Adaptive system identification for estimating future glucose concentrations and hypoglycemia alarms

  • Authors:

  • Meriyan Eren-Oruklu;Ali Cinar;Derrick K. Rollins;Lauretta Quinn

  • Affiliations:

  • Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL 60616-3793, USA;Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL 60616-3793, USA;Department of Chemical and Biological Engineering, Iowa State University, Ames, IA 50011-2230, USA;College of Nursing, University of Illinois at Chicago, Chicago, IL 60612-7350, USA

  • Venue:
  • Automatica (Journal of IFAC)
  • Year:
  • 2012

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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 on-line 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.