Monitoring and fault detection of continuous glucose sensor measurements

Faults in subcutaneous glucose concentration readings can cause the computation of insulin infusion rates that can lead to hypoglycemia or hyperglycemia in artificial pancreas control systems for patients with type 1 diabetes (T1D). In this paper, multivariable statistical monitoring methods are used for detection of faults in glucose concentration values reported by a glucose sensor. Multiway principal component analysis is used to develop a model that describes the expected variation in glucose readings under normal conditions. Various meal scenarios are defined to generate different data-sets using the UVa/Padova metabolic simulator. Dynamic time warping is used for synchronization of different scenarios. Different faults such as step, random, drift and exponential changes are tested. The results show that the proposed method is able to detect various types of faults with high accuracy. Detailed analysis of sensitivity, false detection ratio and detection time for different fault types is also presented. The proposed fault detection algorithm can decrease the effects of faults on insulin infusion rates and reduce the potential for hypo- or hyperglycemia for patients with T1D.