Govindaraju Divya, Subbian Sutha
Department of Instrumentation Engineering, MIT Campus, Anna University, Chennai, 600044, Tamil Nadu, India.
Sci Rep. 2025 Sep 1;15(1):32045. doi: 10.1038/s41598-025-18085-9.
Diabetes is a chronic disorder that disrupts the body's ability to regulate blood glucose (BG) levels, leading to dangerous fluctuations such as hypoglycemia and hyperglycemia. In managing Type 1 Diabetes (T1D), the Dual Hormone Artificial Pancreas (DHAP) has emerged as a promising solution for maintaining optimal BG levels by administering both insulin and glucagon. However, the major challenges in DHAPs are slow dynamics in glucose sensing and delayed insulin absorption. In this paper, a Smart Dual Hormone Artificial Pancreas (SDHAP) with Event-triggered Feed-Back (FB)-Feed Forward (FF) control schemes are proposed to control the BG level of diabetic individuals and reject external disturbance due to food intake or exercise. Firstly, the classification of blood glucose level was performed with features extracted from the T1DiabetesGranada dataset using Machine Learning (ML) algorithms like K-Nearest Neighbor (KNN) and Support Vector Machine (SVM), and BG levels were predicted using time-series analysis. Secondly, the Event -Triggered Proportional-Integral feedback controllers: Proportional Integral (PI) and Model Predictive Control are designed based on the Bergman Minimal Model (BMM) model to deliver appropriate hormones namely insulin/glucagon based on predicted results. Finally, the FF controller was designed to reject external disturbances under hypoglycemia and hyperglycemia conditions. The results show the proposed SDHAP is more effective in controlling blood glucose by delivering patient-specific drugs with appropriate dosages based on individualized pathological conditions of T1D patients.
糖尿病是一种慢性疾病，它会破坏人体调节血糖（BG）水平的能力，导致低血糖和高血糖等危险波动。在1型糖尿病（T1D）的管理中，双激素人工胰腺（DHAP）已成为一种有前景的解决方案，通过同时注射胰岛素和胰高血糖素来维持最佳血糖水平。然而，DHAP的主要挑战在于葡萄糖传感的动力学缓慢和胰岛素吸收延迟。本文提出了一种具有事件触发反馈（FB）-前馈（FF）控制方案的智能双激素人工胰腺（SDHAP），以控制糖尿病患者的血糖水平，并抵御因食物摄入或运动引起的外部干扰。首先，使用机器学习（ML）算法，如K近邻（KNN）和支持向量机（SVM），从T1DiabetesGranada数据集中提取特征来进行血糖水平分类，并使用时间序列分析来预测血糖水平。其次，基于伯格曼最小模型（BMM）设计事件触发比例积分反馈控制器：比例积分（PI）和模型预测控制，以便根据预测结果输送适当的激素，即胰岛素/胰高血糖素。最后，设计FF控制器以抵御低血糖和高血糖条件下的外部干扰。结果表明，所提出的SDHAP通过根据T1D患者的个体化病理状况提供适当剂量的个性化药物，在控制血糖方面更有效。
