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A New Stochastic Approach for Modeling Glycemic Disturbances in Type 2 Diabetes

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Objective: To improve insulin treatment in type 2 diabetes (T2D) using model-based control techniques, the underlying model needs to be individualized to each patient. Due to the impact of unknown meals, exercise and other factors on the blood glucose, it is difficult to utilize available data from continuous glucose monitors (CGMs) for model fitting and parameter estimation purposes. Methods: To overcome this problem, we propose a novel method for modeling the glycemic disturbances as a stochastic process. To differentiate meals from other glycemic disturbances, we model the meal intake as a separate stochastic process while encompassing all other disturbances in another stochastic process. Using particle filtering, we validate the model on simulations as well as on clinical data. Results: Based on simulated CGM data, the residuals generated by the particle filter are white, indicating a good model fit. For the clinical data, we use parameter values estimated based on fasting glucose data. The residuals obtained from clinical CGM data contain correlations up to lag 5. Conclusion: The proposed model is shown to adequately describe the meal-induced glucose fluctuations in simulated CGM data while validations on clinical CGM data show promising results as well. Significance: The proposed model may lay the grounds for new ways of utilizing available CGM data, including CGM-based parameter estimation and stochastic optimal control.

Original languageEnglish
JournalIEEE Transactions on Biomedical Engineering
Volume68
Issue number10
Pages (from-to)3161-3172
Number of pages12
ISSN0018-9294
DOIs
Publication statusPublished - Oct 2021

Bibliographical note

Funding Information:
Manuscript received September 18, 2020; revised January 8, 2021 and February 19, 2021; accepted April 5, 2021. Date of publication April 21, 2021; date of current version September 20, 2021. This work was supported by the IFD Grand Solution project under Grant ADAPT-T2D, project number 9068-00056B. (Corresponding author: Henrik Glavind Clausen.) Henrik Glavind Clausen is with the Section of Automation and Control, Department of Electronic Systems, Aalborg University, DK-9220 Aalborg Øst, Denmark (e-mail: hgcl@es.aau.dk).

Publisher Copyright:
© 2021 IEEE.

    Research areas

  • Parameter estimation, particle filtering, stochastic differential equations, type 2 diabetes

ID: 68200648