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

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  • 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
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    References listed on IDEAS

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    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. 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).
    3. 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.
    4. 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.
    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. 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).
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    Cited by:

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    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).

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