Adaptive system identification for estimating future glucose concentrations and hypoglycemia alarms

作者：Eren Oruklu Meriyan^*; Cinar Ali; Rollins Derrick K; Quinn Lauretta

来源：Automatica, 2012, 48(8): 1892-1897.

DOI：10.1016/j.automatica.2012.05.076

• Modeling and identification
• Parameter and state estimation
• Identification algorithms
• Recursive estimation

Many patients with diabetes experience high variability in glucose concentrations that includes prolonged hyperglycemia or hypoglycemia. Models predicting a subject%26apos;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%26apos;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.

• 出版日期2012-8