Abstract
Monitoring of blood glucose (BG) in diabetic patients is of utmost importance and an essential requirement in the development of an artificial pancreas for diabetes treatment based on the supply of exogenous insulin (EI). The algorithms of practical utility for an automated insulin infusion must consider discrete and low-order dynamic models for their efficient implementation and continuous execution. A virtual patient, based on a 19th-order nonlinear continuous physiological model, is used to analyze and represent the dynamics of glucose metabolism by stochastic identification. The resulting third-order linear discrete models strike an appropriate balance between accuracy and complexity. These models are used to program a discrete Kalman filter to predict BG in a virtual patient, taking advantage of its capabilities for handling measurement uncertainty and limitations in modeling accuracy. Tuning the parameters of the Kalman filter, by calculated process noise and average current BG meter accuracy, is discussed in this work and applied to evaluate its effect. Tuned noise parameters improve the performance of the Kalman filter during carbohydrate intake, EI delivery, random variation of measurement parameters, and measurement loss, achieving improvements of more than 50% with respect to the use of unquantified process and measurement uncertainty.
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Tavarez, J.R., Sanchez, I.Y., Maldonado, V.A., Montes, M., Ortiz, R.A. (2022). Stochastic Identification and Kalman Filter for Blood Glucose Estimation. In: Flores Rodríguez, K.L., Ramos Alvarado, R., Barati, M., Segovia Tagle, V., Velázquez González, R.S. (eds) Recent Trends in Sustainable Engineering. ICASAT 2021. Lecture Notes in Networks and Systems, vol 297. Springer, Cham. https://doi.org/10.1007/978-3-030-82064-0_10
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