title: Personalized State-space Modeling of Glucose Dynamics for Type 1 Diabetes Using Continuously Monitored Glucose, Insulin Dose, and Meal Intake: An Extended Kalman Filter Approach.
abstract: An essential component of any artificial pancreas is on the prediction of blood glucose levels as a function of exogenous and endogenous perturbations such as insulin dose, meal intake, and physical activity and emotional tone under natural living conditions. In this article, we present a new data-driven state-space dynamic model with time-varying coefficients that are used to explicitly quantify the time-varying patient-specific effects of insulin dose and meal intake on blood glucose fluctuations. Using the 3-variate time series of glucose level, insulin dose, and meal intake of an individual type 1 diabetic subject, we apply an extended Kalman filter (EKF) to estimate time-varying coefficients of the patient-specific state-space model. We evaluate our empirical modeling using (1) the FDA-approved UVa/Padova simulator with 30 virtual patients and (2) clinical data of 5 type 1 diabetic patients under natural living conditions. Compared to a forgetting-factor-based recursive ARX model of the same order, the EKF model predictions have higher fit, and significantly better temporal gain and J index and thus are superior in early detection of upward and downward trends in glucose. The EKF based state-space model developed in this article is particularly suitable for model-based state-feedback control designs since the Kalman filter estimates the state variable of the glucose dynamics based on the measured glucose time series. In addition, since the model parameters are estimated in real time, this model is also suitable for adaptive control.
authors.affiliation: Department of Mechanical and Nuclear Engineering, Pennsylvania State University, University Park, PA, USA quw6@psu.edu.
authors.forename: Qian
authors.initials: Q
authors.lastname: Wang
authors.affiliation: Department of Mechanical and Nuclear Engineering, Pennsylvania State University, University Park, PA, USA.
authors.forename: Peter
authors.initials: P
authors.lastname: Molenaar
authors.affiliation: Department of Mechanical and Nuclear Engineering, Pennsylvania State University, University Park, PA, USA.
authors.forename: Saurabh
authors.initials: S
authors.lastname: Harsh
authors.affiliation: Department of Integrated Power Systems, Applied Research Lab, Pennsylvania State University, University Park, PA, USA.
authors.forename: Kenneth
authors.initials: K
authors.lastname: Freeman
authors.affiliation: Department of Mechanical and Nuclear Engineering, Pennsylvania State University, University Park, PA, USA.
authors.forename: Jinyu
authors.initials: J
authors.lastname: Xie
authors.affiliation: Department of Human Development and Family Studies, Pennsylvania State University, University Park, PA, USA.
authors.forename: Carol
authors.initials: C
authors.lastname: Gold
authors.affiliation: Department of Human Development and Family Studies, Pennsylvania State University, University Park, PA, USA.
authors.forename: Mike
authors.initials: M
authors.lastname: Rovine
authors.affiliation: Department of Biobehavioral Health and Medicine, Institute for Diabetes and Obesity, Pennsylvania State University, University Park, PA, USA.
authors.forename: Jan
authors.initials: J
authors.lastname: Ulbrecht
keywords: Kalman filter; artificial pancreas; empirical model; glucose; identification; type 1 diabetes