Personalized hybrid artificial pancreas using unidirectional sliding-modes control algorithm

• Autorzy: Orozco-López Onofre , Castañeda Carlos E. , García-Sáez Gema , Hernando M. Elena , Rodríguez-Herrero Agustín

Identyfikatory

DOI

10.1016/j.bbe.2022.10.003

• Języki publikacji: EN

Abstrakty

EN

This paper presents an artificial pancreas algorithm implemented with a discrete-time sliding-mode method combined with a nonlinear block controllable form using a unidirectional control law and a discrete adaptive observer. The algorithm is both unidirectional, using insulin as unique input, and hybrid with a percentage of the pre-meal bolus administered in advance to complement the control action. Personalisation combines patient clustering and individual gain calculation after analysing glucose time-in-range. The stability of the complete closed-loop system involving the observer bounds is tested using the Lyapunov methodology. The performance of the algorithm is evaluated with 256 patients, simulated using Hovorka’s model. The evaluation defines two scenarios (closed-loop and open loop with continuous subcutaneous insulin infusion, CSII), divided into three 1- week intervals, to respectively compare the impact of glucose control under the expected meal plan as a result of changes in carbohydrates quantities and meal schedule, and in response to more extreme events. The performance of time-in-range in each period obtained with CSII is improved by hybrid closed-loop in all cases, i.e., with the expected meals (weekdays 74.9 vs 78.4; weekends 71.5 vs 73.4), with meal variability (weekdays 74.3 vs 77.6; weekend 71.6 vs 72.7), and in the presence of transgressions (forgetting meal announcement: 50.1 vs 63.4; meal omission: 78.2 vs 82.03; copious meal: 60.1 vs 63.4). Compared to CSII, the personalised nonlinear block controller was able to increase the time-in-range without glucose presence in time below range level 2 for 98 % of the cohort for expected meals, meal variability, and transgressions.

Słowa kluczowe

EN

hybrid artificial pancreas; unidirectional sliding mode; type 1 diabetes mellitus; adaptive Luenberger observer

PL

sztuczna trzustka; cukrzyca typu 1; adaptacyjny obserwator Luenbergera

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2022
• Tom: Vol. 42, no. 4
• Strony: 1218--1235
• Opis fizyczny: Bibliogr. 44 poz., rys., tab., wykr.

Twórcy

autor

Orozco-López Onofre

• University Center of Los Lagos, University of Guadalajara, Guadalajara, Mexico

autor

Castañeda Carlos E.

• University Center of Los Lagos, University of Guadalajara, Mexico

autor

García-Sáez Gema

• Center for Biomedical Technology (CTB), Universidad Politécnica de Madrid, Madrid, Spain
• Biomedical Research Networking Centre in Bioengineering Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain

autor

Hernando M. Elena

• Center for Biomedical Technology (CTB), Universidad Politécnica de Madrid, Madrid, Spain
• Biomedical Research Networking Centre in Bioengineering Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain

autor

Rodríguez-Herrero Agustín

• Center for Biomedical Technology (CTB), Universidad Politécnica de Madrid, Madrid, Spain
• Biomedical Research Networking Centre in Bioengineering Biomaterials and Nanomedicine (CIBER-BBN), Madrid, Spain

Bibliografia

• [1] Saeedi P, Petersohn I, Salpea P, Malanda B, Karuranga S, Unwin N, Colagiuri S, Guariguata L, Motala AA, Ogurtsova K, et al. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the international diabetes federation diabetes atlas. Diabetes Res Clin Practice 2019;157 107843.
• [2] Linkeschova R, Raoul M, Bott U, Berger M, Spraul M. Less severe hypoglycaemia, better metabolic control, and improved quality of life in type 1 diabetes mellitus with continuous subcutaneous insulin infusion (csii) therapy; an observational study of 100 consecutive patients followed for a mean of 2 years. Diabetic Med 2002;19(9):746–51.
• [3] Schönauer M, Thomas A. Sensor-augmented pump therapy– on the way to artificial pancreas. Avances en diabetología 2010;26(3):143–6.
• [4] Quintal A, Messier V, Rabasa-Lhoret R, Racine E. A critical review and analysis of ethical issues associated with the artificial pancreas. Diabetes Metabolism 2019;45(1):1–10.
• [5] Peyser T, Dassau E, Breton M, Skyler JS. The artificial pancreas: current status and future prospects in the management of diabetes. Ann N Y Acad Sci 2014;1311 (1):102–23.
• [6] Hernando ME, García-Sáez G, Gómez EJ, Pérez-Gandía C, Rodríguez-Herrero A. Automated insulin delivery: the artificial pancreas technical challenges. Am J Therap 2020;27 (1):e62–70.
• [7] Messori M, Toffanin C, Del Favero S, De Nicolao G, Cobelli C, Magni L. Model individualization for artificial pancreas. Comput Methods Programs Biomed 2019;171:133–40.
• [8] Olçomendy L, Pirog A, Bornat Y, Cieslak J, Gucik-Derigny D, Henry D, Catargi B, Renaud S. Tuning of an artificial pancreas controller: an in silico methodology based on clinically-relevant criteria. 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), IEEE 2020:2544–7.
• [9] Weng H, Hettiarachchi C, Nolan C, Suominen H, Lenskiy A. Ensuring security of artificial pancreas device system using homomorphic encryption. Biomed Signal Process Control 2023;79 104044.
• [10] Balasaheb WV, Uttam C. Novel intelligent optimization algorithm based fractional order adaptive proportional integral derivative controller for linear time invariant based biological systems. J Electr Eng Technol 2022;17(1):565–80.
• [11] Kumar V, Singh AK. Design of fuzzy controller for blood glucose level. In: Recent Advances in Operations Management Applications. Springer; 2022. p. 91–102.
• [12] Mosavi AH, Mohammadzadeh A, Rathinasamy S, Zhang C, Reuter U, Levente K, Adeli H. Deep learning fuzzy immersion and invariance control for type-i diabetes. Comput Biol Med 2022;149 105975.
• [13] Sun X, Rashid M, Hobbs N, Brandt R, Askari MR, Cinar A. Incorporating prior information in adaptive model predictive control for multivariable artificial pancreas systems. J Diabetes Sci Technol 2022;16(1):19–28.
• [14] Pavan J, Salvagnin D, Facchinetti A, Sparacino G, Del Favero S. Incorporating sparse and quantized carbohydrates suggestions in model predictive control for artificial pancreas in type 1 diabetes. IEEE Trans Control Syst Technol.
• [15] Gondhalekar R, Dassau E, Doyle III FJ. Velocity-weighting & velocity-penalty mpc of an artificial pancreas: Improved safety & performance. Automatica 2018;91:105–17.
• [16] Acharya D, Das DK. 1St odisha international conference on electrical power engineering. 2021 1St odisha international conference on electrical power engineering, communication and computing technology (ODICON), IEEE 2021:1–5.
• [17] Khodadadi H, Ghadiri H, Dehghani A. Improved sliding mode control for glucose regulation of type 1 diabetics patients considering delayed nonlinear model. In: Communication and Intelligent Systems. Springer; 2022. p. 1083–92.
• [18] Villarreal S, Lombeida D, Haro J, Camacho O. Design, simulation, and implementation of an artificial pancreas prototype for virtual patients with type 1 diabetes applying smc controller with anticipated carbohydrate information. In: Latest Advances in Electrical Engineering, and Electronics. Springer; 2022. p. 115–28.
• [19] Li T, Cui W, Xie N, Li H, Liu H, Li X, Wang Y. Intelligent and strong robust cvs-lvad control based on soft-actor-critic algorithm. Artif Intell Med 2022;128 102308.
• [20] Kovatchev B. Automated closed-loop control of diabetes: the artificial pancreas. Bioelectron Med 2018;4(1):1–12.
• [21] Ramkissoon CM, Aufderheide B, Bequette BW, Vehí J. A review of safety and hazards associated with the artificial pancreas. IEEE Rev Biomed Eng 2017;10:44–62.
• [22] Tabassum MF, Farman M, Naik PA, Ahmad A, Ahmad AS, et al. Modeling and simulation of glucose insulin glucagon algorithm for artificial pancreas to control the diabetes mellitus. Network Model Anal Health Inf Bioinf 2021;10 (1):1–8.
• [23] Orozco O, Castañeda CE, Rodríguez-Herrero A, García-Saéz G, Hernando ME. Luenberger observer with nonlinear structure applied to diabetes type 1. Int J Combinator Optim Problems Inf 2018;9(1):68–80.
• [24] Kovatchev B. The artificial pancreas in 2017: the year of transition from research to clinical practice. Nat Rev Endocrinol 2018;14(2):74.
• [25] Kovatchev BP, Breton M, Dalla Man C, Cobelli C. In silico preclinical trials: a proof of concept in closed-loop control of type 1 diabetes. J Diabetes Sci Technol 2009;3(1):44–55.
• [26] Visentin R, Campos-Náñez E, Schiavon M, Lv D, Vettoretti M, Breton M, Kovatchev BP, Dalla Man C, Cobelli C. The uva/padova type 1 diabetes simulator goes from single meal to single day. J Diabetes Sci Technol 2018;12(2):273–81.
• [27] Wilinska ME, Chassin LJ, Acerini CL, Allen JM, Dunger DB, Hovorka R. Simulation environment to evaluate closed-loop insulin delivery systems in type 1 diabetes. J Diabetes Sci Technol 2010;4(1):132–44.
• [28] Smaoui MR, Rabasa-Lhoret R, Haidar A. Development platform for artificial pancreas algorithms. Plos one 2020;15 (12) e0243139.
• [29] Loukianov AG. Robust block decomposition sliding mode control design. Math Problems Eng 2002;8(4–5):349–65.
• [30] Castaneda CE, Loukianov AG, Sanchez EN, Castillo-Toledo B. Discrete-time neural sliding-mode block control for a dc motor with controlled flux. IEEE Trans Ind Electron 2011;59 (2):1194–207.
• [31] Morfin OA, Valenzuela FA, Betancour RR, Castañeda CE, Ruíz-Cruz R, Valderrabano-Gonzalez A. Real-time sosm super-twisting combined with block control for regulating induction motor velocity. IEEE Access 2018;6:25898–907.
• [32] Leyva H, Carrillo FA, Quiroz G, Femat R. Robust stabilization of positive linear systems via sliding positive control. J Process Control 2016;41:47–55.
• [33] Álvaro E, Rivadeneira S, Chávez D, Camacho O, Rivadeneira PS. A sliding mode control approach for patients with type 1 diabetes. In: 2017 IEEE 3rd Colombian Conference on Automatic Control (CCAC). IEEE; 2017. p. 1–6.
• [34] Ahmad S, Ahmed N, Ilyas M, Khan W, et al. Super twisting sliding mode control algorithm for developing artificial pancreas in type 1 diabetes patients. Biomed Signal Process Control 2017;38:200–11.
• [35] Boiroux D, Bátora V, Hagdrup M, Tárnik M, Murgaš J, Schmidt S, Nørgaard K, Poulsen NK, Madsen H, Jørgensen JB. Comparison of prediction models for a dual-hormone artificial pancreas. IFAC-PapersOnLine 2015;48(20):7–12.
• [36] Isidori A, Sontag E, Thoma M. Nonlinear control systems, vol. 3. Springer; 1995.
• [37] Kazantzis N, Kravaris C. Time-discretization of nonlinear control systems via taylor methods. Comput Chem Eng 1999;23(6):763–84.
• [38] Hovorka R, Canonico V, Chassin LJ, Haueter U, Massi-Benedetti M, Federici MO, Pieber TR, Schaller HC, Schaupp L, Vering T, et al. Nonlinear model predictive control of glucose concentration in subjects with type 1 diabetes. Physiol Meas 2004;25(4):905–20.
• [39] Orozco-López O, Rodríguez-Herrero A, Castañeda CE, García-Sáez G, Hernando ME. Method to generate a large cohort insilico for type 1 diabetes. Comput Methods Programs Biomed 2020;105523.
• [40] Battelino T, Danne T, Bergenstal RM, Amiel SA, Beck R, Biester T, Bosi E, Buckingham BA, Cefalu WT, Close KL, et al. Clinical targets for continuous glucose monitoring data interpretation: recommendations from the international consensus on time in range. Diabetes Care 2019;42 (8):1593–603.
• [41] Chen C-T, Chen C-T. Linear system theory and design, vol. 301. New York: Holt, Rinehart and Winston; 1984.
• [42] Rugh WJ. Linear system theory. Prentice-Hall Inc; 1996.
• [43] Zhou B, Zhao T. On asymptotic stability of discrete-time linear time-varying systems. IEEE Trans Autom Control 2017;62(8):4274–81.
• [44] Khalil HK, Grizzle JW. Nonlinear systems, vol. 3. NJ: Prentice hall Upper Saddle River; 2002.

Uwagi

PL

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023)