title: Adaptive System Identification for Estimating Future Glucose Concentrations and Hypoglycemia Alarms.
abstract: Many patients with diabetes experience high variability in glucose concentrations that includes prolonged hyperglycemia or hypoglycemia. Models predicting a subject's future glucose concentrations can be used for preventing such conditions by providing early alarms. This paper presents a time-series model that captures dynamical changes in the glucose metabolism. Adaptive system identification is proposed to estimate model parameters which enable the adaptation of the model to inter-/intra-subject variation and glycemic disturbances. It consists of online parameter identification using the weighted recursive least squares method and a change detection strategy that monitors variation in model parameters. Univariate models developed from a subject's continuous glucose measurements are compared to multivariate models that are enhanced with continuous metabolic, physical activity and lifestyle information from a multi-sensor body monitor. A real life application for the proposed algorithm is demonstrated on early (30 min in advance) hypoglycemia detection.
authors.affiliation: Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL 60616-3793, USA.
authors.forename: Meriyan
authors.initials: M
authors.lastname: Eren-Oruklu
authors.forename: Ali
authors.initials: A
authors.lastname: Cinar
authors.forename: Derrick K
authors.initials: DK
authors.lastname: Rollins
authors.forename: Lauretta
authors.initials: L
authors.lastname: Quinn