Model-Based Detection and Classification of Insulin Pump Faults and Missed Meal Announcements in Artificial Pancreas Systems for Type 1 Diabetes Therapy

Objective: The artificial pancreas (AP) is an innovative closed-loop system for type 1 diabetes therapy, in which insulin is infused by portable pumps and insulin dosage is modulated by a control algorithm on the basis of the measurements collected by continuous glucose monitoring (CGM) sensors. AP systems safety and effectiveness could be affected by several technological and user-related issues, among which insulin pump faults and missed meal announcements. This work proposes an algorithm to detect in real-time these two types of failure. Methods: The algorithm works as follows. First, a personalized autoregressive moving-average model with exogenous inputs is identified using historical data of the patient. Second, the algorithm is used in real time to predict future CGM values. Then, alarms are triggered when the difference between predicted vs measured CGM values is higher than opportunely set thresholds. In addition, by using two different set of parameters, the algorithm is able to distinguish the two types of failures. The algorithm was developed and assessed in silico using the latest version of the FDA-approved Padova/UVa T1D simulator. Results: The algorithm showed a sensitivity of ∼81.3% on average when detecting insulin pump faults with ∼0.15 false positives per day on average. Missed meal announcements were detected with a sensitivity of ∼86.8% and 0.15 FP/day. Conclusion: The presented method is able to detect insulin pump faults and missed meal announcements in silico, correctly distinguishing one from another. Significance: The method increases the safety of AP systems by providing prompt alarms to the diabetic subject and effectively discriminating pump malfunctioning from user errors.