Multivariate Statistical Monitoring of Sensor Faults of A Multivariable Artificial Pancreas

Abstract

Sensor faults in an artificial pancreas (AP) system for people with type 1 diabetes (T1D) can yield insulin infusion rates that may cause hypoglycemia or hyperglycemia. New statistical process monitoring methods are proposed for real-time detection of sensor-related faults in AP systems to mitigate their effects. Remodulated dynamic time warping for synchronization of signal trajectories and Savitzky-Golay filter for calculation of real-time numerical derivatives are integrated with multiway principal component analysis. Data from 14 subjects that participated in 60 hours of closed-loop AP experiments with variations in meals and physical activity levels and times are used. Glucose measurements from a continuous glucose monitoring sensor are monitored for fault detection. The results illustrate that the proposed method is able to detect various types of unexpected dynamical changes or faults, and label them correctly. There were no missed faults in any tested cases. The algorithm can inform AP users about sensor faults and unexpected changes, and provides valuable information to an AP for prevention hypoglycemia or hyperglycemia that may be caused by sensor faults.