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. 2012 Aug;48(8):1892-1897.
doi: 10.1016/j.automatica.2012.05.076. Epub 2012 Jun 22.

Adaptive System Identification for Estimating Future Glucose Concentrations and Hypoglycemia Alarms

Meriyan Eren-Oruklu  1 , Ali Cinar, Derrick K Rollins, Lauretta Quinn
Affiliations

Adaptive System Identification for Estimating Future Glucose Concentrations and Hypoglycemia Alarms

Meriyan Eren-Oruklu et al. Automatica (Oxf). 2012 Aug.

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.

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Figures

Fig. 1
Fig. 1
Glucose prediction with multivariate and univariate models for a typical 24 h period of one of the subjects.
Fig. 2
Fig. 2
Early hypoglycemic alarms with the proposed multivariate algorithm for 30 min ahead prediction.
Fig. 3
Fig. 3
ROC curves for prediction horizon of 15, 30 and 45 minutes. Hypoglycemia thresholds are increased at 10 mg/dl intervals in the range of 60–120 mg/dl. Sensitivity = TruePositive/(TruePositive + FalseNegative), Specificity = TrueNegative/(TrueNegative + FalsePositive).
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