School of Information Engineering, Shandong Youth University of Political Science, Jinan, China.
Key Laboratory of Intelligent Information Processing and Information Security in Universities of Shandong, Jinan, China.
J Healthc Eng. 2022 Apr 11;2022:8956850. doi: 10.1155/2022/8956850. eCollection 2022.
Continuous noninvasive blood glucose monitoring and estimation management by using photoplethysmography (PPG) technology always have a series of problems, such as substantial time variability, inaccuracy, and complex nonlinearity. This paper proposes a blood glucose (BG) prediction model for more precise prediction based on BG series decomposition by complete aggregation empirical mode decomposition based on adaptive white noise (CEEMDAN) and the gated recurrent unit (GRU) that is optimized by improved bacterial foraging optimization (IBFO). Hierarchical clustering technology recombines the decomposed BG series according to their sample entropy and the correlations with the original BG trends. Dynamic BG trends are regressed separately for each recombined BG series by the GRU model to realize the more precise estimations, which are optimized by IBFO for its structure and superparameters. Through experiments, the optimized and basic LSTM, RNN, and support vector regression (SVR) are compared to evaluate the performance of the proposed model. The experimental results indicate that the root mean square error (RMSE) and mean absolute percentage error (MAPE) of the 15-min IBFO-GRU prediction is improved on average by about 13.1% and 18.4%, respectively, compared with those of the RNN and LSTM optimized by IBFO. Meanwhile, the proposed model improved the Clarke error grid results by about 2.6% and 5.0% compared with those of the IBFO-LSTM and IBFO-RNN in 30-min prediction and by 4.1% and 6.6% in 15-min ahead forecast, respectively. The evaluation outcomes of our proposed CEEMDAN-IBFO-GRU model have high accuracy and adaptability and can effectively provide early intervention control of the occurrence of hyperglycemic complications.
基于光电容积脉搏波（PPG）技术的连续无创血糖监测和估计管理一直存在一系列问题，例如实质性的时间可变性、不准确性和复杂的非线性。本文提出了一种基于 BG 系列分解的血糖（BG）预测模型，该模型通过基于自适应白噪声的完全聚合经验模态分解（CEEMDAN）和门控循环单元（GRU）对血糖进行更精确的预测，其中 GRU 是通过改进的细菌觅食优化（IBFO）进行优化的。层次聚类技术根据样本熵及其与原始 BG 趋势的相关性，对分解后的 BG 系列进行重新组合。通过 GRU 模型分别对每个重新组合的 BG 系列进行动态 BG 趋势回归，以实现更精确的估计，通过 IBFO 对其结构和超参数进行优化。通过实验，将优化后的和基本的 LSTM、RNN 和支持向量回归（SVR）进行比较，以评估所提出模型的性能。实验结果表明，与优化后的 LSTM 和 RNN 相比，15 分钟的 IBFO-GRU 预测的均方根误差（RMSE）和平均绝对百分比误差（MAPE）平均提高了约 13.1%和 18.4%。同时，与 IBFO-LSTM 和 IBFO-RNN 相比，该模型在 30 分钟预测中提高了 Clarke 误差网格结果约 2.6%和 5.0%，在 15 分钟预测中提高了约 4.1%和 6.6%。我们提出的 CEEMDAN-IBFO-GRU 模型的评估结果具有较高的准确性和适应性，可以有效地提供对高血糖并发症发生的早期干预控制。
