IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v168y2023ics0960077923000589.html
   My bibliography  Save this article

Deviation distance entropy: A method for quantifying the dynamic features of biomedical time series

Author

Listed:
  • Yu, Xiao
  • Li, Weimin
  • Yang, Bing
  • Li, Xiaorong
  • Chen, Jie
  • Fu, Guohua

Abstract

Physiological system time series (signals) usually follow a pattern of fluctuations over time. Mining the potential dynamic features of physiological system time series is the key to understanding changes in the state and behavior of physiological systems. In this paper, we propose a new method to measure the complexity of the dynamic features of physiological system time series, namely deviation distance entropy (DE). It achieves the modeling of dynamic features by considering the relationship between current and future segments of the time series and further quantifies their complexity. Through simulation and analysis, we show that DE enables accurate extraction of key features of the signal. Applying the DE method to real electrocardiogram (ECG) signals, we find that DE has a better ability to distinguish between signals from healthy individuals and atrial fibrillation (AF) patients than other methods for measuring sequence irregularities, such as approximate entropy, sample entropy and fuzzy entropy. Further, we propose the idea of “clarity” for the curve of dynamic features. Using “clarity”, we can graphically grade patients with AF according to their ECG signals. According to our numerical analysis, deviation distances for patients with AF follow two different power laws. The magnitude of the difference between these two power laws is positively correlated with the severity of AF onset in the corresponding patients. An in-depth analysis of this phenomenon reveals that it is essentially the development of chaos in the corresponding system, while fluctuations in the corresponding trajectory periods of the mapped attractors can also be observed, which may explain how AF starts and develops. Our study provides a novel perspective for characterizing the time series dynamics of physiological systems.

Suggested Citation

  • Yu, Xiao & Li, Weimin & Yang, Bing & Li, Xiaorong & Chen, Jie & Fu, Guohua, 2023. "Deviation distance entropy: A method for quantifying the dynamic features of biomedical time series," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
  • Handle: RePEc:eee:chsofr:v:168:y:2023:i:c:s0960077923000589
    DOI: 10.1016/j.chaos.2023.113157
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077923000589
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.chaos.2023.113157?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. Strozzi, Fernanda & Zaldı́var, José-Manuel & Zbilut, Joseph P, 2002. "Application of nonlinear time series analysis techniques to high-frequency currency exchange data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 312(3), pages 520-538.
    2. Timothy V. Pyrkov & Konstantin Avchaciov & Andrei E. Tarkhov & Leonid I. Menshikov & Andrei V. Gudkov & Peter O. Fedichev, 2021. "Longitudinal analysis of blood markers reveals progressive loss of resilience and predicts human lifespan limit," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
    3. Marten Scheffer & Jordi Bascompte & William A. Brock & Victor Brovkin & Stephen R. Carpenter & Vasilis Dakos & Hermann Held & Egbert H. van Nes & Max Rietkerk & George Sugihara, 2009. "Early-warning signals for critical transitions," Nature, Nature, vol. 461(7260), pages 53-59, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Liu, Xiao & Hu, Qunpeng & Li, Jinsong & Li, Weimin & Liu, Tong & Xin, Mingjun & Jin, Qun, 2024. "Decoupling representation contrastive learning for carbon emission prediction and analysis based on time series," Applied Energy, Elsevier, vol. 367(C).

    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. Richter, Andries & Dakos, Vasilis, 2015. "Profit fluctuations signal eroding resilience of natural resources," Ecological Economics, Elsevier, vol. 117(C), pages 12-21.
    2. Karimi Rahjerdi, Bahareh & Ramamoorthy, Ramesh & Nazarimehr, Fahimeh & Rajagopal, Karthikeyan & Jafari, Sajad, 2022. "Indicating the synchronization bifurcation points using the early warning signals in two case studies: Continuous and explosive synchronization," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
    3. John M Drake & Tobias S Brett & Shiyang Chen & Bogdan I Epureanu & Matthew J Ferrari & Éric Marty & Paige B Miller & Eamon B O’Dea & Suzanne M O’Regan & Andrew W Park & Pejman Rohani, 2019. "The statistics of epidemic transitions," PLOS Computational Biology, Public Library of Science, vol. 15(5), pages 1-14, May.
    4. Gianluca Fabiani & Nikolaos Evangelou & Tianqi Cui & Juan M. Bello-Rivas & Cristina P. Martin-Linares & Constantinos Siettos & Ioannis G. Kevrekidis, 2024. "Task-oriented machine learning surrogates for tipping points of agent-based models," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    5. James J Elser & Timothy J Elser & Stephen R Carpenter & William A Brock, 2014. "Regime Shift in Fertilizer Commodities Indicates More Turbulence Ahead for Food Security," PLOS ONE, Public Library of Science, vol. 9(5), pages 1-7, May.
    6. Roland Clift & Sarah Sim & Henry King & Jonathan L. Chenoweth & Ian Christie & Julie Clavreul & Carina Mueller & Leo Posthuma & Anne-Marie Boulay & Rebecca Chaplin-Kramer & Julia Chatterton & Fabrice , 2017. "The Challenges of Applying Planetary Boundaries as a Basis for Strategic Decision-Making in Companies with Global Supply Chains," Sustainability, MDPI, vol. 9(2), pages 1-23, February.
    7. Darrell Jiajie Tay & Chung-I Chou & Sai-Ping Li & Shang You Tee & Siew Ann Cheong, 2016. "Bubbles Are Departures from Equilibrium Housing Markets: Evidence from Singapore and Taiwan," PLOS ONE, Public Library of Science, vol. 11(11), pages 1-13, November.
    8. Fushing, Hsieh & Jordà, Òscar & Beisner, Brianne & McCowan, Brenda, 2014. "Computing systemic risk using multiple behavioral and keystone networks: The emergence of a crisis in primate societies and banks," International Journal of Forecasting, Elsevier, vol. 30(3), pages 797-806.
    9. Dur, Gaël & Won, Eun-Ji & Han, Jeonghoon & Lee, Jae-Seong & Souissi, Sami, 2021. "An individual-based model for evaluating post-exposure effects of UV-B radiation on zooplankton reproduction," Ecological Modelling, Elsevier, vol. 441(C).
    10. Martin Lindegren & Vasilis Dakos & Joachim P Gröger & Anna Gårdmark & Georgs Kornilovs & Saskia A Otto & Christian Möllmann, 2012. "Early Detection of Ecosystem Regime Shifts: A Multiple Method Evaluation for Management Application," PLOS ONE, Public Library of Science, vol. 7(7), pages 1-9, July.
    11. Simon DeDeo, 2016. "Conflict and Computation on Wikipedia: A Finite-State Machine Analysis of Editor Interactions," Future Internet, MDPI, vol. 8(3), pages 1-23, July.
    12. Quentin Remy & Julius Hohlfeld & Maxime Vergès & Yann Le Guen & Jon Gorchon & Grégory Malinowski & Stéphane Mangin & Michel Hehn, 2023. "Accelerating ultrafast magnetization reversal by non-local spin transfer," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    13. Hu, Jiang-Hong & Xue, Ya-Kui & Sun, Gui-Quan & Jin, Zhen & Zhang, Juan, 2016. "Global dynamics of a predator–prey system modeling by metaphysiological approach," Applied Mathematics and Computation, Elsevier, vol. 283(C), pages 369-384.
    14. Jinxiao Duan & Guanwen Zeng & Nimrod Serok & Daqing Li & Efrat Blumenfeld Lieberthal & Hai-Jun Huang & Shlomo Havlin, 2023. "Spatiotemporal dynamics of traffic bottlenecks yields an early signal of heavy congestions," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    15. Zaldívar, José-Manuel & Strozzi, Fernanda & Dueri, Sibylle & Marinov, Dimitar & Zbilut, Joseph P., 2008. "Characterization of regime shifts in environmental time series with recurrence quantification analysis," Ecological Modelling, Elsevier, vol. 210(1), pages 58-70.
    16. Vasilis Dakos & Stephen R Carpenter & William A Brock & Aaron M Ellison & Vishwesha Guttal & Anthony R Ives & Sonia Kéfi & Valerie Livina & David A Seekell & Egbert H van Nes & Marten Scheffer, 2012. "Methods for Detecting Early Warnings of Critical Transitions in Time Series Illustrated Using Simulated Ecological Data," PLOS ONE, Public Library of Science, vol. 7(7), pages 1-20, July.
    17. Wang, Gang-Jin & Xie, Chi, 2013. "Cross-correlations between Renminbi and four major currencies in the Renminbi currency basket," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(6), pages 1418-1428.
    18. Marisa Faggini, 2011. "Chaotic Time Series Analysis in Economics: Balance and Perspectives," Working papers 25, Former Department of Economics and Public Finance "G. Prato", University of Torino.
    19. Domenico Di Gangi & Fabrizio Lillo & Davide Pirino, 2015. "Assessing systemic risk due to fire sales spillover through maximum entropy network reconstruction," Papers 1509.00607, arXiv.org, revised Jul 2018.
    20. Nils Bertschinger & Oliver Pfante, 2020. "Early Warning Signs of Financial Market Turmoils," JRFM, MDPI, vol. 13(12), pages 1-24, November.

    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:chsofr:v:168:y:2023:i:c:s0960077923000589. 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: Thayer, Thomas R. (email available below). General contact details of provider: https://www.journals.elsevier.com/chaos-solitons-and-fractals .

    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.