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.

Figures

Fig. 1

Glucose prediction with multivariate and univariate models for a typical 24 h period of one of the subjects.

Fig. 2

Early hypoglycemic alarms with the proposed multivariate algorithm for 30 min ahead prediction.

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).