Adaptive and Personalized Plasma Insulin Concentration Estimation for Artificial Pancreas Systems. (May 2018)

• Record Type: Journal Article
• Authors: Hajizadeh, Iman; Rashid, Mudassir; Samadi, Sediqeh; Feng, Jianyuan; Sevil, Mert; Hobbs, Nicole; Lazaro, Caterina; Maloney, Zacharie; Brandt, Rachel; Yu, Xia; Turksoy, Kamuran; Littlejohn, Elizabeth; Cengiz, Eda; Cinar, Ali
• Is Part Of: Journal of diabetes science and technology. Volume 12:Number 3(2018:May)
• Page Start: 639
• Page End: 649
• Publication Date: 2018-05
• Subjects: artificial pancreas -- glucose control -- hypoglycemia mitigation -- insulin on board -- plasma insulin concentration; Diabetes -- Periodicals; Medical technology -- Periodicals; Diabetes Mellitus -- Periodicals; 616.462005
• DOI: 10.1177/1932296818763959
• Languages: English
• ISSNs: 1932-2968

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.