IDEAS home Printed from https://ideas.repec.org/a/eee/chsofr/v164y2022ics0960077922008529.html
   My bibliography  Save this article

A novel blood glucose time series prediction framework based on a novel signal decomposition method

Author

Listed:
  • Zhu, Ting
  • Wang, Wenbo
  • Yu, Min

Abstract

Unstable blood glucose levels will bring about a series of complications, and even threaten our life in severe cases. Therefore, accurately predicting and controlling blood glucose has become an indispensable part of diabetes treatment. For the sake of boosting the accuracy of blood glucose prediction, a novel time-frequency signal analytical technology called improved ensemble empirical mode decomposition based on fractal dimension (FEEMD) is first proposed to decompose the nonlinear and non-stationary raw blood glucose series to obtain intrinsic mode functions (IMFs) and residual components (Rs) in different frequency bands. Then, in order to overcome the problem that the kernel extreme learning machine (KELM) is easy to fall into the local optimum, the sparrow search algorithm (SSA) is introduced to further enhance the robustness and stability of KELM model. Ultimately, the predictions of each optimized KELM are fused to achieve the final prognostic results. Through the experimental results of three patients, the following contributions can be summarized: (a) FEEMD exploits the box-counting dimension to remove abnormal signals in the original sequence, which guarantees the completeness of decomposition and obtains better decomposition results; (b) SSA is exploited to optimize the KELM, which can significantly boost the prediction effects; (c) When compared with the other seven benchmark models, the proposed framework in this paper is more suitable for predicting blood glucose and has higher prediction accuracy; (d) This paper also verifies that the proposed architecture can effectively reduce the false negative rate and increase the overall alarming accuracy in early warning of hypoglycemia.

Suggested Citation

  • Zhu, Ting & Wang, Wenbo & Yu, Min, 2022. "A novel blood glucose time series prediction framework based on a novel signal decomposition method," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
    • Handle: RePEc:eee:chsofr:v:164:y:2022:i:c:s0960077922008529
    DOI: 10.1016/j.chaos.2022.112673
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0960077922008529
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.chaos.2022.112673?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. Tang, Li-Hong & Bai, Yu-Long & Yang, Jie & Lu, Ya-Ni, 2020. "A hybrid prediction method based on empirical mode decomposition and multiple model fusion for chaotic time series," Chaos, Solitons & Fractals, Elsevier, vol. 141(C).
    2. Xiu, Chunbo & Wang, Tiantian & Tian, Meng & Li, Yanqing & Cheng, Yi, 2014. "Short-term prediction method of wind speed series based on fractal interpolation," Chaos, Solitons & Fractals, Elsevier, vol. 68(C), pages 89-97.
    3. Zhang, Yagang & Pan, Guifang & Chen, Bing & Han, Jingyi & Zhao, Yuan & Zhang, Chenhong, 2020. "Short-term wind speed prediction model based on GA-ANN improved by VMD," Renewable Energy, Elsevier, vol. 156(C), pages 1373-1388.
    4. Wang, Rui & Singh, Abhinandan Kumar & Kolan, Subash Reddy & Tsotsas, Evangelos, 2022. "Fractal analysis of aggregates: Correlation between the 2D and 3D box-counting fractal dimension and power law fractal dimension," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    5. Wang, Jujie & Cui, Quan & He, Maolin, 2022. "Hybrid intelligent framework for carbon price prediction using improved variational mode decomposition and optimal extreme learning machine," Chaos, Solitons & Fractals, Elsevier, vol. 156(C).
    6. Zhou, Yilin & Wang, Jianzhou & Lu, Haiyan & Zhao, Weigang, 2022. "Short-term wind power prediction optimized by multi-objective dragonfly algorithm based on variational mode decomposition," Chaos, Solitons & Fractals, Elsevier, vol. 157(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. Orang, Omid & de Lima e Silva, Petrônio Cândido & Guimarães, Frederico Gadelha, 2023. "Multi-output time series forecasting with randomized multivariate Fuzzy Cognitive Maps," Chaos, Solitons & Fractals, Elsevier, vol. 176(C).
    2. Zhu, Ting & Wang, Wenbo & Yu, Min, 2023. "A novel hybrid scheme for remaining useful life prognostic based on secondary decomposition, BiGRU and error correction," Energy, Elsevier, vol. 276(C).
    3. Wu, Han & Liang, Yan & Gao, Xiao-Zhi, 2023. "Left-right brain interaction inspired bionic deep network for forecasting significant wave height," Energy, Elsevier, vol. 278(PB).

    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. Ai, Chunyu & He, Shan & Hu, Heng & Fan, Xiaochao & Wang, Weiqing, 2023. "Chaotic time series wind power interval prediction based on quadratic decomposition and intelligent optimization algorithm," Chaos, Solitons & Fractals, Elsevier, vol. 177(C).
    2. Jin, Ji & Tian, Jinyu & Yu, Min & Wu, Yong & Tang, Yuanyan, 2024. "A novel ultra-short-term wind speed prediction method based on dynamic adaptive continued fraction," Chaos, Solitons & Fractals, Elsevier, vol. 180(C).
    3. Ribeiro, Matheus Henrique Dal Molin & da Silva, Ramon Gomes & Ribeiro, Gabriel Trierweiler & Mariani, Viviana Cocco & Coelho, Leandro dos Santos, 2023. "Cooperative ensemble learning model improves electric short-term load forecasting," Chaos, Solitons & Fractals, Elsevier, vol. 166(C).
    4. Qingyuan Wang & Longnv Huang & Jiehui Huang & Qiaoan Liu & Limin Chen & Yin Liang & Peter X. Liu & Chunquan Li, 2022. "A Hybrid Generative Adversarial Network Model for Ultra Short-Term Wind Speed Prediction," Sustainability, MDPI, vol. 14(15), pages 1-16, July.
    5. Zhang, Yagang & Wang, Hui & Wang, Jingchao & Cheng, Xiaodan & Wang, Tong & Zhao, Zheng, 2024. "Ensemble optimization approach based on hybrid mode decomposition and intelligent technology for wind power prediction system," Energy, Elsevier, vol. 292(C).
    6. Liu, Xingdou & Zhang, Li & Wang, Jiangong & Zhou, Yue & Gan, Wei, 2023. "A unified multi-step wind speed forecasting framework based on numerical weather prediction grids and wind farm monitoring data," Renewable Energy, Elsevier, vol. 211(C), pages 948-963.
    7. Sumit Saroha & Marta Zurek-Mortka & Jerzy Ryszard Szymanski & Vineet Shekher & Pardeep Singla, 2021. "Forecasting of Market Clearing Volume Using Wavelet Packet-Based Neural Networks with Tracking Signals," Energies, MDPI, vol. 14(19), pages 1-21, September.
    8. Zhu, Xiaoxun & Liu, Ruizhang & Chen, Yao & Gao, Xiaoxia & Wang, Yu & Xu, Zixu, 2021. "Wind speed behaviors feather analysis and its utilization on wind speed prediction using 3D-CNN," Energy, Elsevier, vol. 236(C).
    9. Wang, Yun & Zou, Runmin & Liu, Fang & Zhang, Lingjun & Liu, Qianyi, 2021. "A review of wind speed and wind power forecasting with deep neural networks," Applied Energy, Elsevier, vol. 304(C).
    10. Xu, Yingying & Dai, Yifan & Guo, Lingling & Chen, Jingjing, 2024. "Leveraging machine learning to forecast carbon returns: Factors from energy markets," Applied Energy, Elsevier, vol. 357(C).
    11. Wang, Jianzhou & An, Yining & Li, Zhiwu & Lu, Haiyan, 2022. "A novel combined forecasting model based on neural networks, deep learning approaches, and multi-objective optimization for short-term wind speed forecasting," Energy, Elsevier, vol. 251(C).
    12. Li, Ke & Shen, Ruifang & Wang, Zhenguo & Yan, Bowen & Yang, Qingshan & Zhou, Xuhong, 2023. "An efficient wind speed prediction method based on a deep neural network without future information leakage," Energy, Elsevier, vol. 267(C).
    13. Wang, Jianzhou & Niu, Xinsong & Zhang, Lifang & Liu, Zhenkun & Wei, Danxiang, 2022. "The influence of international oil prices on the exchange rates of oil exporting countries: Based on the hybrid copula function," Resources Policy, Elsevier, vol. 77(C).
    14. Bai, Yulong & Liu, Ming-De & Ding, Lin & Ma, Yong-Jie, 2021. "Double-layer staged training echo-state networks for wind speed prediction using variational mode decomposition," Applied Energy, Elsevier, vol. 301(C).
    15. Zhang, Yagang & Zhang, Jinghui & Yu, Leyi & Pan, Zhiya & Feng, Changyou & Sun, Yiqian & Wang, Fei, 2022. "A short-term wind energy hybrid optimal prediction system with denoising and novel error correction technique," Energy, Elsevier, vol. 254(PC).
    16. Wang, Jianzhou & Wang, Shuai & Li, Zhiwu, 2021. "Wind speed deterministic forecasting and probabilistic interval forecasting approach based on deep learning, modified tunicate swarm algorithm, and quantile regression," Renewable Energy, Elsevier, vol. 179(C), pages 1246-1261.
    17. Li, Qingyang & Wang, Guosong & Wu, Xinrong & Gao, Zhigang & Dan, Bo, 2024. "Arctic short-term wind speed forecasting based on CNN-LSTM model with CEEMDAN," Energy, Elsevier, vol. 299(C).
    18. Serpa, Cristina & Buescu, Jorge, 2015. "Explicitly defined fractal interpolation functions with variable parameters," Chaos, Solitons & Fractals, Elsevier, vol. 75(C), pages 76-83.
    19. Yu, Min & Niu, Dongxiao & Gao, Tian & Wang, Keke & Sun, Lijie & Li, Mingyu & Xu, Xiaomin, 2023. "A novel framework for ultra-short-term interval wind power prediction based on RF-WOA-VMD and BiGRU optimized by the attention mechanism," Energy, Elsevier, vol. 269(C).
    20. Chen, Zhengganzhe & Zhang, Bin & Du, Chenglong & Meng, Wei & Meng, Anbo, 2024. "A novel dynamic spatio-temporal graph convolutional network for wind speed interval prediction," Energy, Elsevier, vol. 294(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:164:y:2022:i:c:s0960077922008529. 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.