IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v165y2022ip1s096007792200981x.html
   My bibliography  Save this article

Modified multiscale sample entropy and cross-sample entropy based on horizontal visibility graph

Author

Listed:
  • Lin, Guancen
  • Lin, Aijing

Abstract

As a crucial method of the feature extraction, the complexity measurement has a wide range of applications in the field of nonlinear time series research. This paper presents an innovative multiscale sample entropy for measuring the complexity of time series based on the horizontal visibility graph. The modified multiscale sample entropy has been proven to be robust on two artificial time series, and is capable of reducing the undefined entropy generated as a result of the increase in scale. We apply the modified multiscale sample entropy to the diagnosis of epilepsy. Using a novel data processing algorithm that combines frequency bands with decomposition, feature vectors are constructed for Electroencephalography (EEG) signals through the proposed entropy calculation algorithm, and different classes of subjects are categorized based on K-Nearest Neighbors, Support Vector Machine, and Artificial Neural Network. In the meantime, we propose an improved horizontal visibility graph-based multiscale cross-sample entropy method to measure the synchronization between two time series. It shows robustness in artificial data and decreases the appearance of undefined entropy to a certain extent. It is possible to extract the characteristics of sleep EEG signals and divide the subjects’ sleep stages using this method. Furthermore, this paper introduces the surrogate data test and the proposed methods have the ability to detect the nonlinearity and synchronization in simulations and in real-world experiments. Experimental results demonstrate that the two proposed frameworks are effective in monitoring human health and in assessing physical status through EEG signals.

Suggested Citation

  • Lin, Guancen & Lin, Aijing, 2022. "Modified multiscale sample entropy and cross-sample entropy based on horizontal visibility graph," Chaos, Solitons & Fractals, Elsevier, vol. 165(P1).
    • Handle: RePEc:eee:chsofr:v:165:y:2022:i:p1:s096007792200981x
    DOI: 10.1016/j.chaos.2022.112802
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S096007792200981X
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.chaos.2022.112802?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. Kang, Huan & Zhang, Xiaofeng & Zhang, Guangbin, 2021. "Phase permutation entropy: A complexity measure for nonlinear time series incorporating phase information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 568(C).
    2. Li, Sange & Shang, Pengjian, 2021. "Analysis of nonlinear time series using discrete generalized past entropy based on amplitude difference distribution of horizontal visibility graph," Chaos, Solitons & Fractals, Elsevier, vol. 144(C).
    3. Li, Jingmiao & Wang, Jun, 2020. "Forcasting of energy futures market and synchronization based on stochastic gated recurrent unit model," Energy, Elsevier, vol. 213(C).
    4. Yin, Yi & Shang, Pengjian & Feng, Guochen, 2016. "Modified multiscale cross-sample entropy for complex time series," Applied Mathematics and Computation, Elsevier, vol. 289(C), pages 98-110.
    5. Zhang, Ningning & Lin, Aijing & Ma, Hui & Shang, Pengjian & Yang, Pengbo, 2018. "Weighted multivariate composite multiscale sample entropy analysis for the complexity of nonlinear times series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 508(C), pages 595-607.
    6. Wang, Xin & Sun, Mei, 2021. "A novel prediction model of multi-layer symbolic pattern network: Based on causation entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 575(C).
    7. Podobnik, Boris & Horvatic, Davor & Lam Ng, Alfonso & Eugene Stanley, H. & Ivanov, Plamen Ch., 2008. "Modeling long-range cross-correlations in two-component ARFIMA and FIARCH processes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(15), pages 3954-3959.
    8. Gu, Danlei & Lin, Aijing & Lin, Guancen, 2022. "Sleep and cardiac signal processing using improved multivariate partial compensated transfer entropy based on non-uniform embedding," Chaos, Solitons & Fractals, Elsevier, vol. 159(C).
    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. Lei, Shuaihao & Cheng, Li & Yang, Weixing & Xu, Wentao & Yu, Lei & Luo, Can & Jiao, Weixuan & Shen, Jiantao, 2024. "Dynamic multiscale pressure fluctuation features extraction of mixed-flow pump as turbine (PAT) and flow state recognition of the outlet passage using variational mode decomposition and refined compos," Energy, Elsevier, vol. 305(C).
    2. Bai, Shiwei & Niu, Min & Wang, Yu, 2024. "The network structure of Sturmian sequences based on HVG," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 634(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. Lin, Guancen & Lin, Aijing & Mi, Yujia & Gu, Danlei, 2023. "Measurement of information transfer based on phase increment transfer entropy," Chaos, Solitons & Fractals, Elsevier, vol. 174(C).
    2. Yin, Yi & Shang, Pengjian & Ahn, Andrew C. & Peng, Chung-Kang, 2019. "Multiscale joint permutation entropy for complex time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 515(C), pages 388-402.
    3. He, Jiayi & Shang, Pengjian & Xiong, Hui, 2018. "Multidimensional scaling analysis of financial time series based on modified cross-sample entropy methods," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 500(C), pages 210-221.
    4. İşcanoğlu-Çekiç, Ayşegül & Gülteki̇n, Havva, 2019. "Are cross-correlations between Turkish Stock Exchange and three major country indices multifractal or monofractal?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 978-990.
    5. Wu, Yue & Shang, Pengjian & Chen, Shijian, 2019. "Modified multifractal large deviation spectrum based on CID for financial market system," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 523(C), pages 1331-1342.
    6. El Alaoui, Marwane & Benbachir, Saâd, 2013. "Multifractal detrended cross-correlation analysis in the MENA area," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(23), pages 5985-5993.
    7. Tan, Jinghua & Li, Zhixi & Zhang, Chuanhui & Shi, Long & Jiang, Yuansheng, 2024. "A multiscale time-series decomposition learning for crude oil price forecasting," Energy Economics, Elsevier, vol. 136(C).
    8. Xu, Meng & Shang, Pengjian, 2018. "Analysis of financial time series using multiscale entropy based on skewness and kurtosis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 1543-1550.
    9. Ladislav Kristoufek, 2018. "Power-law cross-correlations: Issues, solutions and future challenges," Papers 1806.01616, arXiv.org.
    10. Contreras-Reyes, Javier E. & Jeldes-Delgado, Fabiola & Carrasco, Raúl, 2024. "Jensen-Detrended Cross-Correlation function for non-stationary time series with application to Latin American stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 654(C).
    11. Li, Jianfeng & Lu, Xinsheng & Zhou, Ying, 2016. "Cross-correlations between crude oil and exchange markets for selected oil rich economies," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 453(C), pages 131-143.
    12. Wan, Li & Ling, Guang & Guan, Zhi-Hong & Fan, Qingju & Tong, Yu-Han, 2022. "Fractional multiscale phase permutation entropy for quantifying the complexity of nonlinear time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 600(C).
    13. Kang, Sang Hoon & Yoon, Seong-Min, 2008. "Long memory features in the high frequency data of the Korean stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(21), pages 5189-5196.
    14. Li, Shuping & Lu, Xinsheng & Li, Jianfeng, 2021. "Cross-correlations between the P2P interest rate, Shibor and treasury yields," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 574(C).
    15. Teng, Yue & Shang, Pengjian, 2018. "Detrended fluctuation analysis based on higher-order moments of financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 311-322.
    16. Wu, Binrong & Wang, Lin & Wang, Sirui & Zeng, Yu-Rong, 2021. "Forecasting the U.S. oil markets based on social media information during the COVID-19 pandemic," Energy, Elsevier, vol. 226(C).
    17. Bai, Shiwei & Niu, Min, 2022. "The visibility graph of n-bonacci sequence," Chaos, Solitons & Fractals, Elsevier, vol. 163(C).
    18. Al Rahahleh, Naseem & Bhatti, M. Ishaq, 2017. "Co-movement measure of information transmission on international equity markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 470(C), pages 119-131.
    19. Liu, Li-Zhi & Qian, Xi-Yuan & Lu, Heng-Yao, 2010. "Cross-sample entropy of foreign exchange time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(21), pages 4785-4792.
    20. Wang, Fang & Wang, Lin & Chen, Yuming, 2022. "Multi-affine visible height correlation analysis for revealing rich structures of fractal time series," Chaos, Solitons & Fractals, Elsevier, vol. 157(C).

    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:165:y:2022:i:p1:s096007792200981x. 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.