School of General Quality Education, Wuchang University of Technology, Wuhan, China.
Crit Rev Biomed Eng. 2021;49(2):9-19. doi: 10.1615/CritRevBiomedEng.2021038397.
Aiming at the difficulty of accurate prediction due to the randomness and nonstationary nature of blood glucose concentration series, a blood glucose concentration prediction model based on complementary ensemble empirical mode decomposition (CEEMD) and least squares support vector machine (LSSVM) is proposed. Firstly, CEEMD is used to convert the blood glucose concentration sequence into a series of intrinsic mode functions (IMFs) to reduce the impact of randomness and nonstationary signals on prediction performance. Then, a LSSVM prediction model is established for each mode IMF. The comprehensive learning particle swarm optimization (CLPSO) algorithm is used to optimize the kernel parameters of LSSVM. Finally, the prediction results of all IMFs are superimposed to yield the final blood glucose concentration prediction value. The experimental results show that the proposed prediction model has higher prediction accuracy in short-term blood glucose concentration values.
针对血糖浓度序列的随机性和非平稳性导致预测困难的问题，提出了一种基于互补集合经验模态分解（CEEMD）和最小二乘支持向量机（LSSVM）的血糖浓度预测模型。首先，利用 CEEMD 将血糖浓度序列转换为一系列固有模态函数（IMF），以降低随机性和非平稳信号对预测性能的影响。然后，针对每个模态 IMF 建立 LSSVM 预测模型。采用综合学习粒子群优化（CLPSO）算法优化 LSSVM 的核参数。最后，将所有 IMF 的预测结果叠加得到最终的血糖浓度预测值。实验结果表明，所提出的预测模型在短期血糖浓度值预测中具有更高的预测精度。
