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Estimation of Future Glucose Concentrations with Subject-Specific Recursive Linear Models
Title
Estimation of Future Glucose Concentrations with Subject-Specific Recursive Linear Models
Creator
Eren-oruklu, Meriyan, Cinar, Ali, Quinn, Lauretta, Smith, Donald
Date
2009-04
Publisher
MARY ANN LIEBERT INC
Description
Background: Estimation of future glucose concentrations is a crucial task for diabetes management. Predicted glucose values can be used for...
Show moreBackground: Estimation of future glucose concentrations is a crucial task for diabetes management. Predicted glucose values can be used for early hypoglycemic/hyperglycemic alarms or for adjustment of insulin injections or insulin infusion rates of manual or automated pumps. Continuous glucose monitoring (CGM) technologies provide glucose readings at a high frequency and consequently detailed insight into the subject's glucose variations. The objective of this research is to develop reliable subject-specific glucose prediction models using CGM data. Methods: Two separate patient databases collected under hospitalized (disturbance-free) and normal daily life conditions are used for validation of the proposed glucose prediction algorithm. Both databases consist of glucose concentration data collected at 5-min intervals using a CGM device. Using time-series analysis, low-order linear models are developed from patients' own CGM data. The time-series models are integrated with recursive identification and change detection methods, which enables dynamic adaptation of the model to inter-/intra-subject variability and glycemic disturbances. Prediction performance is evaluated in terms of glucose prediction error and Clarke Error Grid analysis (CG-EGA). Results: Prediction errors are significantly reduced with recursive identification of the models, and predictions are further improved with inclusion of a parameter change detection method. CG-EGA analysis results in accurate readings of 90% or more. Conclusions: Subject-specific glucose prediction strategy has been developed. Including a change detection method to the recursive algorithm improves the prediction accuracy. The proposed modeling algorithm with small number of parameters is a good candidate for installation in portable devices for early hypoglycemic/hyperglycemic alarms and for closing the glucose regulation loop with an insulin pump.
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Adaptive control strategy for regulation of blood glucose levels in patients with type 1 diabetes
Title
Adaptive control strategy for regulation of blood glucose levels in patients with type 1 diabetes
Creator
Eren-oruklu, Meriyan, Cinar, Ali, Quinn, Lauretta, Smith, Donald
Date
2009-09
Publisher
ELSEVIER SCI LTD
Description
Current insulin therapy for patients with type 1 diabetes often results in high variability in blood glucose concentrations and may cause hype...
Show moreCurrent insulin therapy for patients with type 1 diabetes often results in high variability in blood glucose concentrations and may cause hype rglycemic/hypoglycemic episodes. Closing the glucose control loop with a fully automated electro-mechanical pancreas will improve the quality of life for insulin-dependent patients. An adaptive control algorithm is proposed to keep glucose concentrations within normoglycemic range and dynamically respond to glycemic challenges. A model-based control strategy is used to calculate the required insulin infusion rate, while the model parameters are recursively tuned. The algorithm handles delays associated with insulin absorption, time-lag between subcutaneous and blood glucose concentrations, and variations in inter/intra-subject glucose-insulin dynamics. Simulation results for simultaneous meat and physiological disturbances are demonstrated for subcutaneous insulin infusion.
Endnote format citation for DOI:10.1016/j.jprocont.2009.04.004
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