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Progress in Blood Glucose Closed-Loop Control Algorithms for Artificial Pancreas |
Harbin Institute of Technology at Weihai, Key Laboratory of Shandong Province Modern Digital Medical Equipment, Weihai 264209 China |
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Abstract In order to achieve proper glucose control, diabetes patients are required frequent blood glucose monitoring and insulin injection. Over the past three decades, automated closedloop insulin delivery systems with glucose monitoring function (called artificial pancreas) have been largely investigated world-wide. In the system, algorithms that control insulin delivery in artificial pancreas with timedelay are the most important issue. This review will introduce and analyze the components and control problems of existing artificial pancreas prototypes. Next we will focus on the blood glucose control algorithms that enable outpatient use of closed-loop insulin delivery system. The advantages and disadvantages of various control algorithms will be discussed from the point of adaptability in artificial pancreas. Latest clinical application of these glucose control algorithms in artificial pancreas system are also summarized and commented. Among those methods, model predictive control, PID and fuzzy control are efficient control methods in clinical. The challenges and futher directions, such as rewrite algorithms in chips, meal prediction, and safety problem will be discussed.
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[1]Steil GM, Panteleon AE, Rebrin K. Closedloop insulin delivery—the path to physiological glucose control [J]. Advanced Drug Delivery Reviews, 2004, 56:125-144.
[2]Hanaire H. Continuous glucose monitoring and external insulin pump: towards a subcutaneous closed loop [J]. Diabetes Metab, 2006, 32:534-538.
[3]Albisser AM, Leibel BS, Ewart TG, et al. Clinical control of diabetes by the artificial pancreas [J]. Diatetes, 1974,23:397-404.
[4]Hovorka R. Continuous glucose monitoring and closedloop systems [J]. Diabetic Medicine, 2005, 23:1-12.
[5]Weinzimer SA. Closedloop artificial pancreas: current studies and promise for the future [J].Curr Opin Endocrinol Diabetes Obes, 2012,19: 88-92.
[6]Hovorka R, Wilinska ME, Chassin LJ, et al. Roadmap to the artificial pancreas [J]. Diabetes Research and Clinical Practice, 2006, 74: S178-S182.
[7]B. Wayne Bequette.Challenges and recent progress in the development of a closedloop artificial pancreas [J]. Annual Reviews in Control.2012, 36(2): 255–266.
[8]Hovorka R. Closedloop insulin delivery: from bench to clinical practice [J]. Nature Reviews Endocrinology, 2011, 7:385-395
[9]Parker RS, Doyle III FJ, Peppas NA. The intravenous route to blood glucose control [J]. IEEE Engineering in Medicine and Biology. 2001, 1:65-73.
[10]Elleri D, Dunger DB, Hovorka R. Closedloop insulin delivery for treatment of type 1 diabetes [J]. BMC Medicine, 2011, 9:120-129.
[11]Hovorka R, Nodale M, Haidar A, et al. Assessing performance of closedloop insulin delivery systems by continuous glucose monitoring: drawbacks and way forward[J]. Diabetes Technology &Therapeutics,2013,15(1):4-12.
[12]Tsukamoto Y, Kinoshita Y, Kitagawa H, et al. Evaluation of a Novel Artificial Pancreas: Closed Loop Glycemic Control System With Continuous Blood Glucose Monitoring[J].Artificial Organs.2013,37(4):E67-E73.
[13]Broekhuyse HM, Nelson JD, Zinman B, et al. Comparison of algorithms for the closedloop control of blood glucose using the artificial beta cell [J]. IEEE Trans Biomed Eng BME, 1981, 28: 678-687.
[14]Swan GW. An Optimal control model of diabetes mellitus [J].Bull Math Bio, 1982, 44:793-808.
[15]Fisher ME, Teo KL. Optimal insulin infusion resulting from a mathematical model of blood glucose dynamics [J]. IEEE Trans Biomed Eng, 1989, 36: 479-486.
[16]Lim CC, Teo KL. Optimal insulin infusion control via a mathematical blood glucoregulatory model with fuzzy parameters [J]. Cybernet. Syst, 1991, 22: 1-16.
[17]Fisher ME. A semiclosedloop algorithm for the control of blood glucose levels in diabetics. IEEE Trans Biomed Eng, 1991, 38: 57-61.
[18]Parker RS, Doyle III FJ, Peppas NA. A modelbased algorithm for blood glucose control in type I diabetic patients. IEEE Trans Biomed Eng, 1999, 46(2): 148-157.
[19]Leesirikul M. a study on glucose computer simulation and modeling [D]. Boca Raton: Florida Atlantic University, 2005.
[20]Hovorka R, Chassin LJ, Ellmerer M, et al. A simulation model of glucose regulation in the critically ill [J]. Physiological Measurement, 2008,29: 959-978.
[21]Cobelli C, Man CD, Sparacino G, et al. Diabetes: models, signals, and control [J]. IEEE Reviews in Biomedical Engineering, 2009, 2:54-96
[22]Ramprasad Y, Rangaiah GP, Lakshminarayanan S. Robust PID controller for blood glucose regulation in type I diabetics [J]. Ind Eng Chem Res, 2004, 43: 8257-8268.
[23]Shen, JC. New tuning method for PID controller. ISA Trans, 2002, 41: 473-484.
[24]Li Chengwei, Hu Ruiqiang. PID control based on BP neural network for the regulation of blood glucose level in diabetes [C]// Proceedings of the 7th IEEE International Conference on Bioinformatics and Bioengineering. Boston: IEEE, 2007:1168-1172.
[25]Marchetti MBG, Jovanovic L, Zisser H, et al. An improved PID switching control strategy for type 1 diabetes [J].IEEE Trans Biomed Eng, 2008,55 (3): 857-865.
[26]Parker RS, Doyle III FJ, Harting JE, et al. Model predictive control for infusion pump insulin delivery[C]// Proceedings of 18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Amsterdam : IEEE, 1996:1822-1823.
[27]Parker RS, Doyle III FJ, Peppas NA. A modelbased algorithm for blood glucose control in type I diabetic patients [J]. IEEE Transactions on Biomedical Engineering, 1999, 46(2):148-157.
[28]Parker RS, Gatzke EP, Doyle III FJ. Advanced model predictive control (MPC) for type I diabetic patient bloold glucose control[C]// Hyatt Regency. Proceedings of the American Control Conference. Chicago: American Automatic Control Council, 2000,6:3483-3487.
[29]Sandra M. Lynch B. Wayne B. Estimationbased model predictive control of blood glucose in type I diabetics: a simulation study [C]// Enderle JD,eds. Proceedings of the IEEE 27th Annual Northeast Bioengineering Conference. Storrs:: IEEE, 2001:79-80.
[30]Schauer T, Raisch J. Modelbased predictive control of bloodsugar level in intensive care [C]// Proceedings of Mediterranean conference on control and Automation. Athens: IEEE, 2007,7: T 27-017.
[31]Magni L, Raimondo DM, Bossi L, et al. Model predictive control of type 1 diabetes: an in silico trial [J]. Journal of Diabetes Science and Technology, 2007, 1(6):804-812.
[32]Magni L, Raimondo DM, Man CD, et al. Model predictive control of glucose concentration in type I diabetic patients: An in silico trial [J]. Biomedical Signal Processing and Control.2009,4: 338-346.
[33]Markakis MG, Mitsis GD, Papavassilopoulos GP, et al. Model predictive control of blood glucose in type 1 diabetes: the principal dynamic modes approach[C]//Proceedings of 30th Annual International IEEE EMBS Conference. Vancouver: 2008,8:5466-5469.
[34]Iancu E, Iancu L, Sfredel V. Predictive Control of Blood Glucose in Diabetes Mellitus Patients [C]// Proceedings of 2010 IEEE International Conference on Automation Quality and Testing Robotics (AQTR). ClujNapoca : IEEE, 2010, 2:1-6.
[35]Hovorka R, Canonico V, Chassin LJ, et al. Nonlinear model predictive control of glucose concentration in subjects with type 1 diabetes [J]. Physiological measurement.2004, 25: 905–920.
[36]Hovorka R, Kremen J, Blaha J, et al. Blood Glucose Control by a Model Predictive Control Algorithm with Variable Sampling Rate versus a RoutineGlucose Management Protocol in Cardiac SurgeryPatients: A Randomized Controlled Trial [J]. The Journal of Clinical Endocrinology & Metabolism,2007, 92(8):2960-2964.
[37]Schlotthauer G, Nicolini GA, Garnero LG, et al. Type 1 diabetes: modeling identification and nonlinear model predictive control [C] //Proceedings of the Second Joint EMBS Conference. Houston: IEEE,2002:226-227.
[38]Schlotthauer G, Gamero LG, Torres ME, et al. Modeling, identification and nonlinear model predictive control of type I diabetic patient [J]. Medical Engineering & Physics, 2005,28: 240-250.
[39]Zarkogianni K, Mougiakakou SG, Prountzou A, et al. An Insulin Infusion Advisory System for Type 1 Diabetes Patients based on NonLinear Model Predictive Control Methods [C]//Proceedings of the 29th Annual International Conference of the IEEE EMBS. Lyon: IEEE, 2007,8:5971-5974.
[40]Wang Youqing, Dassau E, Doyle III FJ. ClosedLoop Control of Artificial Pancreatic βCell in Type 1 Diabetes Mellitus Using Model Predictive Iterative Learning Control [J]. IEEE Transactions on biomedical Engineering, 2010, 57 (2):211-219.
[41]Percivala MW, Wanga Y, Grosmana B, et al. Development of a multiparametric model predictive control algorithm for insulin delivery in type 1 diabetes mellitus using clinical parameters [J]. Journal of Process Control, 2011, 21: 391-404.
[42]Zarkogianni K, Vazeou A, Mougiakakou SG, et al. An insulin infusion advisory system based on autotuning nonlinear modelpredictive control [J]. IEEE Transsctions on Biomedical engineering, 2011, 58 (9):2467-2477.
[43]Jaramillo MG, Calm R, Bondia J, et al Insulin dosage optimization based on prediction of postprandial glucose excursions under uncertain parameters and food intake [J]. Comput Meth Prog Bio, 2012,105: 61-69.
[44]Hughes CS, Patek SD, Breton M, et al. Anticipating the next meal using meal behavioral profiles: A hybrid modelbased stochastic predictive control algorithm for T1DM [J]. Comput Meth Prog Bio, 2011, 102:138–148.
[45]Turksoy K, Bayrak ES, Quinn L, et al. Multivariable Adaptive ClosedLoop Control of an Artificial Pancreas Without Meal and Activity Announcement[J]. Diabetes Technology & Therapeutics,2013,15(5):386-400.
[46]Stephan S, Stefan W, Lukas S, et al. A New Perspective on ClosedLoop Glucose Control Using a PhysiologyBased Pharmacokinetic/Pharmacodynamic Model Kernel [J]. Biological and Medical Systems, 2012, 8: 420-425
[47]Kienitz KH, Yoneyama T. A robust controller for insulin pumps based on HInfinity theory [J]. IEEE Transaction on Bionedical Engineering, 1993,40(20):1133-1137.
[48]Veliazquez ER, Fematb R, CamposDelgado DU. Blood glucose control for type I diabetes mellitus: A robust tracking H∞ problem [J]. Control Engineering Practice, 2004, 12:1179-1195.
[49]Parker RS, Doyle III FJ, Ward JH, et al. Robust H∞ glucose control in diabetes using a physiological model [J]. AlChE Journal, 2000, 16(12):2537-2549.
[50]Yasini Sh, Karimpour A, NaghibiSistanil MB, et al. An Automatic insulin infusion system based on Hinfinity control technique[C]//Proceedings of the 2008 IEEE, CIBEC'08. Cairo: IEEE, 2008:1-5.
[51]Quiroz G, Femat R. Theoretical blood glucose control in hyperand hypoglycemic and exercise scenarios by means of an H∞ algorithm [J]. Journal of Theoretical Biology, 2010, 263: 154-160.
[52]Trajanoski Z, Wach P. Neural predictive controller for insulin delivery using the subcutaneous route [J]. IEEE Transactions on Biomedical Engineering, 1998, 〖STHZ〗45(9):1122-1134.
[53]Phee1 HK, Tung WL, Quek C. A personalized approach to insulin regulation using braininspired neural semantic memory in diabetic glucose control [C]// Proceedings of the 2007 IEEE Congress on Evolutionary Computation. Singapore city: IEEE, 2007:2644-2651.
[54]Ali SF, Padhi R. Optimal blood glucose regulation using single network adaptive critics[C]//Proceedings of the 18th IEEE International Conference on Control Applications. St. Petersburg: IEEE, 2009,7: 89-94.
[55]Leon BS, Alanis AY, Sanchez EN, et al. Neural inverse optimal control applied to type 1 diabetes mellitus patients [C].// Hernandez L,eds. Proceedings of 2012 IEEE 3rd Latin American Symposium on Circuits and Systems. Playa del Carmen:IEEE, 2012: 1-4.
[56]Delgado DUC, Ordoez H, Femat R, et al. FuzzyBased controller for glucose regulation in type-1 diabetic patients by subcutaneous route [J]. IEEE Transactions on Biomedical Engineering, 2006, 53(11):2201-2210.
[57]Yasini Sh, Sistani MBN, Karimpour A. Active insulin infusion using fuzzybased closedloop control[C]//Proceedings of 2008 3rd International Conference on Intelligent System and Knowledge Engineering. Xiamen : IEEE,2008:429-434.
[58]Hsu JJ, Wang JI, Lee A, et al. Automated control of blood glucose in the OR and surgical ICU [J].Conf Proc IEEE Eng Med Biol Soc, 2009, 9:1286-1289.
[59]Fandi MA, Jaradat MA, Sardahi Y, et al. Intelligent control of glucose concentration based on an implantable insulin delivery system for type I diabetes[C]//2011 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT). Amman: IEEE, 2011:1-6.
[60]Osgouie KG, Azizi A. Optimizing fuzzy logic controller for diabetes type Ι by genetic algorithm[C]// Mahadevan V. The 2nd International Conference on Computer and Automation Engineering. Singapore City: IEEE, 2010, 2:4-8.
[61]Atlas E, Grunberg EA, Nimri R, et al. MDLogic artificial pancreas system——A pilot study in adults with type 1 diabetes [J]. Diabetes Care, 2010, 33(5):1072-1076.
[62]Nimri R, Danne T, Kordonouri O, et al. The “Glucositter” overnight automated closed loop system for type 1 diabetes: A randomized crossover trial [J]. Pediatric Diabetes, 2013,14(3):159-167.
[63]Kovatchev BP, Breton M, Man CD, et al. In silico preclinical trials: A proof of concept in closedloop control of type 1 diabetes [J]. Journal of Diabetes Science and Technology, 2009, 3(1):44-55.
[64]Shichiri M, Sakakida M, Nishida K, et al. Enhanced, simplified glucose sensors: longterm clinical application of wearable artificial endocrine pancreas [J]. Artif Organs, 1998; 22: 32-42.
[65]Steil GM, Rebrin K, Hariri F, et al. Continuous automated insulin delivery based on subcutaneous glucose sensing and an external insulin pump [J]. Diabetes, 2004; 53: A2.
[66]Renard E, Costalat G, Chevassus H, et al. Closed loop insulin delivery using implanted insulin pumps and sensors in type 1 diabetic patients[J]. Diabetes Research and Clinical Practice, 2006,74: S173-S177.
[67]Renard E, Panteleon AE, Leong P, et al. Efficacy of closed loop control of blood glucose based on an implantable i.v. sensor and intraperitoneal pump[J]. Diabetes, 2004:53: A114.
[68]Ruiz JL, Sherr JL, Cengiz E, et al. Effect of insulin feedback on closedloop glucose control: a crossover study[J]. Journal of Diabetes Science and Technology, 012, 6(5):1123-1130.
[69]O′Grady MJ,Retterath AJ,Keenan DB, et al. The Use of an automated, portable glucose control system for overnight glucose control in adolescents and young adults with type 1 diabetes. Diabetes Care, 2012, 35(11): 2182-2187.
[70]Hovorka R, Chassin LJ, Wilinska ME, et al. Closing the loop: the Adicol experience[J]. Diabetes Technol Ther, 2004; 6: 307-318.
[71]Schaller HC, Schaupp L, Bodenlenz M, et al. Avoidance of hypo and hyperglycaemia with a control loop system in patients with Type 1 DM under daily life conditions[J].Diabetes Metab, 2003; 29: A2225.
[72]Vering T. Minimally invasive control loop system for SCSC control on patients with type 1 diabetes [J]. Diabetes Technol Ther,2004; 6: 278.
[73]Freckmann G, Kalatz B, Pfeiffer B, et al. Recent advances in continuous glucose monitoring [J]. Exp Clin Endocr Diab, 2001, 109: S347-S357.
[74]Kalatz B, Hoss U, Gessler R, et al. Development of algorithms for feedbackcontrolled subcutaneous insulin infusion with insulin lispro [J]. Acta Diabetol, 1999; 36:215.
[75]Kalatz B. Algorithmen zur Glucosegesteuerten Insulininfusion bei Diabetes Mellitus—Entwicklung und Experimentelle Untersuchung. Medical Dissertation[D]. Ulm: University of Ulm, 1999.
[76]Hovorka R, Allen JM, Elleri D, et al. Manual closedloop insulin delivery in children and adolescents with type 1 diabetes: a phase 2 randomised crossover trial [J]. The Lancet, 2010,375:743-751.
[77]Murphy HR, Elier D, Alien JM.et al. ClosedLoop insulin delivery during pregnancy complicated by type 1 diabetes[J]. Diabetes Care, 2011,2(34):11.
[78]Hovorka R, Harris J, Allen JM, et al. Overnight closed loop insulin delivery (artificial pancreas) in adults with type 1 diabetes: crossover randomized controlled studies [J].BMJ, April 2011,342. [Epub ahead of print]
[79]Haidar A, Legault L, Dallaire M, et al. Glucoseresponsive insulin and glucagon delivery (dualhormone artificial pancreas) in adults with type 1 diabetes: a randomized crossover controlled trial [J]. CMAJ, 2013,185(4): 297-305.
[80]Leelarathna L, English SW, Thabit H, et al. Feasibility of fully automated closedloop glucose control utilizing continuous subcutaneous glucose measurements in critical illness: a randomised controlled trial [J]. Critical Care, 8 Jul,2013.[Epub ahead of print]
[81]Mel Ho, Georgiou P, Singhal S,et al. A Bioinspired closedloop insulin delivery based on the silicon pancreatic betacell [C]// Proceedings of IEEE International Symposium on Circuits and Systems. Seattle: Printing House,2008:1052-1055.
[82]Pagkalos I, Herrero P, Sharkawy ME,et al. A VHDL Implementation of the biostator II glucose control algorithm for critical Care[C]// Proceedings of 2011 IEEE Biomedical Circuits and Systems Conference (BioCAS). San Diego: IEEE, 2011:94-97.
[83]HariKumar R, Sudhaman VK, Babu CG. FPGA synthesis of fuzzy (PD and PID) controller for insulin pumps in diabetes using cadence [J]. International Journal of Soft Computing and Engineering (IJSCE), 2012,6(1):324-331.
[84]Vouzis PD, Bleris LG, Arnold MG, et al. A Systemonachip implementation for embedded realtime model predictive control [J]. IEEE Transactions on Control system technology, 2009,17(5):1006-1017.
[85]Kovatchev BP, Renard E,Cobelli C, et al. Feasibility of outpatient fully integrated closedloop control [J]. Diabetes Care Symposium, 2013, 36:1851-1858. |
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