Meal Disturbance Research Articles

Meal Disturbance Effect on Control of Blood Glucose Level for Critically-ill Patients using In-silico Works

This study was conducted to determine the effect of meal disturbance on blood glucose level of the critically ill patients and to simulate the control algorithm previously developed using in-silico works. The study is significant so as to reduce the mortality rate of critically ill patients who usually encounter hyperglycaemia or/and hypoglycaemia while in treatment. The meal intake is believed to affect the blood glucose regulation and causes the hyperglycaemia to occur. Critically ill patients receive their meal through parenteral and enteral nutrition. Furthermore, by using in-silico works, time consumed and resources needed for clinical evaluation of the patients can be reduced. Hovorka model was employed in which the simulation study was carried out using MATLAB on the virtual patient and it was being compared with actual patient in which the data were provided by Institut Jantung Negara (IJN). Based on the simulation, the disturbance on enteral glucose supplied had affected the blood glucose level of the patient; however, it remained unchanged for the parental glucose. To reduce the occurrence of hypoglycaemia and hyperglycaemia, the patient was injected with 30 g/hr and 10 g/hr of enteral glucose, respectively. In conclusion, the disturbance of meal received can be controlled through in-silico works.

Accelerated model predictive controller for artificial pancreas

This work consists of a contribution to the artificial pancreas (AP) development by introducing new techniques of control based on an acceleration of reference tracking by using a variable penalisation of the cost function instead of fixed and arbitrary penalisation, two new functions of the weighting factors are introduced in the formulation of the control algorithm. This method allows a rapid rejection of meal disturbance, a reduction of glucose peak and a complete avoidance of hypoglycaemia. The developed controller performances are evaluated in silico test which is equivalent to animal test using the UVa/Padova simulator.

Evaluation of an artificial pancreas in in silico patients with online-tuned internal model control

Abstract A fully-automated controller in the artificial pancreas (AP) system designed to regulate blood glucose concentration can give better lifestyle to a type 1 diabetic patient. This paper deals with evaluating the benefit of fully-automated online-tuned controller for the AP system over offline-tuned and semi-automated controller based on internal model control (IMC) strategy. The online-tuned controller is fully-automatic in the sense that it can automatically deal with intra- and inter-patient variabilities and compensate for unannounced meal disturbances without any prior knowledge of patient parameters, patient specific characteristics or patient specific input–output data. A data driven Volterra model of patients is used to design IMC algorithms. For online-tuned controller, the Volterra kernels of the model are computed online by recursive least squares algorithm. The IMC algorithms are evaluated using different scenarios in the UVA/Padova metabolic simulator for validation, comparison with a fully-automatic zone model predictive controller and robustness analysis. Unlike offline-tuned IMC and semi-automated IMC, the online-tuned IMC in the AP system performs satisfactorily for every patient condition without patients’ intervention. Experimental results show that the online-tuned IMC compensates unannounced meal disturbances with low frequency of hypoglycemic events and most importantly, with low insulin infusion even with variations in insulin sensitivity, in the presence of irregular amounts of meal disturbances at random times, and in the presence of very high noise levels in the sensors and actuators. Patients experience hypoglycemia 0.46%, 1.01% and 20% of the time using online-tuned, offline-tuned and semi-automated IMC respectively when the insulin sensitivity is increased by +20%.

Super twisting sliding mode control algorithm for developing artificial pancreas in type 1 diabetes patients

Diabetes mellitus is one of the most common disease in the world. Specifically, juvenile diabetes in which pancreas of patient does not produce insulin that results in high concentration of glucose in the blood plasma for longer periods of time. High concentration of the glucose can cause many complications and may lead to the death. For this purpose optimal control of the blood glucose concentration is required in type 1 diabetes patients. In this research work closed loop control algorithm is proposed based on the sliding mode control (SMC) for type 1 diabetes patient. Bergmans minimal model is presented for studying dynamical behaviour of the glucose and insulin inside human body. Control mechanism based on classical SMC is designed and compared with the super twisting control algorithm which is based on the higher order SMC. A common drawback of classical SMC is the high frequency chattering in control signal which is minimized by using super twisting SMC approach. Super twisting SMC controller is designed and subjected to external disturbances for verifying the required robustness of the closed loop. A comparative analysis of the classical SMC and super twisting SMC is given in results section. Meal disturbance to diabetes patient is also explicitly explained and is given in simulation results. Numerical simulation results are provided to show the superiority and the advantages of the proposed controller such as robustness to external disturbances, no chattering, less control effort and accurate output tracking, thus proposed controller can be considered as a suitable choice for designing automatic insulin pumps for diabetes patients.

Predictive Control of the Blood Glucose Level in Type I Diabetic Patient Using Delay Differential Equation Wang Model

Because of increasing risk of diabetes, the measurement along with control of blood sugar has been of great importance in recent decades. In type I diabetes, because of the lack of insulin secretion, the cells cannot absorb glucose leading to low level of glucose. To control blood glucose (BG), the insulin must be injected to the body. This paper proposes a method for BG level regulation in type I diabetes. The control strategy is based on nonlinear model predictive control. The aim of the proposed controller optimized with genetics algorithms is to measure BG level each time and predict it for the next time interval. This merit causes a less amount of control effort, which is the rate of insulin delivered to the patient body. Consequently, this method can decrease the risk of hypoglycemia, a lethal phenomenon in regulating BG level in diabetes caused by a low BG level. Two delay differential equation models, namely Wang model and Enhanced Wang model, are applied as controller model and plant, respectively. The simulation results exhibit an acceptable performance of the proposed controller in meal disturbance rejection and robustness against parameter changes. As a result, if the nutrition of the person decreases instantly, the hypoglycemia will not happen. Furthermore, comparing this method with other works, it was shown that the new method outperforms previous studies.

Performance Analysis of Fuzzy-PID Controller for Blood Glucose Regulation in Type-1 Diabetic Patients.

This paper presents Fuzzy-PID (FPID) control scheme for a blood glucose control of type 1 diabetic subjects. A new metaheuristic Cuckoo Search Algorithm (CSA) is utilized to optimize the gains of FPID controller. CSA provides fast convergence and is capable of handling global optimization of continuous nonlinear systems. The proposed controller is an amalgamation of fuzzy logic and optimization which may provide an efficient solution for complex problems like blood glucose control. The task is to maintain normal glucose levels in the shortest possible time with minimum insulin dose. The glucose control is achieved by tuning the PID (Proportional Integral Derivative) and FPID controller with the help of Genetic Algorithm and CSA for comparative analysis. The designed controllers are tested on Bergman minimal model to control the blood glucose level in the facets of parameter uncertainties, meal disturbances and sensor noise. The results reveal that the performance of CSA-FPID controller is superior as compared to other designed controllers.

Meal context and food preferences in cancer patients: results from a French self-report survey

PurposeThe present study examined patient self-reports of descriptions, experiences and consequences of meal disturbances and food preferences within a cultural context (i.e., French meal traditions) in various treated cancer patients along their disease trajectory.MethodsOver 800 questionnaires were sent to 20 cancer treatment centres in France. During a 9-month period, 255 questionnaires were received from five centres. Inclusion criteria included those French patients over 18 years of age, could read and understand French, had an Eastern Cooperative Oncology Group score between 0 and 2, experienced treatment-induced nutrition changes and/or had decreased oral intake. Dietetic staff assessed clinical characteristics while patients completed a 17-item questionnaire.ResultsThe majority of patients were diagnosed with breast, gastro-intestinal (GI) tract and head and neck cancers (62 %). Half of the patients (49 %) experienced weight loss >5 %. The main treatment-induced side effects were fatigue, nausea, dry mouth, hypersensitivity to odors and GI tract transit disorders. These discomforts affected eating and drinking in 83 % of patients, inducing appetite loss and selected food aversion. Food preference appeared heterogeneous. Food taste, odor and finally appearance stimulated appetite. Finally, dietary behaviors and satisfaction were driven by the extent to which food was enjoyed.ConclusionsDuring oncologic treatments, eating and drinking were affected in more than three-quarters of patients. As recommended by practice guidelines, nutritional assessment and follow-up are required. Personalized nutritional counseling should include the role of the family, patient’s meal traditions, and food habits.

Data driven nonparametric identification and model based control of glucose-insulin process in type 1 diabetics

Closed loop control of glucose homeostasis via subcutaneous insulin infusion and continuous glucose monitoring system can give better living to a type 1 diabetic patient. This paper deals with the real time implementation of internal model control (IMC) of subcutaneous insulin infusion. The model based control is applied on the nonparametric model of the patient identified in real time from input–output data. Meal simulation model of the glucose-insulin system of type 1 diabetic patient based on the work of Dalla Man et. al. is considered. This model is constructed in hardware platform that acts as the virtual patient. The data-driven nonparametric model of the virtual patient is identified in real time by computing Volterra kernels. The kernels are solved up to second order using recursive least squares (RLS) algorithm with short memory length of M=2. The validation results of the identified model output and the actual output have shown good fit in both simulation and real time environments. The frequency domain kernels are used in internal model control to generate insulin dosage. The control algorithm is developed in simulation and implemented in real time with hardware in loop on dSPACE platform. The closed loop system yields good meal disturbance rejection, less undershoots, settling time and more profoundly smaller requirement of insulin infusion as compared to the earlier reported data.

Design and Evaluation of a Robust PID Controller for a Fully Implantable Artificial Pancreas

Treatment of type 1 diabetes mellitus could be greatly improved by applying a closed-loop control strategy to insulin delivery, also known as an artificial pancreas (AP). In this work, we outline the design of a fully implantable AP using intraperitoneal (IP) insulin delivery and glucose sensing. The design process utilizes the rapid glucose sensing and insulin action offered by the IP space to tune a PID controller with insulin feedback to provide safe and effective insulin delivery. The controller was tuned to meet robust performance and stability specifications. An anti-reset windup strategy was introduced to prevent dangerous undershoot toward hypoglycemia after a large meal disturbance. The final controller design achieved 78% of time within the tight glycemic range of 80–140 mg/dL, with no time spent in hypoglycemia. The next step is to test this controller design in an animal model to evaluate the in vivo performance.

Model predictive control of blood sugar in patients with type‐1 diabetes

SummaryIn this article, two adaptive model predictive controllers (AMPC) are applied to regulate the blood glucose in type 1 diabetic patients. The first controller is constructed based on a linear model, while the second one is designed by using a nonlinear Hammerstein model. The adaptive version of these control schemes is considered to make them more robust against model mismatches and external disturbances. The least squares method with forgetting factor is used to update the model parameters. For simulation study, two well‐known mathematical models namely, Puckett and Hovorka which describe the dynamical behavior of patient's body have been selected. The performances and robustness of the proposed controllers are tested for regulating the blood glucose of diabetic patients in presences of model mismatches and measurement noises. Simulation results indicate that the non‐linear model predictive controller (NMPC) outperforms the linear one. To improve the performance of the NMPC in rejecting the meal disturbances, two different feedforward control strategies have been considered. Simulation results indicate that the combined adaptive NMPC with feedforward controller has a better performance over the other considered control schemes. Copyright © 2015 John Wiley & Sons, Ltd.

Static output feedback ℋ ∞ control for a fractional-order glucose-insulin system

This paper presents the $H_\infty$ static output feedback control of nonlinear fractional-order systems. Based on the extended bounded real lemma, the $H_\infty$ control is formulated and sufficient conditions are derived in terms of linear matrix inequalities (LMIs) formulation by using the fractional Lyapunov direct method where the fractional-order α belongs to 0 < $\alpha$ < 1. The control approach is finally applied to the regulation of the glucose level in diabetes type 1 treatment. Therefore, it is attempted to incorporate fractional-order into the mathematical minimal model of glucose-insulin system dynamics and it is still an interesting challenge to show, how the order of a fractional differential system affects the dynamics of the system in the presence of meal disturbance. Numerical simulations are carried out to illustrate our proposed results and show that the nonlinear fractional-order glucose-insulin systems are, at least, as stable as their integer-order counterpart in the presence of exogenous glucose infusion or meal disturbance.

&lt;italic&gt;In Silico&lt;/italic&gt; Closed-Loop Control Validation Studies for Optimal Insulin Delivery in Type 1 Diabetes

This study presents a general closed-loop control strategy for optimal insulin delivery in type 1 Diabetes Mellitus (T1DM). The proposed control strategy aims toward an individualized optimal insulin delivery that consists of a patient-specific model predictive controller, a state estimator, a personalized scheduling level, and an open-loop optimization problem subjected to patient-specific process model and constraints. This control strategy can be also modified to address the case of limited patient data availability resulting in an "approximation" control strategy. Both strategies are validated in silico in the presence of predefined and unknown meal disturbances using both a novel mathematical model of glucose-insulin interactions and the UVa/Padova Simulator model as a virtual patient. The robustness of the control performance is evaluated under several conditions such as skipped meals, variability in the meal time, and metabolic uncertainty. The simulation results of the closed-loop validation studies indicate that the proposed control strategies can potentially achieve improved glycaemic control.

Regulation of Blood Glucose Concentration in Type 1 Diabetics Using Single Order Sliding Mode Control Combined with Fuzzy On-line Tunable Gain, a Simulation Study.

Diabetes is considered as a global affecting disease with an increasing contribution to both mortality rate and cost damage in the society. Therefore, tight control of blood glucose levels has gained significant attention over the decades. This paper proposes a method for blood glucose level regulation in type 1 diabetics. The control strategy is based on combining the fuzzy logic theory and single order sliding mode control (SOSMC) to improve the properties of sliding mode control method and to alleviate its drawbacks. The aim of the proposed controller that is called SOSMC combined with fuzzy on-line tunable gain is to tune the gain of the controller adaptively. This merit causes a less amount of control effort, which is the rate of insulin delivered to the patient body. As a result, this method can decline the risk of hypoglycemia, a lethal phenomenon in regulating blood glucose level in diabetics caused by a low blood glucose level. Moreover, it attenuates the chattering observed in SOSMC significantly. It is worth noting that in this approach, a mathematical model called minimal model is applied instead of the intravenously infused insulin-blood glucose dynamics. The simulation results demonstrate a good performance of the proposed controller in meal disturbance rejection and robustness against parameter changes. In addition, this method is compared to fuzzy high-order sliding mode control (FHOSMC) and the superiority of the new method compared to FHOSMC is shown in the results.

Design of an Artificial Pancreas using Zone Model Predictive Control with a Moving Horizon State Estimator.

A zone model predictive control (zone-MPC) algorithm that utilizes the Moving Horizon State Estimator (MHSE) is presented. The control application is an artificial pancreas for treating people with type 1 diabetes mellitus. During the meal challenge, the prediction quality of the zone-MPC algorithm with the MHSE was significantly better than when using the current Luenberger observer to provide the state estimate. Consequently, the controller using the MHSE rejected the meal disturbance faster and without inducing extra hypoglycemia risk (e.g., lower postprandial blood glucose peak by 10 mg/dL and higher postprandial minimum blood glucose by 11 mg/dL). The faster rejection of the meal disturbance led to a longer time in the clinically accepted safe region (70-180 mg/dL) by 13%, and this may reduce the likelihood of the complications related to type 1 diabetes mellitus.

Multi-parametric model predictive control-based regulation of blood glucose in type 1 diabetics under unmeasured meal disturbances: a simulation study

Closed-loop control of Blood Glucose Concentration (BGC) is performed using a model differencing-based multi-parametric model predictive controller. An empirical model derived from a virtual nominal patient is used in the design of the controller. A time varying hard constraint is imposed on the control action based on insulin onboard criterion. By varying the model parameters used in the Sorensen’s patient model, 25 virtual patients are created. Varying meal disturbances over a period of five days, the Mean Square Error (MSE) of BGC’s deviation from reference (100 mg/dL) is used to tune the controller. In-silico trials with simulated sensor noise (± 20 mg/dL range) show that the multi-parametric model predictive controller performs well under model uncertainties and also regulates the BGC within the safe limits (above 60 mg/dL) during multiple meal disturbances.

A New Controlling Approach of Type 1 Diabetics Based on Interval Type-2 Fuzzy Controller

Augmented Minimal Model which is developed in consideration of the patient is taken into attention and uncertainties in this model which can occur by factors such as blood glucose, daily meals or sudden stress in considered. In addition to eliminate the effects of uncertainty, different control methods may be utilized. In this article, fuzzy control as a logical tool is used to transform words into control actions. To enhance the system performance, an interval type-2 Fuzzy controller has been implemented. To date, because of computational complexity of using a general type-2 fuzzy set (T2 FS) in a T2 fuzzy logic system (FLS), most people only use an interval T2 FS, the result being an interval T2 FLS (IT2 FLS). A daily meal disturbance is injected to model to consider the real environment for simulation. Finally, the control method tuned by standard tuning procedure and simulation results show the efficiency of method in regulating the blood glucose level in presentment of daily meal disturbance.

A constrained sub-optimal controller for glucose regulation in type 1 diabetes mellitus

SUMMARY Patients with type 1 diabetes mellitus require exogenous insulin infusion to avoid chronic complications related to elevated glucose levels. With diabetes reaching epidemic proportions, recent times have witnessed an increased attention in the field of optimal glucose management by closed-loop insulin delivery system. A proper glucose management scheme, in addition of maintaining the glucose level within the normal range of 80–120 mg/dL, should avoid excessive insulin delivery leading to hypoglycemia. By considering the glucose regulation as a linear quadratic problem, a constrained novel sub-optimal controller is proposed in the present work, for the maintenance of normal glucose level in type 1 diabetic subjects. The observer free state feedback controller is based on the feedback of only physiological variables (plasma glucose and plasma insulin). Constraining the feedback elements corresponding to the non-physiological variables avoids the use of an observer while maintaining the advantages of state feedback control. The implementation of the proposed scheme requires simple measurement protocol with no online computation. The closed-loop performance of the controller is evaluated on a physiologically relevant model for a meal disturbance and continuous glucose infusion. Copyright © 2013 John Wiley & Sons, Ltd.

H∞ Static Output Feedback Control for a Fractional-Order Glucose-Insulin System

This paper presents the H∞ static output feedback control of nonlinear fractional-order glucose-insulin systems. In this paper, it is an attempt to incorporate fractional-order into the mathematical minimal model of glucose-insulin system dynamics and it is still an interesting challenge to show, how the order of a fractional differential system affects the dynamics of system in the presence of meal disturbance. A static output feedback control is considered for the problem. Sufficient conditions are derived in terms of linear matrix inequalities (LMIs) formulation by using the fractional Lyapunov direct method where the fractional-order a belongs to 0 < α < 1. Finally, numerical simulations are carried out to illustrate our proposed results and show that the nonlinear fractional-order glucose-insulin systems are as stable as their integer order counterpart in the presence of exogenous glucose infusion or meal disturbance.

Blood glucose concentration regulation in Type 1 diabetics using multi model multi parametric model predictive control: An empirical approach

A Glucose- Insulin steady state static map is obtained from the Hovorka's 8th order virtual patient model. Three First Order Plus Time Delay (FOPTD) models are derived for the three piecewise linear regions in it. Through polyhedral vector space partitioning based on constraint violation, critical regions in state vector space are identified. A state feedback gain based controller is designed for each critical region. The controller design prevents constraint violations and ensures convexity while regulating the state vector to origin. The solution is also globally minimal. The state vector space of each empirical model is subjected to such analysis, resulting in three different set of critical regions and corresponding controllers. Gain Scheduling (GS) based on the Blood Glucose Concentration (BGC) measurement ensured proper profile selection. Through delay time compensation techniques, the multi model multi-parametric Model Predictive Control (mp-MPC) is designed for pure dynamics of each linear region. It is observed that the gain scheduled controller regulates the BGC within the acceptable range (80mg/dL to 160 mg/dL) during multiple meal disturbances. The explicit state feedback gain nature of the controller implies ease of deployment on memory constrained embedded devices.

Swarm optimization tuned Mamdani fuzzy controller for diabetes delayed model

Diabetes is a chronic disease in which there are high levels of sugar in the blood. Insulin is a hormone that regulates the blood glucose level in the body. Diabetes mellitus can be caused by too little insulin, a resistance to insulin, or both. Although research activities on controlling blood glucose have been attempted to lower the blood glucose level in the quickest possible time, there are some shortages in the amount of the insulin injection. In this paper, a complete model of the glucose--insulin regulation system, which is a nonlinear delay differential model, is used. The purpose of this paper is to follow the glucose profiles of a healthy person with minimum infused insulin. To achieve these purposes, an intelligent fuzzy controller based on a Mamdani-type structure, namely the swarm optimization tuned Mamdani fuzzy controller, is proposed for type 1 diabetic patients. The proposed fuzzy controller is optimized by a novel heuristic algorithm, namely linearly decreasing weight particle swarm optimization. To verify the robust performance of the proposed controller, a group of 4 tests is applied. Insensitivity to multiple meal disturbances, high accuracy, and superior robustness to model the parameter uncertainties are the key aspects of the proposed method. The simulation results illustrate the superiority of the proposed controller.