Event-triggered smart dual hormone artificial pancreas for patient-specific drug delivery

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.