Udskriv Udskriv
Switch language
Region Hovedstaden - en del af Københavns Universitetshospital

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

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review


  1. Characterization of Blood Pressure and Heart Rate Oscillations of POTS Patients via Uniform Phase Empirical Mode Decomposition

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

  2. Using Kalman filtering to predict time-varying parameters in a model predicting baroreflex regulation during head-up tilt

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

  3. EMGTools, an adaptive and versatile tool for detailed EMG analysis

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

  1. Is reimbursement for alerts and real-time continuous glucose monitoring needed?

    Publikation: Bidrag til tidsskriftKommentar/debatForskningpeer review

  2. Changes in the lipidome in type 1 diabetes following low carbohydrate diet: Post-hoc analysis of a randomized crossover trial

    Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

Vis graf over relationer

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.

TidsskriftIEEE Transactions on Biomedical Engineering
Udgave nummer10
Sider (fra-til)3161-3172
Antal sider12
StatusUdgivet - okt. 2021

Bibliografisk note

Publisher Copyright:
© 2021 IEEE.

ID: 68200648