de Farias João Lucas Correia Barbosa, Bessa Wallace Moreira
Department of Mechanical Engineering, Federal University of Rio Grande do Norte, Natal 59078-970, RN, Brazil.
Department of Mechanical and Materials Engineering, University of Turku, 20500 Turku, Finland.
Bioengineering (Basel). 2022 Nov 8;9(11):664. doi: 10.3390/bioengineering9110664.
Type 1 diabetes is a disease that affects millions of people around the world. Recent progress in embedded devices has allowed the development of artificial pancreas that can pump insulin subcutaneously to automatically regulate blood glucose levels in diabetic patients. In this work, a Lyapunov-based intelligent controller using artificial neural networks is proposed for application in automated insulin delivery systems. The adoption of an adaptive radial basis function network within the control scheme allows regulation of blood glucose levels without the need for a dynamic model of the system. The proposed model-free approach does not require the patient to inform when they are going to have a meal and is able to deal with inter- and intrapatient variability. To ensure safe operating conditions, the stability of the control law is rigorously addressed through a Lyapunov-like analysis. In silico analysis using virtual patients are provided to demonstrate the effectiveness of the proposed control scheme, showing its ability to maintain normoglycemia in patients with type 1 diabetes . Three different scenarios were considered: one long- and two short-term simulation studies. In the short-term analyses, 20 virtual patients were simulated for a period of 7 days, with and without prior basal therapy, while in the long-term simulation, 1 virtual patient was assessed over 63 days. The results show that the proposed approach was able to guarantee a time in the range above 95% for the target glycemia in all scenarios studied, which is in fact well above the desirable 70%. Even in the long-term analysis, the intelligent control scheme was able to keep blood glucose metrics within clinical care standards: mean blood glucose of 119.59 mg/dL with standard deviation of 32.02 mg/dL and coefficient of variation of 26.78%, all below the respective reference values.
1型糖尿病是一种影响着全球数百万人的疾病。嵌入式设备的最新进展使得人工胰腺得以开发，这种人工胰腺能够皮下注射胰岛素，以自动调节糖尿病患者的血糖水平。在这项工作中，提出了一种基于李雅普诺夫的使用人工神经网络的智能控制器，用于自动胰岛素输送系统。在控制方案中采用自适应径向基函数网络，无需系统动态模型即可调节血糖水平。所提出的无模型方法不需要患者告知用餐时间，并且能够应对患者之间和患者自身的变异性。为确保安全的操作条件，通过类似李雅普诺夫分析严格探讨了控制律的稳定性。提供了使用虚拟患者的计算机模拟分析，以证明所提出控制方案的有效性，显示其在1型糖尿病患者中维持正常血糖水平的能力。考虑了三种不同的情况：一项长期和两项短期模拟研究。在短期分析中，对20名虚拟患者进行了为期7天的模拟，有和没有先前的基础治疗，而在长期模拟中，对1名虚拟患者进行了63天的评估。结果表明，在所研究的所有情况下，所提出方法能够保证目标血糖水平在95%以上的时间，这实际上远高于理想的70%。即使在长期分析中，智能控制方案也能够将血糖指标保持在临床护理标准范围内：平均血糖为119.59mg/dL，标准差为32.02mg/dL，变异系数为26.78%，均低于各自的参考值。
