiology 28, 1005–1011 (1996)
Teo, K., Yusuf, S., Furberg, C.: Effects of prophylactic antiarrhythmic drug therapy in acute myocardial infarction: An overview of results from randomized controlled trials. JAMA 270, 1589–1595 (1993)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Bachler, M., Hörtenhuber, M., Mayer, C., Holzinger, A., Wassertheurer, S. (2014). Entropy-Based Data Mining on the Example of Cardiac Arrhythmia Suppression. In: Ślȩzak, D., Tan, AH., Peters, J.F., Schwabe, L. (eds) Brain Informatics and Health. BIH 2014. Lecture Notes in Computer Science(), vol 8609. Springer, Cham. https://doi.org/10.1007/978-3-319-09891-3_52
Download citation
DOI: https://doi.org/10.1007/978-3-319-09891-3_52
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-09890-6
Online ISBN: 978-3-319-09891-3
eBook Packages: Computer ScienceComputer Science (R0)