Detecting sensor and insulin infusion set anomalies in an artificial pancreas

Continuous subcutaneous insulin infusion pumps and continuous glucose monitors enable individuals with type 1 diabetes to achieve tighter blood glucose control, and are critical components in a closed-loop artificial pancreas. Insulin infusion sets can fail and CGM sensor signals can suffer from a variety of anomalies. In this paper algorithms are developed to detect infusion set failures and sensor signal anomalies; both in-patient and out-patient studies are presented. A threshold-based method, based on high glucose concentrations, is shown to be adequate to detect infusion set failures. Pressure-induced sensor attenuation (PISA), which can occur when a subject rolls over and puts pressure on their sensor, is a particularly challenging problem. An algorithm based on non-physiological rates-of-change, coupled with a maximum attenuation time window, is developed to detect and compensate for PISAs. These algorithms can be used either in advisory mode for current open-loop technology, as well as an additional safety/fault detection layer as part of a fully closed-loop artificial pancreas.