Iman Hajizadeh1, Mudassir Rashid1, Sediqeh Samadi1, Jianyuan Feng1, Mert Sevil2, Nicole Hobbs2, Caterina Lazaro3, Zacharie Maloney2, Rachel Brandt2, Xia Yu4, Kamuran Turksoy2, Elizabeth Littlejohn5, Eda Cengiz6, Ali Cinar1,2. 1. 1 Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL, USA. 2. 2 Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, USA. 3. 3 Department of Electrical and Computer Engineering, Illinois Institute of Technology, Chicago, IL, USA. 4. 4 School of Information Science and Technology, Northeastern University, Shenyang, China. 5. 5 Department of Pediatrics and Medicine, Section of Endocrinology, Kovler Diabetes Center, University of Chicago, Chicago, IL, USA. 6. 6 Department of Pediatrics, Yale University School of Medicine, New Haven, CT, USA.
Abstract
BACKGROUND: The artificial pancreas (AP) system, a technology that automatically administers exogenous insulin in people with type 1 diabetes mellitus (T1DM) to regulate their blood glucose concentrations, necessitates the estimation of the amount of active insulin already present in the body to avoid overdosing. METHOD: An adaptive and personalized plasma insulin concentration (PIC) estimator is designed in this work to accurately quantify the insulin present in the bloodstream. The proposed PIC estimation approach incorporates Hovorka's glucose-insulin model with the unscented Kalman filtering algorithm. Methods for the personalized initialization of the time-varying model parameters to individual patients for improved estimator convergence are developed. Data from 20 three-days-long closed-loop clinical experiments conducted involving subjects with T1DM are used to evaluate the proposed PIC estimation approach. RESULTS: The proposed methods are applied to the clinical data containing significant disturbances, such as unannounced meals and exercise, and the results demonstrate the accurate real-time estimation of the PIC with the root mean square error of 7.15 and 9.25 mU/L for the optimization-based fitted parameters and partial least squares regression-based testing parameters, respectively. CONCLUSIONS: The accurate real-time estimation of PIC will benefit the AP systems by preventing overdelivery of insulin when significant insulin is present in the bloodstream.
BACKGROUND: The artificial pancreas (AP) system, a technology that automatically administers exogenous insulin in people with type 1 diabetes mellitus (T1DM) to regulate their blood glucose concentrations, necessitates the estimation of the amount of active insulin already present in the body to avoid overdosing. METHOD: An adaptive and personalized plasma insulin concentration (PIC) estimator is designed in this work to accurately quantify the insulin present in the bloodstream. The proposed PIC estimation approach incorporates Hovorka's glucose-insulin model with the unscented Kalman filtering algorithm. Methods for the personalized initialization of the time-varying model parameters to individual patients for improved estimator convergence are developed. Data from 20 three-days-long closed-loop clinical experiments conducted involving subjects with T1DM are used to evaluate the proposed PIC estimation approach. RESULTS: The proposed methods are applied to the clinical data containing significant disturbances, such as unannounced meals and exercise, and the results demonstrate the accurate real-time estimation of the PIC with the root mean square error of 7.15 and 9.25 mU/L for the optimization-based fitted parameters and partial least squares regression-based testing parameters, respectively. CONCLUSIONS: The accurate real-time estimation of PIC will benefit the AP systems by preventing overdelivery of insulin when significant insulin is present in the bloodstream.
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Authors: Mert Sevil; Mudassir Rashid; Iman Hajizadeh; Minsun Park; Laurie Quinn; Ali Cinar Journal: IEEE Trans Biomed Eng Date: 2021-06-17 Impact factor: 4.756