1
|
Yao Y, Kothare MV. Nonlinear Closed-Loop Predictive Control of Heart Rate and Blood Pressure Using Vagus Nerve Stimulation: An In Silico Study. IEEE Trans Biomed Eng 2023; 70:2764-2775. [PMID: 37656644 PMCID: PMC11058472 DOI: 10.1109/tbme.2023.3261744] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/03/2023]
Abstract
We propose a nonlinear model-based control technique for regulating the heart rate and blood pressure using vagus nerve neuromodulation. The closed-loop framework is based on an in silico model of the rat cardiovascular system for the simulation of the hemodynamic response to multi-location vagal nerve stimulation. The in silico model is derived by compartmentalizing the various physiological components involved in the closed-loop cardiovascular system with intrinsic baroreflex regulation to virtually generate nominal and hypertension-related heart dynamics of rats in rest and exercise states. The controller, using a reduced cycle-averaged model, monitors the outputs from the in silico model, estimates the current state of the reduced model, and computes the optimum stimulation locations and the corresponding parameters using a nonlinear model predictive control algorithm. The results demonstrate that the proposed control strategy is robust with respect to its ability to handle setpoint tracking and disturbance rejection in different simulation scenarios.
Collapse
|
2
|
Yan S, Chu LL, Cai Y. Robust H∞ control of T–S fuzzy blood glucose regulation system via adaptive event-triggered scheme. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
|
3
|
Farahmand B, Dehghani M, Vafamand N, Mirzaee A, Boostani R, Pieper JK. Robust nonlinear control of blood glucose in diabetic patients subject to model uncertainties. ISA TRANSACTIONS 2023; 133:353-368. [PMID: 35927074 DOI: 10.1016/j.isatra.2022.07.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/23/2021] [Revised: 07/07/2022] [Accepted: 07/07/2022] [Indexed: 06/15/2023]
Abstract
Recent advances in the artificial pancreas system provide an emerging treatment option for type 1 diabetes. The performance of the blood glucose regulation directly relies on the accuracy of the glucose-insulin modeling. Sorenson model involves the behavior of different organs and offers precise representation. However, the high complexity of such a model makes the controller design procedure a hard task. Therefore, the high-order nonlinear Sorensen model as a popular high-fidelity physiological model is opted in this paper to analyze the glucose-insulin interactions in great detail, and a new robust nonlinear approach to regulate the blood glucose concentration (BGC) in Type-I diabetic patients is proposed. Inspiring the backstepping technique, for designing an acceptable controller, the model is divided into three main subsystems such that in each subsystem, the virtual control input laws are obtained using both Lyapunov stability and input-to-state theorems. Since the measurement of the parameters in the glucose-insulin system is not accurate, parametric uncertainties are defined in the investigated model. Furthermore, owing to the fact that the only measurable state variable is blood glucose, the estimation of inaccessible state variables is an important issue that is properly considered by the unscented Kalman filter (UKF) estimator. The suggested approach is compared to H∞, robust H∞, and linear parameter-varying control approaches. The comparison results on 500 simulated patients imply a remarkable superiority of the proposed controller approach to the compared methods in terms of the BGC tracking and the algorithm robustness in the presence of food intake disturbance patterns.
Collapse
Affiliation(s)
- Bahareh Farahmand
- School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran; Schulich School of Engineering, University of Calgary, Calgary, Alberta, Canada
| | - Maryam Dehghani
- School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran.
| | - Navid Vafamand
- School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran
| | - Alireza Mirzaee
- School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran
| | - Reza Boostani
- School of Electrical and Computer Engineering, Shiraz University, Shiraz, Iran
| | - Jeffrey Kurt Pieper
- Schulich School of Engineering, University of Calgary, Calgary, Alberta, Canada
| |
Collapse
|
4
|
Sharma A, Singh HP, Nilam. A methodical survey of mathematical model-based control techniques based on open and closed loop control approach for diabetes management. INT J BIOMATH 2022. [DOI: 10.1142/s1793524522500516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Disturbance of blood sugar level is controlled through well-known biomechanical feedback loops: high levels of glucose in blood facilitate to release insulin from the pancreas which accelerates the absorption rate of cellular glucose. Low glucose levels encourage to release pancreatic glucagon which induces glycogen breakdown to glucose in the liver. These bio-control systems do not function properly in diabetic patients. Though the control of disease seems intuitively easy, in real life, due to many differences in structure by diet and fasting, exercise, medications, patient’s profile and other stressors, it is not that easy. The mathematical models of the glucose-insulin regulatory system follow the patient’s physiological conditions which make it difficult to identify and estimate all the model parameters. In this paper, we have given a systematic literature review on mathematical models of the diabetic patients, and various kinds of disease control techniques through the development of open and closed loop insulin deliver command system and optimization of exogenous insulin rate. It demonstrates the open and closed loop type model-based control strategies underlying the assumptions of the concerned models. The combination of mathematical model with control strategies such as genetic algorithm (GA), neural network (NN), sliding mode controller (SMC), model predictive controller (MPC), and fuzzy logic control (FLC) has been considered, which provides an overview of this area, highlighting the control profile over the diabetic model with promising clinical results, outlining key challenges, and identifying needs for the future research. Also, the significance of these control algorithms has been discussed in the presence of the noises, the controller’s robustness and various other disturbances. It provides substantial information on diabetes management through various control techniques.
Collapse
Affiliation(s)
- Ankit Sharma
- Department of Applied Mathematics, Delhi Technological University, Delhi 110042, India
| | | | - Nilam
- Department of Applied Mathematics, Delhi Technological University, Delhi 110042, India
| |
Collapse
|
5
|
Martinez F, Rodriguez E, Vernon-Carter E, Alvarez-Ramirez J. A simple two-compartment model for analysis of feedback control of glucose regulation. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
|
6
|
Polisano F, Ryals AD, Pannocchia G, Landi A. MPC based optimization applied to treatment of HCV infections. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2021; 210:106383. [PMID: 34496321 DOI: 10.1016/j.cmpb.2021.106383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2021] [Accepted: 08/24/2021] [Indexed: 06/13/2023]
Abstract
BACKGROUND AND OBJECTIVE The recent introduction of antivirals for the treatment of the hepatitis C virus opens new frontiers but also poses a significant burden on public health systems. This paper presents a simulation study in which model predictive control (MPC) is proposed for optimizing the therapy aiming to obtain a reduction of the costs of therapy, while maintaining the best pharmacological control of the infection. METHODS A dynamic model describing the evolution of hepatitis C is deployed as internal model for MPC implementation, using nominal values of parameters. Different closed-loop simulations are presented both in nominal and in mismatch conditions. In addition, a more easily implementable treatment is proposed, which is based on a discrete dosage approach, where days on/off therapy are considered instead of continuous therapy modulation. RESULTS Results show that therapy modulation allows one to achieve the same infection evolution as with full therapy, with a reduction of drug consumption between 10% and 40%. The alternative discrete dosage approach shows similar results achieved with therapy modulation, both in terms of therapy effectiveness and drug consumption reduction. CONCLUSIONS The proposed model predictive control therapy optimization strategies appear to be effective, implementable and robust to model errors. It therefore represents a potentially useful approach to alleviate the burden of HCV therapy cost on national health systems.
Collapse
Affiliation(s)
- Fabio Polisano
- Department of Information Engineering, University of Pisa, Italy
| | - Andrea Dan Ryals
- Department of Information Engineering, University of Pisa, Italy
| | | | - Alberto Landi
- Department of Information Engineering, University of Pisa, Italy
| |
Collapse
|
7
|
Farahmand B, Dehghani M, Vafamand N. Fuzzy model-based controller for blood glucose control in type 1 diabetes: An LMI approach. Biomed Signal Process Control 2019. [DOI: 10.1016/j.bspc.2019.101627] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
|
8
|
De Falco I, Cioppa AD, Giugliano A, Marcelli A, Koutny T, Krcma M, Scafuri U, Tarantino E. A genetic programming-based regression for extrapolating a blood glucose-dynamics model from interstitial glucose measurements and their first derivatives. Appl Soft Comput 2019. [DOI: 10.1016/j.asoc.2019.01.020] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
|
9
|
Bhattacharjee A, Easwaran A, Leow MKS, Cho N. Evaluation of an artificial pancreas in in silico patients with online-tuned internal model control. Biomed Signal Process Control 2018. [DOI: 10.1016/j.bspc.2017.12.002] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
|
10
|
Embedded Control in Wearable Medical Devices: Application to the Artificial Pancreas. Processes (Basel) 2016. [DOI: 10.3390/pr4040035] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
|
11
|
Zarkogianni K, Litsa E, Mitsis K, Wu PY, Kaddi CD, Cheng CW, Wang MD, Nikita KS. A Review of Emerging Technologies for the Management of Diabetes Mellitus. IEEE Trans Biomed Eng 2015; 62:2735-49. [PMID: 26292334 PMCID: PMC5859570 DOI: 10.1109/tbme.2015.2470521] [Citation(s) in RCA: 33] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
OBJECTIVE High prevalence of diabetes mellitus (DM) along with the poor health outcomes and the escalated costs of treatment and care poses the need to focus on prevention, early detection and improved management of the disease. The aim of this paper is to present and discuss the latest accomplishments in sensors for glucose and lifestyle monitoring along with clinical decision support systems (CDSSs) facilitating self-disease management and supporting healthcare professionals in decision making. METHODS A critical literature review analysis is conducted focusing on advances in: 1) sensors for physiological and lifestyle monitoring, 2) models and molecular biomarkers for predicting the onset and assessing the progress of DM, and 3) modeling and control methods for regulating glucose levels. RESULTS Glucose and lifestyle sensing technologies are continuously evolving with current research focusing on the development of noninvasive sensors for accurate glucose monitoring. A wide range of modeling, classification, clustering, and control approaches have been deployed for the development of the CDSS for diabetes management. Sophisticated multiscale, multilevel modeling frameworks taking into account information from behavioral down to molecular level are necessary to reveal correlations and patterns indicating the onset and evolution of DM. CONCLUSION Integration of data originating from sensor-based systems and electronic health records combined with smart data analytics methods and powerful user centered approaches enable the shift toward preventive, predictive, personalized, and participatory diabetes care. SIGNIFICANCE The potential of sensing and predictive modeling approaches toward improving diabetes management is highlighted and related challenges are identified.
Collapse
Affiliation(s)
| | | | | | | | | | | | - May D. Wang
- Contact information for the corresponding author: , Phone: 404-385-2954, Fax: 404-894-4243, Address: Suite 4106, UA Whitaker Building, 313 Ferst Drive, Atlanta, GA 30332, USA
| | | |
Collapse
|
12
|
PAROC—An integrated framework and software platform for the optimisation and advanced model-based control of process systems. Chem Eng Sci 2015. [DOI: 10.1016/j.ces.2015.02.030] [Citation(s) in RCA: 108] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
|
13
|
De Paula M, Ávila LO, Martínez EC. Controlling blood glucose variability under uncertainty using reinforcement learning and Gaussian processes. Appl Soft Comput 2015. [DOI: 10.1016/j.asoc.2015.06.041] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
|
14
|
Abstract
For implantable sensors to become a more viable option for continuous glucose monitoring strategies, they must be able to persist in vivo for periods longer than the 3- to 7-day window that is the current industry standard. Recent studies have attributed such limited performance to tissue reactions resulting from implantation. While in vivo biocompatibility studies have provided much in the way of understanding histology surrounding an implanted sensor, little is known about how each constituent of the foreign body response affects sensor function. Due to the ordered composition and geometry of implant-associated tissue reactions, their effects on sensor function may be computationally modeled and analyzed in a way that would be prohibitive using in vivo studies. This review both explains how physiologically accurate computational models of implant-associated tissue reaction can be designed and shows how they have been utilized thus far. Going forward, these in silico models of implanted sensor behavior may soon complement in vivo studies to provide valuable information for improved sensor designs.
Collapse
Affiliation(s)
- Matthew T Novak
- Department of Biomedical Engineering, Duke University, Durham, NC, USA
| | | |
Collapse
|
15
|
Zavitsanou S, Mantalaris A, Georgiadis MC, Pistikopoulos EN. In Silico Closed-Loop Control Validation Studies for Optimal Insulin Delivery in Type 1 Diabetes. IEEE Trans Biomed Eng 2015; 62:2369-78. [PMID: 25935026 DOI: 10.1109/tbme.2015.2427991] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
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.
Collapse
|
16
|
A nonparametric approach for model individualization in an artificial pancreas∗∗This work was supported by ICT FP7-247138 Bringing the Artificial Pancreas at Home. (AP@home) project and the Fondo per gli Investimenti della Ricerca di Base project Artificial Pancreas:In Silico Development and In Vivo Validation of Algorithms forBlood Glucose Control funded by Italian Ministero dell'Istruzione,dell'Universit_a e della Ricerca. ACTA ACUST UNITED AC 2015. [DOI: 10.1016/j.ifacol.2015.10.143] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
|
17
|
Artificial Pancreas: from in-silico to in-vivo∗∗This work was supported by the Fondo per gli Investimenti della Ricerca di Base project Artificial Pancreas: In Silico Development and In Vivo Validation of Algorithms for Blood Glucose Control funded by Italian Ministero dell'Istruzione, dell'Universitä e della Ricerca. ACTA ACUST UNITED AC 2015. [DOI: 10.1016/j.ifacol.2015.09.148] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
|
18
|
Effect of concurrent oxygen therapy on accuracy of forecasting imminent postoperative desaturation. J Clin Monit Comput 2014; 29:521-31. [PMID: 25326787 DOI: 10.1007/s10877-014-9629-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2014] [Accepted: 10/12/2014] [Indexed: 10/24/2022]
Abstract
Episodic postoperative desaturation occurs predominantly from respiratory depression or airway obstruction. Monitor display of desaturation is typically delayed by over 30 s after these dynamic inciting events, due to perfusion delays, signal capture and averaging. Prediction of imminent critical desaturation could aid development of dynamic high-fidelity response systems that reduce or prevent the inciting event from occurring. Oxygen therapy is known to influence the depth and duration of desaturation epochs, thereby potentially influencing the accuracy of forecasting of desaturation. In this study, postoperative pulse oximetry data were retrospectively modeled using autoregressive methods to create prediction models for [Formula: see text] and imminent critical desaturation in the postoperative period. The accuracy of these models in predicting near future [Formula: see text] values was tested using root mean square error. The model accuracy for prediction of critical desaturation ([Formula: see text] [Formula: see text]) was evaluated using meta-analytical methods (sensitivity, specificity, likelihood ratios, diagnostic odds ratios and area under summary receiver operating characteristic curves). Between-study heterogeneity was used as a measure of reliability of the model across different patients and evaluated using the tau-squared statistic. Model performance was evaluated in [Formula: see text] patients who received postoperative oxygen supplementation and [Formula: see text] patients who did not receive oxygen. Our results show that model accuracy was high with root mean square errors between 0.2 and 2.8%. Prediction accuracy as defined by area under the curve for critical desaturation events was observed to be greater in patients receiving oxygen in the 60-s horizon ([Formula: see text] vs. [Formula: see text]). This was likely related to the higher frequency of events in this group (median [IQR] [Formula: see text] [Formula: see text]) than patients who were not treated with oxygen ([Formula: see text] [Formula: see text]; [Formula: see text]). Model reliability was reflected by the homogeneity of the prediction models which were homogenous across both prediction horizons and oxygen treatment groups. In conclusion, we report the use of autoregressive models to predict [Formula: see text] and forecast imminent critical desaturation events in the postoperative period with high degree of accuracy. These models reliably predict critical desaturation in patients receiving supplemental oxygen therapy. While high-fidelity prophylactic interventions that could modify these inciting events are in development, our current study offers proof of concept that the afferent limb of such a system can be modeled with a high degree of accuracy.
Collapse
|
19
|
Sopasakis P, Patrinos P, Sarimveis H. Robust model predictive control for optimal continuous drug administration. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2014; 116:193-204. [PMID: 24986530 DOI: 10.1016/j.cmpb.2014.06.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2013] [Revised: 06/05/2014] [Accepted: 06/06/2014] [Indexed: 06/03/2023]
Abstract
In this paper the model predictive control (MPC) technology is used for tackling the optimal drug administration problem. The important advantage of MPC compared to other control technologies is that it explicitly takes into account the constraints of the system. In particular, for drug treatments of living organisms, MPC can guarantee satisfaction of the minimum toxic concentration (MTC) constraints. A whole-body physiologically-based pharmacokinetic (PBPK) model serves as the dynamic prediction model of the system after it is formulated as a discrete-time state-space model. Only plasma measurements are assumed to be measured on-line. The rest of the states (drug concentrations in other organs and tissues) are estimated in real time by designing an artificial observer. The complete system (observer and MPC controller) is able to drive the drug concentration to the desired levels at the organs of interest, while satisfying the imposed constraints, even in the presence of modelling errors, disturbances and noise. A case study on a PBPK model with 7 compartments, constraints on 5 tissues and a variable drug concentration set-point illustrates the efficiency of the methodology in drug dosing control applications. The proposed methodology is also tested in an uncertain setting and proves successful in presence of modelling errors and inaccurate measurements.
Collapse
Affiliation(s)
- Pantelis Sopasakis
- School of Chemical Engineering, National Technical University of Athens, 9 Heroon Polytechneiou Street, Zografou Campus, 15780 Athens, Greece; IMT Institute for Advanced Studies Lucca, Piazza San Ponziano 6, 55100 Lucca, Italy
| | - Panagiotis Patrinos
- IMT Institute for Advanced Studies Lucca, Piazza San Ponziano 6, 55100 Lucca, Italy
| | - Haralambos Sarimveis
- School of Chemical Engineering, National Technical University of Athens, 9 Heroon Polytechneiou Street, Zografou Campus, 15780 Athens, Greece.
| |
Collapse
|
20
|
Palumbo P, Pizzichelli G, Panunzi S, Pepe P, De Gaetano A. Model-based control of plasma glycemia: Tests on populations of virtual patients. Math Biosci 2014; 257:2-10. [PMID: 25223234 DOI: 10.1016/j.mbs.2014.09.003] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2014] [Revised: 06/26/2014] [Accepted: 09/01/2014] [Indexed: 11/27/2022]
Abstract
Closed-loop devices delivering medical treatments in an automatic fashion clearly require a thorough preliminary phase according to which the proposed control law is tested and validated as realistically as possible, before arranging in vivo experiments in a clinical setting. The present note develops a virtual environment aiming to validate a recently proposed model-based glucose control law on a solid simulation framework. From a theoretical viewpoint, the artificial pancreas has been designed by suitably exploiting a minimal set of delay differential equations modeling the glucose-insulin regulatory system; on the other hand, the validation platform makes use of a different, multi-compartmental model to build up a population of virtual patients. Simulations are carried out by properly addressing the available technological limits and the unavoidable uncertainties in real-time continuous glucose sensors as well as possible malfunctioning on the insulin delivery devices. The results show the robustness of the proposed control law that turns out to be efficient and extremely safe on a heterogenous population of virtual patients.
Collapse
Affiliation(s)
- P Palumbo
- Istituto di Analisi dei Sistemi ed Informatica "A. Ruberti", Consiglio Nazionale delle Ricerche (IASI-CNR), BioMatLab - UCSC - Largo A. Gemelli 8, 00168 Roma, Italy.
| | - G Pizzichelli
- Istituto Italiano di Tecnologia, Center for Micro-BioRobotics@SSSA, Viale R. Piaggio 34, 56025 Pontedera, Italy; Scuola Superiore Sant'Anna, The BioRobotics Institute, Viale R. Piaggio 34, 56025 Pontedera, Italy
| | - S Panunzi
- Istituto di Analisi dei Sistemi ed Informatica "A. Ruberti", Consiglio Nazionale delle Ricerche (IASI-CNR), BioMatLab - UCSC - Largo A. Gemelli 8, 00168 Roma, Italy
| | - P Pepe
- Università degli Studi dellAquila, 67040 Poggio di Roio, L'Aquila, Italy
| | - A De Gaetano
- Istituto di Analisi dei Sistemi ed Informatica "A. Ruberti", Consiglio Nazionale delle Ricerche (IASI-CNR), BioMatLab - UCSC - Largo A. Gemelli 8, 00168 Roma, Italy
| |
Collapse
|
21
|
Kirubakaran V, Radhakrishnan TK, Sivakumaran N. Metaheuristic Patient Estimation Based Patient-Specific Fuzzy Aggregated Artificial Pancreas Design. Ind Eng Chem Res 2014. [DOI: 10.1021/ie5009647] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- V. Kirubakaran
- Department of Chemical Engineering and ‡Department of Instrumentation and
Control Engineering, National Institute of Technology, Tiruchirappalli 620015, India
| | - T. K. Radhakrishnan
- Department of Chemical Engineering and ‡Department of Instrumentation and
Control Engineering, National Institute of Technology, Tiruchirappalli 620015, India
| | - N. Sivakumaran
- Department of Chemical Engineering and ‡Department of Instrumentation and
Control Engineering, National Institute of Technology, Tiruchirappalli 620015, India
| |
Collapse
|
22
|
Wang Y, Xie H, Jiang X, Liu B. Intelligent closed-loop insulin delivery systems for ICU patients. IEEE J Biomed Health Inform 2014; 18:290-9. [PMID: 24403427 DOI: 10.1109/jbhi.2013.2269699] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Good glycemic control through insulin administration among intensive care unit (ICU) patients can reduce mortality significantly; however, it remains a big challenge because of scarcity of individualized models for ICU patients. To deal with this challenge, a new combination of particle swarm optimization (PSO) and model predictive control (MPC) has been proposed to identify the model online as well as to optimally design the input, i.e., the insulin delivery rate automatically. According to the population distribution, ten typical linear dynamic models were selected such that any patient's model could be approximated by a linear combination of these ten typical models. PSO was used to update the weight coefficients while MPC was used to design the insulin delivery rate based on the combination model identified by using PSO. The proposed strategy was compared with the Yale protocol on 30 virtual subjects. According to the control-variability grid analysis, the percentage values in A + B zone were, respectively, 100% under the proposed strategy and while 51% under the Yale protocol, which demonstrates the superior performance of the proposed strategy. As a good candidate for the full closed-loop insulin delivery method, this new combination can control the glucose level by bringing it to a safe range promptly thereby reducing the risk of death.
Collapse
|
23
|
Jacobs PG, El Youssef J, Castle J, Bakhtiani P, Branigan D, Breen M, Bauer D, Preiser N, Leonard G, Stonex T, Ward WK. Automated control of an adaptive bihormonal, dual-sensor artificial pancreas and evaluation during inpatient studies. IEEE Trans Biomed Eng 2014; 61:2569-81. [PMID: 24835122 DOI: 10.1109/tbme.2014.2323248] [Citation(s) in RCA: 64] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Automated control of blood glucose in patients with type-1 diabetes has not yet been fully implemented. The aim of this study was to design and clinically evaluate a system that integrates a control algorithm with off-the-shelf subcutaneous sensors and pumps to automate the delivery of the hormones glucagon and insulin in response to continuous glucose sensor measurements. The automated component of the system runs an adaptive proportional derivative control algorithm which determines hormone delivery rates based on the sensed glucose measurements and the meal announcements by the patient. We provide details about the system design and the control algorithm, which incorporates both a fading memory proportional derivative controller (FMPD) and an adaptive system for estimating changing sensitivity to insulin based on a glucoregulatory model of insulin action. For an inpatient study carried out in eight subjects using Dexcom SEVEN PLUS sensors, prestudy HbA1c averaged 7.6, which translates to an estimated average glucose of 171 mg/dL. In contrast, during use of the automated system, after initial stabilization, glucose averaged 145 mg/dL and subjects were kept within the euglycemic range (between 70 and 180 mg/dL) for 73.1% of the time, indicating improved glycemic control. A further study on five additional subjects in which we used a newer and more reliable glucose sensor (Dexcom G4 PLATINUM) and made improvements to the insulin and glucagon pump communication system resulted in elimination of hypoglycemic events. For this G4 study, the system was able to maintain subjects' glucose levels within the near-euglycemic range for 71.6% of the study duration and the mean venous glucose level was 151 mg/dL.
Collapse
|
24
|
Elmoaqet H, Tilbury DM, Ramachandran SK. Evaluating predictions of critical oxygen desaturation events. Physiol Meas 2014; 35:639-55. [PMID: 24621948 DOI: 10.1088/0967-3334/35/4/639] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
This paper presents a new approach for evaluating predictions of oxygen saturation levels in blood ( SpO2). A performance metric based on a threshold is proposed to evaluate SpO2 predictions based on whether or not they are able to capture critical desaturations in the SpO2 time series of patients. We use linear auto-regressive models built using historical SpO2 data to predict critical desaturation events with the proposed metric. In 20 s prediction intervals, 88%-94% of the critical events were captured with positive predictive values (PPVs) between 90% and 99%. Increasing the prediction horizon to 60 s, 46%-71% of the critical events were detected with PPVs between 81% and 97%. In both prediction horizons, more than 97% of the non-critical events were correctly classified. The overall classification capabilities for the developed predictive models were also investigated. The area under ROC curves for 60 s predictions from the developed models are between 0.86 and 0.98. Furthermore, we investigate the effect of including pulse rate (PR) dynamics in the models and predictions. We show no improvement in the percentage of the predicted critical desaturations if PR dynamics are incorporated into the SpO2 predictive models (p-value = 0.814). We also show that including the PR dynamics does not improve the earliest time at which critical SpO2 levels are predicted (p-value = 0.986). Our results indicate oxygen in blood is an effective input to the PR rather than vice versa. We demonstrate that the combination of predictive models with frequent pulse oximetry measurements can be used as a warning of critical oxygen desaturations that may have adverse effects on the health of patients.
Collapse
Affiliation(s)
- Hisham Elmoaqet
- Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA
| | | | | |
Collapse
|
25
|
Zhao C, Dassau E, Zisser HC, Jovanovič L, Doyle FJ, Seborg DE. Online prediction of subcutaneous glucose concentration for type 1 diabetes using empirical models and frequency-band separation. AIChE J 2013. [DOI: 10.1002/aic.14288] [Citation(s) in RCA: 21] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Affiliation(s)
- Chunhui Zhao
- Dept. of Chemical Engineering; University of California; Santa Barbara CA 93106
- Sansum Diabetes Research Institute; Santa Barbara CA 93105
| | - Eyal Dassau
- Dept. of Chemical Engineering; University of California; Santa Barbara CA 93106
- Sansum Diabetes Research Institute; Santa Barbara CA 93105
| | - Howard C. Zisser
- Dept. of Chemical Engineering; University of California; Santa Barbara CA 93106
- Sansum Diabetes Research Institute; Santa Barbara CA 93105
| | - Lois Jovanovič
- Dept. of Chemical Engineering; University of California; Santa Barbara CA 93106
- Sansum Diabetes Research Institute; Santa Barbara CA 93105
| | - Francis J. Doyle
- Dept. of Chemical Engineering; University of California; Santa Barbara CA 93106
- Sansum Diabetes Research Institute; Santa Barbara CA 93105
| | - Dale E. Seborg
- Dept. of Chemical Engineering; University of California; Santa Barbara CA 93106
- Sansum Diabetes Research Institute; Santa Barbara CA 93105
| |
Collapse
|
26
|
Artificial Pancreas Coupled Vital Signs Monitoring for Improved Patient Safety. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2013. [DOI: 10.1007/s13369-012-0456-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
|
27
|
Toffanin C, Messori M, Palma FD, Nicolao GD, Cobelli C, Magni L. Artificial pancreas: model predictive control design from clinical experience. J Diabetes Sci Technol 2013; 7:1470-83. [PMID: 24351173 PMCID: PMC3876325 DOI: 10.1177/193229681300700607] [Citation(s) in RCA: 70] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND The objective of this research is to develop a new artificial pancreas that takes into account the experience accumulated during more than 5000 h of closed-loop control in several clinical research centers. The main objective is to reduce the mean glucose value without exacerbating hypo phenomena. Controller design and in silico testing were performed on a new virtual population of the University of Virginia/Padova simulator. METHODS A new sensor model was developed based on the Comparison of Two Artificial Pancreas Systems for Closed-Loop Blood Glucose Control versus Open-Loop Control in Patients with Type 1 Diabetes trial AP@home data. The Kalman filter incorporated in the controller has been tuned using plasma and pump insulin as well as plasma and continuous glucose monitoring measures collected in clinical research centers. New constraints describing clinical knowledge not incorporated in the simulator but very critical in real patients (e.g., pump shutoff) have been introduced. The proposed model predictive control (MPC) is characterized by a low computational burden and memory requirements, and it is ready for an embedded implementation. RESULTS The new MPC was tested with an intensive simulation study on the University of Virginia/Padova simulator equipped with a new virtual population. It was also used in some preliminary outpatient pilot trials. The obtained results are very promising in terms of mean glucose and number of patients in the critical zone of the control variability grid analysis. CONCLUSIONS The proposed MPC improves on the performance of a previous controller already tested in several experiments in the AP@home and JDRF projects. This algorithm complemented with a safety supervision module is a significant step toward deploying artificial pancreases into outpatient environments for extended periods of time.
Collapse
Affiliation(s)
- Chiara Toffanin
- Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy
| | - Mirko Messori
- Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy
| | - Federico Di Palma
- Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy
| | - Giuseppe De Nicolao
- Department of Industrial and Information Engineering, University of Pavia, Pavia, Italy
| | - Claudio Cobelli
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Lalo Magni
- Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy
| |
Collapse
|
28
|
Colmegna P, Sánchez Peña RS. Analysis of three T1DM simulation models for evaluating robust closed-loop controllers. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2013; 113:371-382. [PMID: 24183071 DOI: 10.1016/j.cmpb.2013.09.020] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/16/2012] [Revised: 09/19/2013] [Accepted: 09/25/2013] [Indexed: 06/02/2023]
Abstract
This work compares three well-known models and simulators in terms of their use in the analysis and design of glucose controllers for patients with Type 1 Diabetes Mellitus (T1DM). The objective is to compare them in practical scenarios which include: model uncertainty, time variance, nonlinearities, glucose measurement noise, delays between subcutaneous and plasma levels, pump saturation, and real-time controller implementation. The pros and cons of all models/simulators are presented. Finally, the simulators are tested with different robust controllers in order to identify the difficulties in the design and implementation phases. To this end, three sources of uncertainty are considered: nonlinearities, time-varying behavior (intra-patient) and inter-patient differences.
Collapse
Affiliation(s)
- P Colmegna
- Centro de Sistemas y Control, Departamento de Matemática, Instituto Tecnológico de Buenos Aires (ITBA), Av. Eduardo Madero 399, C1106ACD Buenos Aires, Argentina.
| | | |
Collapse
|
29
|
Zhao C, Sun Y, Zhao L. Interindividual glucose dynamics in different frequency bands for online prediction of subcutaneous glucose concentration in type 1 diabetic subjects. AIChE J 2013. [DOI: 10.1002/aic.14176] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Affiliation(s)
- Chunhui Zhao
- State Key Laboratory of Industrial Control Technology, Dept. of Control Science and Engineering; Zhejiang University; Hangzhou; 310027; China
| | - Youxian Sun
- State Key Laboratory of Industrial Control Technology, Dept. of Control Science and Engineering; Zhejiang University; Hangzhou; 310027; China
| | - Luping Zhao
- State Key Laboratory of Industrial Control Technology, Dept. of Control Science and Engineering; Zhejiang University; Hangzhou; 310027; China
| |
Collapse
|
30
|
Dassau E, Zisser H, Harvey RA, Percival MW, Grosman B, Bevier W, Atlas E, Miller S, Nimri R, Jovanovic L, Doyle FJ. Clinical evaluation of a personalized artificial pancreas. Diabetes Care 2013; 36. [PMID: 23193210 PMCID: PMC3609541 DOI: 10.2337/dc12-0948] [Citation(s) in RCA: 69] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/03/2023]
Abstract
OBJECTIVE An artificial pancreas (AP) that automatically regulates blood glucose would greatly improve the lives of individuals with diabetes. Such a device would prevent hypo- and hyperglycemia along with associated long- and short-term complications as well as ease some of the day-to-day burden of frequent blood glucose measurements and insulin administration. RESEARCH DESIGN AND METHODS We conducted a pilot clinical trial evaluating an individualized, fully automated AP using commercial devices. Two trials (n = 22, n(subjects) = 17) were conducted using a multiparametric formulation of model predictive control and an insulin-on-board algorithm such that the control algorithm, or "brain," can be embedded on a chip as part of a future mobile device. The protocol evaluated the control algorithm for three main challenges: 1) normalizing glycemia from various initial glucose levels, 2) maintaining euglycemia, and 3) overcoming an unannounced meal of 30 ± 5 g carbohydrates. RESULTS Initial glucose values ranged from 84-251 mg/dL. Blood glucose was kept in the near-normal range (80-180 mg/dL) for an average of 70% of the trial time. The low and high blood glucose indices were 0.34 and 5.1, respectively. CONCLUSIONS These encouraging short-term results reveal the ability of a control algorithm tailored to an individual's glucose characteristics to successfully regulate glycemia, even when faced with unannounced meals or initial hyperglycemia. To our knowledge, this represents the first truly fully automated multiparametric model predictive control algorithm with insulin-on-board that does not rely on user intervention to regulate blood glucose in individuals with type 1 diabetes.
Collapse
Affiliation(s)
- Eyal Dassau
- Department of Chemical Engineering, University of California, Santa Barbara, California, USA
| | | | | | | | | | | | | | | | | | | | | |
Collapse
|
31
|
Blood glucose control algorithms for type 1 diabetic patients: A methodological review. Biomed Signal Process Control 2013. [DOI: 10.1016/j.bspc.2012.09.003] [Citation(s) in RCA: 83] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
|
32
|
Liu SW, Huang HP, Lin CH, Chien IL. A Hybrid Neural Network Model Predictive Control with Zone Penalty Weights for Type 1 Diabetes Mellitus. Ind Eng Chem Res 2012. [DOI: 10.1021/ie202308w] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Shih-Wei Liu
- Department of Chemical Engineering, National Taiwan University, Taipei 10617, Taiwan
| | - Hsiao-Ping Huang
- Department of Chemical Engineering, National Taiwan University, Taipei 10617, Taiwan
| | - Chia-Hung Lin
- Division of Endocrinology and
Metabolism, Department of Internal Medicine, Chang Gung Memorial Hospital, Tao-Yuan 33305, Taiwan
| | - I-Lung Chien
- Department of Chemical Engineering, National Taiwan University, Taipei 10617, Taiwan
| |
Collapse
|
33
|
Zhao C, Dassau E, Jovanovič L, Zisser HC, Doyle FJ, Seborg DE. Predicting subcutaneous glucose concentration using a latent-variable-based statistical method for type 1 diabetes mellitus. J Diabetes Sci Technol 2012; 6:617-33. [PMID: 22768893 PMCID: PMC3440055 DOI: 10.1177/193229681200600317] [Citation(s) in RCA: 46] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Accurate prediction of future glucose concentration for type 1 diabetes mellitus (T1DM) is needed to improve glycemic control and to facilitate proactive management before glucose concentrations reach undesirable concentrations. The availability of frequent glucose measurements, insulin infusion rates, and meal carbohydrate estimates can be used to good advantage to capture important information concerning glucose dynamics. METHODS This article evaluates the feasibility of using a latent variable (LV)-based statistical method to model glucose dynamics and to forecast future glucose concentrations for T1DM applications. The prediction models are developed using a proposed LV-based approach and are evaluated for retrospective clinical data from seven individuals with T1DM and for In silico simulations using the Food and Drug Administration-accepted University of Virginia/University of Padova metabolic simulator. This article provides comparisons of the prediction accuracy of the LV-based method with that of a standard modeling alternative. The influence of key design parameters on the performance of the LV-based method is also illustrated. RESULTS In general, the LV-based method provided improved prediction accuracy in comparison with conventional autoregressive (AR) models and autoregressive with exogenous input (ARX) models. For larger prediction horizons (≥30 min), the LV-based model with exogenous inputs achieved the best prediction performance based on a paired t-test (α = 0.05). CONCLUSIONS The LV-based method resulted in models whose glucose prediction accuracy was as least as good as the accuracies of standard AR/ARX models and a simple model-free approach. Furthermore, the new approach is less sensitive to changing conditions and the effect of key design parameters.
Collapse
Affiliation(s)
- Chunhui Zhao
- Department of Chemical Engineering, University of California, Santa BarbaraSanta Barbara, California
| | - Eyal Dassau
- Department of Chemical Engineering, University of California, Santa BarbaraSanta Barbara, California
| | - Lois Jovanovič
- Sansum Diabetes Research InstituteSanta Barbara, California
| | | | - Francis J. Doyle
- Department of Chemical Engineering, University of California, Santa BarbaraSanta Barbara, California
| | - Dale E. Seborg
- Department of Chemical Engineering, University of California, Santa BarbaraSanta Barbara, California
| |
Collapse
|
34
|
Patek SD, Magni L, Dassau E, Karvetski C, Toffanin C, De Nicolao G, Del Favero S, Breton M, Man CD, Renard E, Zisser H, Doyle FJ, Cobelli C, Kovatchev BP. Modular closed-loop control of diabetes. IEEE Trans Biomed Eng 2012; 59:2986-99. [PMID: 22481809 DOI: 10.1109/tbme.2012.2192930] [Citation(s) in RCA: 137] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
Modularity plays a key role in many engineering systems, allowing for plug-and-play integration of components, enhancing flexibility and adaptability, and facilitating standardization. In the control of diabetes, i.e., the so-called "artificial pancreas," modularity allows for the step-wise introduction of (and regulatory approval for) algorithmic components, starting with subsystems for assured patient safety and followed by higher layer components that serve to modify the patient's basal rate in real time. In this paper, we introduce a three-layer modular architecture for the control of diabetes, consisting in a sensor/pump interface module (IM), a continuous safety module (CSM), and a real-time control module (RTCM), which separates the functions of insulin recommendation (postmeal insulin for mitigating hyperglycemia) and safety (prevention of hypoglycemia). In addition, we provide details of instances of all three layers of the architecture: the APS© serving as the IM, the safety supervision module (SSM) serving as the CSM, and the range correction module (RCM) serving as the RTCM. We evaluate the performance of the integrated system via in silico preclinical trials, demonstrating 1) the ability of the SSM to reduce the incidence of hypoglycemia under nonideal operating conditions and 2) the ability of the RCM to reduce glycemic variability.
Collapse
Affiliation(s)
- S D Patek
- Center for Diabetes Technology and Department of Systems and Information Engineering, University of Virginia, Charlottesville, VA 22904-4747, USA.
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
35
|
Hariri A, Wang LY. Observer-based state feedback for enhanced insulin control of type 'i' diabetic patients. Open Biomed Eng J 2012; 5:98-109. [PMID: 22276077 PMCID: PMC3257839 DOI: 10.2174/1874120701105010098] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2011] [Revised: 10/31/2011] [Accepted: 11/01/2011] [Indexed: 12/05/2022] Open
Abstract
During the past few decades, biomedical modeling techniques have been applied to improve performance of a wide variety of medical systems that require monitoring and control. Diabetes is one of the most important medical problems. This paper focuses on designing a state feedback controller with observer to improve the performance of the insulin control for type ‘I’ diabetic patients. The dynamic model of glucose levels in diabetic patients is a nonlinear model. The system is a typical fourth-order single-input-single-output state space model. Using a linear time invariant controller based on an operating condition is a common method to simplify control design. On the other hand, adaptive control can potentially improve system performance. But it increases control complexity and may create further stability issues. This paper investigates patient models and presents a simplified control scheme using observer-based feedback controllers. By comparing different control schemes, it shows that a properly designed state feedback controller with observer can eliminate the adaptation strategy that the Proportional-Integral-Derivative (PID) controllers need to improve the control performance. Control strategies are simulated and their performance is evaluated in MATLAB and Simulink.
Collapse
Affiliation(s)
- Ali Hariri
- DTE Energy, One Energy Plaza, Detroit, Michigan, 48226
| | | |
Collapse
|
36
|
Zarkogianni K, Vazeou A, Mougiakakou SG, Prountzou A, Nikita KS. An Insulin Infusion Advisory System Based on Autotuning Nonlinear Model-Predictive Control. IEEE Trans Biomed Eng 2011; 58:2467-77. [DOI: 10.1109/tbme.2011.2157823] [Citation(s) in RCA: 50] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
|
37
|
Percival M, Wang Y, Grosman B, Dassau E, Zisser H, Jovanovič L, Doyle F. Development of a multi-parametric model predictive control algorithm for insulin delivery in type 1 diabetes mellitus using clinical parameters. JOURNAL OF PROCESS CONTROL 2011; 21:391-404. [PMID: 21516218 PMCID: PMC3079204 DOI: 10.1016/j.jprocont.2010.10.003] [Citation(s) in RCA: 20] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
A multi-parametric model predictive control (mpMPC) algorithm for subcutaneous insulin delivery for individuals with type 1 diabetes mellitus (T1DM) that is computationally efficient, robust to variations in insulin sensitivity, and involves minimal burden for the user is proposed. System identification was achieved through impulse response tests feasible for ambulatory conditions on the UVa/Padova simulator adult subjects with T1DM. An alternative means of system identification using readily available clinical parameters was also investigated. A safety constraint was included explicitly in the algorithm formulation using clinical parameters typical of those available to an attending physician. Closed-loop simulations were carried out with daily consumption of 200 g carbohydrate. Controller robustness was assessed by subject/model mismatch scenarios addressing daily, simultaneous variation in insulin sensitivity and meal size with the addition of Gaussian white noise with a standard deviation of 10%. A second-order-plus-time-delay transfer function model fit the validation data with a mean (coefficient of variation) root-mean-square-error (RMSE) of 26 mg/dL (19%) for a 3 h prediction horizon. The resulting control law maintained a low risk Low Blood Glucose Index without any information about carbohydrate consumption for 90% of the subjects. Low-order linear models with clinically meaningful parameters thus provided sufficient information for a model predictive control algorithm to control glycemia. The use of clinical knowledge as a safety constraint can reduce hypoglycemic events, and this same knowledge can further improve glycemic control when used explicitly as the controller model. The resulting mpMPC algorithm was sufficiently compact to be implemented on a simple electronic device.
Collapse
Affiliation(s)
- M.W. Percival
- Department of Chemical Engineering, University of California, Santa Barbara, CA 93106-5080, United States
- Sansum Diabetes Research Institute, Santa Barbara, CA 93105-4321, United States
| | - Y. Wang
- Department of Chemical Engineering, University of California, Santa Barbara, CA 93106-5080, United States
- Sansum Diabetes Research Institute, Santa Barbara, CA 93105-4321, United States
| | - B. Grosman
- Department of Chemical Engineering, University of California, Santa Barbara, CA 93106-5080, United States
- Sansum Diabetes Research Institute, Santa Barbara, CA 93105-4321, United States
| | - E. Dassau
- Department of Chemical Engineering, University of California, Santa Barbara, CA 93106-5080, United States
- Sansum Diabetes Research Institute, Santa Barbara, CA 93105-4321, United States
| | - H. Zisser
- Department of Chemical Engineering, University of California, Santa Barbara, CA 93106-5080, United States
- Sansum Diabetes Research Institute, Santa Barbara, CA 93105-4321, United States
| | - L. Jovanovič
- Department of Chemical Engineering, University of California, Santa Barbara, CA 93106-5080, United States
- Sansum Diabetes Research Institute, Santa Barbara, CA 93105-4321, United States
| | - F.J. Doyle
- Department of Chemical Engineering, University of California, Santa Barbara, CA 93106-5080, United States
- Sansum Diabetes Research Institute, Santa Barbara, CA 93105-4321, United States
| |
Collapse
|
38
|
Abstract
Automated closed-loop insulin delivery, also referred to as the 'artificial pancreas', has been an important but elusive goal of diabetes treatment for many decades. Research milestones include the conception of continuous glucose monitoring in the early 1960s, followed by the production of the first commercial hospital-based artificial pancreas in the late 1970s that combined intravenous glucose sensing and insulin delivery. In the past 10 years, research into the artificial pancreas has gained substantial momentum and focused on the subcutaneous route for glucose measurement and insulin delivery, which reflects technological advances in interstitial glucose monitoring and the increasing use of the continuous subcutaneous insulin infusion. This Review discusses the design of an artificial pancreas, its components and clinical results, as well as the advantages and disadvantages of different types of automated closed-loop systems and potential future advances. The introduction of the artificial pancreas into clinical practice will probably occur gradually, starting with simpler approaches, such as overnight control of blood glucose concentration and temporary pump shut-off, that are adapted to more complex situations, such as glycemic control during meals and exercise.
Collapse
Affiliation(s)
- Roman Hovorka
- Institute of Metabolic Science, University of Cambridge, Cambridge CB2 0QQ, UK.
| |
Collapse
|
39
|
De Nicolao G, Magni L, Man CD, Cobelli C. Modeling and Control of Diabetes: Towards the Artificial Pancreas. ACTA ACUST UNITED AC 2011. [DOI: 10.3182/20110828-6-it-1002.03036] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
|
40
|
Time-Varying Procedures for Insulin-Dependent Diabetes Mellitus Control. JOURNAL OF ELECTRICAL AND COMPUTER ENGINEERING 2011. [DOI: 10.1155/2011/697543] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
This work considers the problem of automatically controlling the glucose level in insulin dependent diabetes mellitus (IDDM) patients. The objective is to include several important and practical issues in the design: model uncertainty, time variations, nonlinearities, measurement noise, actuator delay and saturation, and real time implementation. These are fundamental issues to be solved in a device implementing this control. Two time-varying control procedures have been proposed which take into consideration all of them: linear parameter varying (LPV) and unfalsified control (UC). The controllers are implemented with low-order dynamics that adapt continuously according to the glucose levels measured in real time in one case (LPV) and by controller switching based on the actual performance in the other case (UC). Both controllers have performed adequately under all these practical restrictions, and a discussion on pros and cons of each method is presented at the end.
Collapse
|
41
|
Markakis MG, Mitsis GD, Papavassilopoulos GP, Ioannou PA, Marmarelis VZ. A switching control strategy for the attenuation of blood glucose disturbances. OPTIMAL CONTROL APPLICATIONS & METHODS 2011; 32:185-195. [PMID: 21516241 PMCID: PMC3081193 DOI: 10.1002/oca.900] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
In this computational study we consider a generalized minimal model structure for the intravenously infused insulin-blood glucose dynamics, which can represent a wide variety of diabetic patients, and augment this model structure with a glucose rate disturbance signal that captures the aggregate effects of various internal and external factors on blood glucose. Then we develop a model-based, switching controller, which attempts to balance between optimal performance, reduced computational complexity and avoidance of dangerous hypoglycaemic events. We evaluate the proposed algorithm relative to the widely studied proportional-derivative controller for the regulation of blood glucose with continuous insulin infusions. The results show that the proposed switching control strategy can regulate blood glucose much better than the proportional-derivative controller for all the different types of diabetic patients examined. This new algorithm is also shown to be remarkably robust in the event of concurrent, unknown variations in critical parameters of the adopted model.
Collapse
Affiliation(s)
- Mihalis G Markakis
- Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, U.S.A
| | | | | | | | | |
Collapse
|
42
|
Modelling of the Insulin Delivery System for patients with Type 1 Diabetes Mellitus. ACTA ACUST UNITED AC 2011. [DOI: 10.1016/b978-0-444-54298-4.50079-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register]
|
43
|
Harvey RA, Wang Y, Grosman B, Percival MW, Bevier W, Finan DA, Zisser H, Seborg DE, Jovanovic L, Doyle FJ, Dassau E. Quest for the artificial pancreas: combining technology with treatment. ACTA ACUST UNITED AC 2010; 29:53-62. [PMID: 20659841 DOI: 10.1109/memb.2009.935711] [Citation(s) in RCA: 78] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
The various components of the artificial pancreas puzzle are being put into place. Features such as communication, control, modeling, and learning are being realized presently. Steps have been set in motion to carry the conceptual design through simulation to clinical implementation. The challenging pieces still to be addressed include stress and exercise; as integral parts of the ultimate goal, effort has begun to shift toward overcoming the remaining hurdles to the full artificial pancreas. The artificial pancreas is close to becoming a reality, driven by technology, and the expectation that lives will be improved.
Collapse
Affiliation(s)
- Rebecca A Harvey
- Chemical Engineering, Northeastern University, Boston, Massachusetts, USA
| | | | | | | | | | | | | | | | | | | | | |
Collapse
|
44
|
Sánchez Peña RS, Ghersin AS. LPV control of glucose for Diabetes type I. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2010; 2010:680-3. [PMID: 21095893 DOI: 10.1109/iembs.2010.5626217] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
This paper considers the problem of automatically controlling the glucose level in a Diabetes type I patient. Three issues have been considered: model uncertainty, time-varying/nonlinear phenomena and controller implementation. To that end, the dynamical model of the insulin/glucose relation is framed as a Linear Parameter Varying system and a controller is designed based on it. In addition, this framework allows not only a better performance than other classical methods, but also provides stability and performance guarantees. Design computations are based on convex Linear Matrix Inequality (LMI) optimization. Implementation is based on a low order controller whose dynamics adapts according to the glucose levels measured in real-time.
Collapse
Affiliation(s)
- R S Sánchez Peña
- Centro de Sistemas y Control, Department of Physics & Mathematics Buenos Aires Institute of Technology (ITBA), Argentina.
| | | |
Collapse
|
45
|
Aye T, Block J, Buckingham B. Toward closing the loop: an update on insulin pumps and continuous glucose monitoring systems. Endocrinol Metab Clin North Am 2010; 39:609-24. [PMID: 20723823 PMCID: PMC2938733 DOI: 10.1016/j.ecl.2010.05.005] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/04/2023]
Abstract
This article reviews current pump and continuous glucose monitoring therapy and what will be required to integrate these systems into closed-loop control. Issues with sensor accuracy, lag time, and calibration are discussed as well as issues with insulin pharmacodynamics, which result in a delayed onset of insulin action in a closed-loop system. A stepwise approach to closed-loop therapy is anticipated, where the first systems will suspend insulin delivery based on actual or predicted hypoglycemia. Subsequent systems may control to range, limiting the time spent in hyperglycemia by mitigating the effects of a missed food bolus or underestimate of consumed carbohydrates, while minimizing the risk of hypoglycemia.
Collapse
Affiliation(s)
- Tandy Aye
- Department of Pediatrics, Stanford Medical Center, G-313, 300 Pasteur Drive, Stanford, CA, 94305-5208, Phone: 650-723-5791, Fax: 650-7258375
| | - Jen Block
- Department of Pediatrics, Stanford Medical Center, G-313, 300 Pasteur Drive, Stanford, CA, 94305-5208, Phone: 650-723-5791, Fax: 650-7258375
| | - Bruce Buckingham
- Department of Pediatrics, Stanford Medical Center, G-313, 300 Pasteur Drive, Stanford, CA, 94305-5208, Phone: 650-723-5791, Fax: 650-7258375
| |
Collapse
|
46
|
Abu-Rmileh A, Garcia-Gabin W. Feedforward-feedback multiple predictive controllers for glucose regulation in type 1 diabetes. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2010; 99:113-123. [PMID: 20430467 DOI: 10.1016/j.cmpb.2010.02.010] [Citation(s) in RCA: 10] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/07/2009] [Revised: 02/22/2010] [Accepted: 02/26/2010] [Indexed: 05/29/2023]
Abstract
Type 1 diabetic patients depend on insulin therapy to maintain blood glucose levels within safe range. The idea behind the "Artificial Pancreas" is to mimic, as close as possible, the functions of the natural pancreas in glucose sensing and insulin delivery, by using closed-loop control techniques. This work presents a model-based predictive control strategy for blood glucose regulation in diabetic patients. The controller is provided with a feedforward loop to improve meal compensation, a gain scheduling scheme to improve the controller performance in controlling the nonlinear glucose-insulin system, and an asymmetric cost function to reduce the hypoglycemic risk. Simulation scenarios with virtual patients are used to test the designed controller. The obtained results show a good controller performance in fasting conditions and meal disturbance rejection, and robustness against measurements errors, meal estimation errors, and changes in insulin sensitivity.
Collapse
Affiliation(s)
- Amjad Abu-Rmileh
- Department of Electrical, Electronics and Control Engineering, University of Girona, Girona, Spain.
| | | |
Collapse
|
47
|
Modelling and multi-parametric control for delivery of anaesthetic agents. Med Biol Eng Comput 2010; 48:543-53. [DOI: 10.1007/s11517-010-0604-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2009] [Accepted: 03/05/2010] [Indexed: 10/19/2022]
|
48
|
Internal model sliding mode control approach for glucose regulation in type 1 diabetes. Biomed Signal Process Control 2010. [DOI: 10.1016/j.bspc.2009.12.003] [Citation(s) in RCA: 36] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
|
49
|
Noble SL, Sherer E, Hannemann RE, Ramkrishna D, Vik T, Rundell AE. Using adaptive model predictive control to customize maintenance therapy chemotherapeutic dosing for childhood acute lymphoblastic leukemia. J Theor Biol 2010; 264:990-1002. [PMID: 20138060 DOI: 10.1016/j.jtbi.2010.01.031] [Citation(s) in RCA: 37] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2009] [Revised: 01/29/2010] [Accepted: 01/29/2010] [Indexed: 01/18/2023]
Abstract
Acute lymphoblastic leukemia (ALL) is a common childhood cancer in which nearly one-quarter of patients experience a disease relapse. However, it has been shown that individualizing therapy for childhood ALL patients by adjusting doses based on the blood concentration of active drug metabolite could significantly improve treatment outcome. An adaptive model predictive control (MPC) strategy is presented in which maintenance therapy for childhood ALL is personalized using routine patient measurements of red blood cell mean corpuscular volume as a surrogate for the active drug metabolite concentration. A clinically relevant mathematical model is developed and used to describe the patient response to the chemotherapeutic drug 6-mercaptopurine, with some model parameters being patient-specific. During the course of treatment, the patient-specific parameters are adaptively identified using recurrent complete blood count measurements, which sufficiently constrain the patient parameter uncertainty to support customized adjustments of the drug dose. While this work represents only a first step toward a quantitative tool for clinical use, the simulated treatment results indicate that the proposed mathematical model and adaptive MPC approach could serve as valuable resources to the oncologist toward creating a personalized treatment strategy that is both safe and effective.
Collapse
Affiliation(s)
- Sarah L Noble
- Weldon School of Biomedical Engineering, Purdue University, 206 South Martin Jischke Drive, West Lafayette, IN 47907, USA.
| | | | | | | | | | | |
Collapse
|
50
|
Ulas Acikgoz S, Diwekar UM. Blood glucose regulation with stochastic optimal control for insulin-dependent diabetic patients. Chem Eng Sci 2010. [DOI: 10.1016/j.ces.2009.09.077] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
|