Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Imperial College London, London SW7 2AZ, UK.
Department of Electrical and Electronic Engineering, Universitat de Girona and with CIBERDEM, Girona 17004, Spain.
Sensors (Basel). 2019 Oct 8;19(19):4338. doi: 10.3390/s19194338.
(1) Objective: Blood glucose forecasting in type 1 diabetes (T1D) management is a maturing field with numerous algorithms being published and a few of them having reached the commercialisation stage. However, accurate long-term glucose predictions (e.g., >60 min), which are usually needed in applications such as precision insulin dosing (e.g., an artificial pancreas), still remain a challenge. In this paper, we present a novel glucose forecasting algorithm that is well-suited for long-term prediction horizons. The proposed algorithm is currently being used as the core component of a modular safety system for an insulin dose recommender developed within the EU-funded PEPPER (Patient Empowerment through Predictive PERsonalised decision support) project. (2) Methods: The proposed blood glucose forecasting algorithm is based on a compartmental composite model of glucose-insulin dynamics, which uses a deconvolution technique applied to the continuous glucose monitoring (CGM) signal for state estimation. In addition to commonly employed inputs by glucose forecasting methods (i.e., CGM data, insulin, carbohydrates), the proposed algorithm allows the optional input of meal absorption information to enhance prediction accuracy. Clinical data corresponding to 10 adult subjects with T1D were used for evaluation purposes. In addition, in silico data obtained with a modified version of the UVa-Padova simulator was used to further evaluate the impact of accounting for meal absorption information on prediction accuracy. Finally, a comparison with two well-established glucose forecasting algorithms, the autoregressive exogenous (ARX) model and the latent variable-based statistical (LVX) model, was carried out. (3) Results: For prediction horizons beyond 60 min, the performance of the proposed physiological model-based (PM) algorithm is superior to that of the LVX and ARX algorithms. When comparing the performance of PM against the secondly ranked method (ARX) on a 120 min prediction horizon, the percentage improvement on prediction accuracy measured with the root mean square error, A-region of error grid analysis (EGA), and hypoglycaemia prediction calculated by the Matthews correlation coefficient, was 18.8 % , 17.9 % , and 80.9 % , respectively. Although showing a trend towards improvement, the addition of meal absorption information did not provide clinically significant improvements. (4) Conclusion: The proposed glucose forecasting algorithm is potentially well-suited for T1D management applications which require long-term glucose predictions.
(1) 目的：1 型糖尿病（T1D）管理中的血糖预测是一个成熟的领域，有许多算法被发表，其中一些已经进入商业化阶段。然而，通常在精准胰岛素给药（如人工胰腺）等应用中需要的准确的长期血糖预测（例如，>60 分钟）仍然是一个挑战。在本文中，我们提出了一种新的血糖预测算法，非常适合长期预测。所提出的算法目前被用作欧盟资助的 PEPPER（通过预测个性化决策支持赋予患者权力）项目中开发的胰岛素剂量推荐器的模块化安全系统的核心组件。(2) 方法：所提出的血糖预测算法基于葡萄糖-胰岛素动力学的容积复合模型，该模型使用连续血糖监测（CGM）信号的解卷积技术进行状态估计。除了血糖预测方法常用的输入（即 CGM 数据、胰岛素、碳水化合物）外，该算法还允许可选地输入进餐吸收信息以提高预测准确性。使用 10 名 T1D 成年受试者的临床数据进行评估。此外，使用经过修改的 UVa-Padova 模拟器获得的模拟数据进一步评估了考虑进餐吸收信息对预测准确性的影响。最后，与两种成熟的血糖预测算法（自回归外生（ARX）模型和基于潜在变量的统计（LVX）模型）进行了比较。(3) 结果：对于超过 60 分钟的预测范围，基于生理模型的（PM）算法的性能优于 LVX 和 ARX 算法。在 120 分钟预测范围内将 PM 与排名第二的方法（ARX）的性能进行比较时，以均方根误差、误差网格分析（EGA）的 A 区和 Matthews 相关系数计算的低血糖预测衡量，预测准确性的百分比提高分别为 18.8%、17.9%和 80.9%。尽管有改善的趋势，但添加进餐吸收信息并没有提供临床显著的改善。(4) 结论：所提出的血糖预测算法非常适合需要长期血糖预测的 T1D 管理应用。
