IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v470y2017icp60-67.html
   My bibliography  Save this article

Exploring stability of entropy analysis for signal with different trends

Author

Listed:
  • Zhang, Yin
  • Li, Jin
  • Wang, Jun

Abstract

Considering the effects of environment disturbances and instrument systems, the actual detecting signals always are carrying different trends, which result in that it is difficult to accurately catch signals complexity. So choosing steady and effective analysis methods is very important. In this paper, we applied entropy measures—the base-scale entropy and approximate entropy to analyze signal complexity, and studied the effect of trends on the ideal signal and the heart rate variability (HRV) signals, that is, linear, periodic, and power-law trends which are likely to occur in actual signals. The results show that approximate entropy is unsteady when we embed different trends into the signals, so it is not suitable to analyze signal with trends. However, the base-scale entropy has preferable stability and accuracy for signal with different trends. So the base-scale entropy is an effective method to analyze the actual signals.

Suggested Citation

  • Zhang, Yin & Li, Jin & Wang, Jun, 2017. "Exploring stability of entropy analysis for signal with different trends," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 470(C), pages 60-67.
    • Handle: RePEc:eee:phsmap:v:470:y:2017:i:c:p:60-67
    DOI: 10.1016/j.physa.2016.11.073
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437116308792
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2016.11.073?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Li, Jin & Hu, Jing & Zhang, Yinhong & Zhang, Xiaofeng, 2011. "Dynamical complexity changes during two forms of meditation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(12), pages 2381-2387.
    2. Li, Yu & Wang, Jun & Li, Jin & Liu, Dazhao, 2015. "Effect of extreme data loss on heart rate signals quantified by entropy analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 651-658.
    3. Xu, Yinlin & Ma, Qianli D.Y. & Schmitt, Daniel T. & Bernaola-Galván, Pedro & Ivanov, Plamen Ch., 2011. "Effects of coarse-graining on the scaling behavior of long-range correlated and anti-correlated signals," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(23), pages 4057-4072.
    4. Plamen Ch. Ivanov & Luís A. Nunes Amaral & Ary L. Goldberger & Shlomo Havlin & Michael G. Rosenblum & Zbigniew R. Struzik & H. Eugene Stanley, 1999. "Multifractality in human heartbeat dynamics," Nature, Nature, vol. 399(6735), pages 461-465, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Li, Jin & Chen, Chen & Yao, Qin & Zhang, Peng & Wang, Jun & Hu, Jing & Feng, Feilong, 2018. "The effect of circadian rhythm on the correlation and multifractality of heart rate signals during exercise," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 1207-1213.
    2. Vitanov, Nikolay K. & Sakai, Kenshi & Dimitrova, Zlatinka I., 2008. "SSA, PCA, TDPSC, ACFA: Useful combination of methods for analysis of short and nonstationary time series," Chaos, Solitons & Fractals, Elsevier, vol. 37(1), pages 187-202.
    3. Rodriguez, Eduardo & Echeverria, Juan C. & Alvarez-Ramirez, Jose, 2009. "Fractality in electrocardiographic waveforms for healthy subjects and patients with ventricular fibrillation," Chaos, Solitons & Fractals, Elsevier, vol. 39(3), pages 1046-1054.
    4. Rodriguez, Eduardo & Echeverria, Juan C. & Alvarez-Ramirez, Jose, 2007. "Detrended fluctuation analysis of heart intrabeat dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 384(2), pages 429-438.
    5. Nagarajan, Radhakrishnan & Kavasseri, Rajesh G., 2005. "Minimizing the effect of trends on detrended fluctuation analysis of long-range correlated noise," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 354(C), pages 182-198.
    6. Mirzayof, Dror & Ashkenazy, Yosef, 2010. "Preservation of long range temporal correlations under extreme random dilution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(24), pages 5573-5580.
    7. Makowiec, Danuta & Dudkowska, Aleksandra & Gała̧ska, Rafał & Rynkiewicz, Andrzej, 2009. "Multifractal estimates of monofractality in RR-heart series in power spectrum ranges," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(17), pages 3486-3502.
    8. Kaufman, Miron & Zurcher, Ulrich & Sung, Paul S., 2007. "Entropy of electromyography time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 386(2), pages 698-707.
    9. Wang, Jian & Jiang, Wenjing & Wu, Xinpei & Yang, Mengdie & Shao, Wei, 2023. "Role of vaccine in fighting the variants of COVID-19," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    10. Ana Gavrovska & Goran Zajić & Vesna Bogdanović & Irini Reljin & Branimir Reljin, 2017. "Identification of S1 and S2 Heart Sound Patterns Based on Fractal Theory and Shape Context," Complexity, Hindawi, vol. 2017, pages 1-9, November.
    11. Goshvarpour, Atefeh & Goshvarpour, Ateke, 2019. "Matching pursuit based indices for examining physiological differences of meditators and non-meditators: An HRV study," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 524(C), pages 147-156.
    12. Ausloos, Marcel & Nedic, Olgica & Dekanski, Aleksandar, 2016. "Day of the week effect in paper submission/acceptance/rejection to/in/by peer review journals," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 456(C), pages 197-203.
    13. Stanley, H.E. & Amaral, L.A.N. & Goldberger, A.L. & Havlin, S. & Ivanov, P.Ch. & Peng, C.-K., 1999. "Statistical physics and physiology: Monofractal and multifractal approaches," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 270(1), pages 309-324.
    14. Mukli, Peter & Nagy, Zoltan & Eke, Andras, 2015. "Multifractal formalism by enforcing the universal behavior of scaling functions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 417(C), pages 150-167.
    15. Kavasseri, Rajesh G. & Nagarajan, Radhakrishnan, 2005. "A multifractal description of wind speed records," Chaos, Solitons & Fractals, Elsevier, vol. 24(1), pages 165-173.
    16. Núñez-Acosta, Elisa & Lerma, Claudia & Márquez, Manlio F. & José, Marco V., 2012. "Mutual information analysis reveals bigeminy patterns in Andersen–Tawil syndrome and in subjects with a history of sudden cardiac death," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(3), pages 693-707.
    17. França, Lucas Gabriel Souza & Montoya, Pedro & Miranda, José Garcia Vivas, 2019. "On multifractals: A non-linear study of actigraphy data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 612-619.
    18. Vitanov, Nikolay K. & Hoffmann, Norbert P. & Wernitz, Boris, 2014. "Nonlinear time series analysis of vibration data from a friction brake: SSA, PCA, and MFDFA," Chaos, Solitons & Fractals, Elsevier, vol. 69(C), pages 90-99.
    19. Li, Yu & Wang, Jun & Li, Jin & Liu, Dazhao, 2015. "Effect of extreme data loss on heart rate signals quantified by entropy analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 419(C), pages 651-658.
    20. Xiong, Gang & Yu, Wenxian & Xia, Wenxiang & Zhang, Shuning, 2016. "Multifractal signal reconstruction based on singularity power spectrum," Chaos, Solitons & Fractals, Elsevier, vol. 91(C), pages 25-32.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:470:y:2017:i:c:p:60-67. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.