Plasma Insulin Estimation in People with Type 1 Diabetes Mellitus

Hajizadeh, Iman; Rashid, Mudassir; Turksoy, Kamuran; Samadi, Sediqeh; Feng, Jun‐e; Frantz, Nicole; Sevil, Mert; Cengiz, Eda; Çınar, Ali

doi:10.1021/acs.iecr.7b01618

Cited by 46 publications

(32 citation statements)

References 46 publications

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“…Models without meal announcement may classify rapid CGM increases caused by carbohydrate consumption and rapid decreases driven by large PIC as sensor faults. The sensor signal based on a meal detection module and a PIC estimation module developed in our research group can provide on‐line meal information and PIC information, respectively. The meal signal and PIC are integrated into the voting algorithm to reduce the number of false positives (FP) caused by meal consumption and high PIC values.…”

Section: Methodsmentioning

confidence: 99%

“…The meal signal and PIC are integrated into the voting algorithm to reduce the number of false positives (FP) caused by meal consumption and high PIC values. The detailed voting algorithm is described in the following procedure:

\begin{matrix} true \\ {Meal}_{k}^{m} = \{\begin{matrix} 0.5 & if {MPE}_{k ∣ k - 1}^{m} < 0 & {Flag}_{k}^{Meal} = 1 \\ 0 & otherwise \end{matrix} \\ m \in [ORKF, LW - PLS, PBSM, ALD - KRLS] \end{matrix}

\begin{matrix} true \\ {PIC}_{k}^{m} = \{\begin{matrix} 0.5 & if {MPE}_{k ∣ k - 1}^{m} > 0 & {PIC}_{k} > 25 mU {normalL}^{- 1} \\ 0 & otherwise \end{matrix} \\ m \in [ORKF, LW - PLS, PBSM, ALD - KRLS] \end{matrix}

\begin{matrix} true \\ {SC}_{k}^{V} = find (\max (\sum \frac{{OUT}_{k}^{m} + {([],,,,,, {Meal}_{k}^{m} + {PIC}_{k}^{m}, 0, 0, 0, 0, 0)}^{normalT}}{4})) \\ m = [ORKF, LW - PLS, PBSM, ALD - KRLS] \end{matrix}

where

{Flag}_{k}^{Meal}

is a binary variable provided by the meal detection module indicates the meal effect (1: meal effect exists, 0: meal effect does not exist) at step k. And PIC k at step k is indicated by the PIC estimation module .

{SC}_{k}^{V}

is the number of the largest element of the sensor condition vector.…”

Section: Methodsmentioning

confidence: 99%

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Multi‐model sensor fault detection and data reconciliation: A case study with glucose concentration sensors for diabetes

et al. 2018

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Erroneous information from sensors affect process monitoring and control. An algorithm with multiple model identification methods will improve the sensitivity and accuracy of sensor fault detection and data reconciliation (SFD&DR). A novel SFD&DR algorithm with four types of models including outlier robust Kalman filter, locally weighted partial least squares, predictor‐based subspace identification, and approximate linear dependency‐based kernel recursive least squares is proposed. The residuals are further analyzed by artificial neural networks and a voting algorithm. The performance of the SFD&DR algorithm is illustrated by clinical data from artificial pancreas experiments with people with diabetes. The glucose‐insulin metabolism has time‐varying parameters and nonlinearities, providing a challenging system for fault detection and data reconciliation. Data from 17 clinical experiments collected over 896 h were analyzed; the results indicate that the proposed SFD&DR algorithm is capable of detecting and diagnosing sensor faults and reconciling the erroneous sensor signals with better model‐estimated values. © 2018 American Institute of Chemical Engineers AIChE J, 65: 629–639, 2019

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Section: Methodsmentioning

confidence: 99%

\begin{matrix} true \\ {Meal}_{k}^{m} = \{\begin{matrix} 0.5 & if {MPE}_{k ∣ k - 1}^{m} < 0 & {Flag}_{k}^{Meal} = 1 \\ 0 & otherwise \end{matrix} \\ m \in [ORKF, LW - PLS, PBSM, ALD - KRLS] \end{matrix}

\begin{matrix} true \\ {PIC}_{k}^{m} = \{\begin{matrix} 0.5 & if {MPE}_{k ∣ k - 1}^{m} > 0 & {PIC}_{k} > 25 mU {normalL}^{- 1} \\ 0 & otherwise \end{matrix} \\ m \in [ORKF, LW - PLS, PBSM, ALD - KRLS] \end{matrix}

\begin{matrix} true \\ {SC}_{k}^{V} = find (\max (\sum \frac{{OUT}_{k}^{m} + {([],,,,,, {Meal}_{k}^{m} + {PIC}_{k}^{m}, 0, 0, 0, 0, 0)}^{normalT}}{4})) \\ m = [ORKF, LW - PLS, PBSM, ALD - KRLS] \end{matrix}

where

{Flag}_{k}^{Meal}

{SC}_{k}^{V}

is the number of the largest element of the sensor condition vector.…”

Section: Methodsmentioning

confidence: 99%

Multi‐model sensor fault detection and data reconciliation: A case study with glucose concentration sensors for diabetes

et al. 2018

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“…Several approaches have been tested to overcome such issue [11][12][13][14], but while an ultrarapid insulin analogue is not available [15], postprandial control using subcutaneous route will continue to be a challenging situation for closed-loop systems. Diverse studies have included estimations of insulin concentration in the body to avoid excessive insulin stacking [16][17][18][19]; however, hypo-and hyperglycemia are still a hazard for AP systems, and novel approaches are still required.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Rule-Based Algorithm to Tune Insulin-on-Board Constraints for a Hybrid Artificial Pancreas System

Bertachi

Biagi

Beneyto

et al. 2020

Journal of Healthcare Engineering

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The artificial pancreas (AP) is a system intended to control blood glucose levels through automated insulin infusion, reducing the burden of subjects with type 1 diabetes to manage their condition. To increase patients’ safety, some systems limit the allowed amount of insulin active in the body, known as insulin-on-board (IOB). The safety auxiliary feedback element (SAFE) layer has been designed previously to avoid overreaction of the controller and thus avoiding hypoglycemia. In this work, a new method, so-called “dynamic rule-based algorithm,” is presented in order to adjust the limits of IOB in real time. The algorithm is an extension of a previously designed method which aimed to adjust the limits of IOB for a meal with 60 grams of carbohydrates (CHO). The proposed method is intended to be applied on hybrid AP systems during 24 h operation. It has been designed by combining two different strategies to set IOB limits for different situations: (1) fasting periods and (2) postprandial periods, regardless of the size of the meal. The UVa/Padova simulator is considered to assess the performance of the method, considering challenging scenarios. In silico results showed that the method is able to reduce the time spent in hypoglycemic range, improving patients’ safety, which reveals the feasibility of the approach to be included in different control algorithms.

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“…Several compartmental models addressing these challenges are developed to integrate a pharmacokinetic model of insulin action, calculate the time course of plasma and active insulin, and describe the dependence of glucose dynamics on plasma insulin and glucose concentration levels with a pharmacodynamic glucose model. Some of the compartment models filter the administered insulin dose into a derived variable, such as plasma insulin concentration (PIC), to quantify the amount of insulin in the bloodstream, which can better characterize the prolonged diffusion, absorption, and utilization rates of the subcutaneously administered insulin [6,7].…”

Section: Introductionmentioning

confidence: 99%

Plasma-insulin-cognizant adaptive model predictive control for artificial pancreas systems

Hajizadeh

Rashid

Çınar

2019

Journal of Process Control

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An adaptive model predictive control (MPC) algorithm with dynamic adjustments of constraints and objective function weights based on estimates of the plasma insulin concentration (PIC) is proposed for artificial pancreas (AP) systems. A personalized compartment model that translates the infused insulin into estimates of PIC is integrated with a recursive subspace-based system identification to characterize the transient dynamics of glycemic measurements. The system identification approach is able to identify stable, reliable linear time-varying models from closedloop data. An MPC algorithm using the adaptive models is designed to compute the optimal exogenous insulin delivery for AP systems without requiring any manually-entered meal information. A dynamic safety constraint derived from the estimation of PIC is incorporated in the adaptive MPC to improve the efficacy of the AP and prevent insulin overdosing. Simulation case studies demonstrate the performance of the proposed adaptive MPC algorithm.

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Plasma Insulin Estimation in People with Type 1 Diabetes Mellitus

Cited by 46 publications

References 46 publications

Multi‐model sensor fault detection and data reconciliation: A case study with glucose concentration sensors for diabetes

Multi‐model sensor fault detection and data reconciliation: A case study with glucose concentration sensors for diabetes

Dynamic Rule-Based Algorithm to Tune Insulin-on-Board Constraints for a Hybrid Artificial Pancreas System

Plasma-insulin-cognizant adaptive model predictive control for artificial pancreas systems

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