Real-Time Model-Based Fault Detection of Continuous Glucose Sensor Measurements.

Abstract

Faults in subcutaneous glucose concentration readings with a continuous glucose monitoring (CGM) may affect 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). Multivariable statistical monitoring methods are proposed for detection of faults in glucose concentration values reported by a subcutaneous glucose sensor. A nonlinear first principle glucose/insulin/meal dynamic model is developed. An unscented Kalman filter is used for state and parameter estimation of the nonlinear model. Principal component analysis models are developed and used for detection of dynamic changes. K-nearest neighbor classification algorithm is used for diagnosis of faults. Data from 51 subjects are used to assess the performance of the algorithm. The results indicate that the proposed algorithm works successfully with 84.2% sensitivity. Overall, 155 (out of 184) of the CGM failures are detected with a 2.8-min average detection time. A novel algorithm that integrates data-driven and model-based methods is developed. The proposed method is able to detect CGM failures with a high rate of success. 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.