Adaptive System Identification for Estimating Future Glucose Concentrations and Hypoglycemia Alarms
Meriyan Eren-Oruklu, Ali Cinar, Derrick K Rollins, Lauretta Quinn, Meriyan Eren-Oruklu, Ali Cinar, Derrick K Rollins, Lauretta Quinn
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](https://www.ncbi.nlm.nih.gov/pmc/articles/instance/3409594/bin/nihms389282f1.jpg)
![Fig. 2](https://www.ncbi.nlm.nih.gov/pmc/articles/instance/3409594/bin/nihms389282f2.jpg)
![Fig. 3](https://www.ncbi.nlm.nih.gov/pmc/articles/instance/3409594/bin/nihms389282f3.jpg)
Source: PubMed