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Serafini MC, Rosales N, Garelli F. Auto adaptation of closed-loop insulin delivery system using continuous reward functions and incremental discretization. Comput Methods Biomech Biomed Engin 2024; 27:1375-1386. [PMID: 37545465 DOI: 10.1080/10255842.2023.2241945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 07/07/2023] [Accepted: 07/20/2023] [Indexed: 08/08/2023]
Abstract
Several closed or hybrid loop controllers for Blood Glucose (BG) regulation, which are also known as Artificial Pancreas (AP) Systems or Automated Insulin Delivery systems (AIDs), are in development worldwide. Most AIDs are designed and evaluated for short-term performance, with a particular emphasis on the post-meal period. However, if controllers are not adapted properly to account for variations in physiology that affect Insulin Sensitivity (IS), the AIDs may perform inadequately. In this work, the performance of two Reinforcement Learning (RL) agents trained under both piecewise and continuous reward functions is evaluated in-silico for long-term adaptation of a Fully Automated Insulin Delivery (fAID) system. An automatic adaptive discretization scheme that expands the state space as needed is also implemented to avoid disproportionate state space exploration. The proposed agents are evaluated for long-term adaptation of the Automatic Regulation of Glucose (ARG) algorithm, considering variations in IS. Results show that both RL agents have improved performance compared to a rule-based decision-making approach and the baseline controller for the majority of the adult population. Moreover, the use of a continuous shaped reward function proves to enhance the performance of the agents further than a piecewise one.
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Affiliation(s)
- Maria Cecilia Serafini
- Grupo de Control Aplicado, Instituto LEICI (UNLP-CONICET), Facultad de Ingeniería, Universidad Nacional de La Plata, Argentina
| | - Nicolas Rosales
- Grupo de Control Aplicado, Instituto LEICI (UNLP-CONICET), Facultad de Ingeniería, Universidad Nacional de La Plata, Argentina
| | - Fabricio Garelli
- Grupo de Control Aplicado, Instituto LEICI (UNLP-CONICET), Facultad de Ingeniería, Universidad Nacional de La Plata, Argentina
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2
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Ahmad S, Beneyto A, Zhu T, Contreras I, Georgiou P, Vehi J. An automatic deep reinforcement learning bolus calculator for automated insulin delivery systems. Sci Rep 2024; 14:15245. [PMID: 38956183 PMCID: PMC11219905 DOI: 10.1038/s41598-024-62912-4] [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] [Received: 11/10/2023] [Accepted: 05/22/2024] [Indexed: 07/04/2024] Open
Abstract
In hybrid automatic insulin delivery (HAID) systems, meal disturbance is compensated by feedforward control, which requires the announcement of the meal by the patient with type 1 diabetes (DM1) to achieve the desired glycemic control performance. The calculation of insulin bolus in the HAID system is based on the amount of carbohydrates (CHO) in the meal and patient-specific parameters, i.e. carbohydrate-to-insulin ratio (CR) and insulin sensitivity-related correction factor (CF). The estimation of CHO in a meal is prone to errors and is burdensome for patients. This study proposes a fully automatic insulin delivery (FAID) system that eliminates patient intervention by compensating for unannounced meals. This study exploits the deep reinforcement learning (DRL) algorithm to calculate insulin bolus for unannounced meals without utilizing the information on CHO content. The DRL bolus calculator is integrated with a closed-loop controller and a meal detector (both previously developed by our group) to implement the FAID system. An adult cohort of 68 virtual patients based on the modified UVa/Padova simulator was used for in-silico trials. The percentage of the overall duration spent in the target range of 70-180 mg/dL was 71.2 % and 76.2 % , < 70 mg/dL was 0.9 % and 0.1 % , and > 180 mg/dL was 26.7 % and 21.1 % , respectively, for the FAID system and HAID system utilizing a standard bolus calculator (SBC) including CHO misestimation. The proposed algorithm can be exploited to realize FAID systems in the future.
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Affiliation(s)
- Sayyar Ahmad
- Modeling and Intelligent Control Engineering Laboratory, Institute of Informatics and Applications, University of Girona, 17003, Girona, Spain
| | - Aleix Beneyto
- Modeling and Intelligent Control Engineering Laboratory, Institute of Informatics and Applications, University of Girona, 17003, Girona, Spain
| | - Taiyu Zhu
- Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Imperial College London, London, UK
| | - Ivan Contreras
- Modeling and Intelligent Control Engineering Laboratory, Institute of Informatics and Applications, University of Girona, 17003, Girona, Spain
| | - Pantelis Georgiou
- Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Imperial College London, London, UK
| | - Josep Vehi
- Modeling and Intelligent Control Engineering Laboratory, Institute of Informatics and Applications, University of Girona, 17003, Girona, Spain.
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), 28001, Madrid, Spain.
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Sanz R, García P, Romero-Vivó S, Díez JL, Bondia J. Near-optimal feedback control for postprandial glucose regulation in type 1 diabetes. ISA TRANSACTIONS 2023; 133:345-352. [PMID: 36116963 DOI: 10.1016/j.isatra.2022.06.033] [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: 07/05/2021] [Revised: 04/19/2022] [Accepted: 06/26/2022] [Indexed: 06/15/2023]
Abstract
This paper is focused on feedback control of postprandial glucose levels for patients with type 1 Diabetes Mellitus. There are two important limitations that make this a challenging problem. First, the slow subcutaneous insulin pharmacokinetics that introduces a significant lag into the control loop. Second, the positivity constraint on the control action, meaning that it is not possible to remove insulin from the body. In this paper, both issues are explicitly considered in the design process using the internal model control framework, to derive a near-optimal feedback controller. Optimality is understood here as minimizing the blood glucose peak after a meal intake and, at the same time, preventing glucose values below a prescribed threshold. It is shown how the proposed controller approaches the optimal closed-loop performance as a limit case. The theoretical results are supported by a numerical example and the feasibility of the overall strategy under uncertainties is illustrated using an extended version UVa/Padova metabolic simulator.
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Affiliation(s)
- R Sanz
- Instituto de Automática e Informática Industrial, Universitat Politècnica de València, 46022 València, Spain.
| | - P García
- Instituto de Automática e Informática Industrial, Universitat Politècnica de València, 46022 València, Spain.
| | - S Romero-Vivó
- Instituto de Matemática Multidisciplinar, Universitat Politècnica de València, 46022 València, Spain; Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas, Instituto de Salud Carlos III, 28029 Madrid, Spain.
| | - J L Díez
- Instituto de Automática e Informática Industrial, Universitat Politècnica de València, 46022 València, Spain; Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas, Instituto de Salud Carlos III, 28029 Madrid, Spain.
| | - J Bondia
- Instituto de Automática e Informática Industrial, Universitat Politècnica de València, 46022 València, Spain; Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas, Instituto de Salud Carlos III, 28029 Madrid, Spain.
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Sala-Mira I, Garcia P, Díez JL, Bondia J. Internal model control based module for the elimination of meal and exercise announcements in hybrid artificial pancreas systems. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 226:107061. [PMID: 36116400 DOI: 10.1016/j.cmpb.2022.107061] [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: 04/11/2022] [Revised: 07/15/2022] [Accepted: 08/07/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND AND OBJECTIVES Hybrid artificial pancreas systems outperform current insulin pump therapies in blood glucose regulation in type 1 diabetes. However, subjects still have to inform the system about meals intake and exercise to achieve reasonable control. These patient announcements may result in overburden and compromise controller performance if not provided timely and accurately. Here, a hybrid artificial pancreas is extended with an add-on module that releases subjects from meals and exercise announcements. METHODS The add-on module consists of an internal-model controller that generates a "virtual" control action to compensate for disturbances. This "virtual" action is converted into insulin delivery, rescue carbohydrates suggestions, or insulin-on-board limitations, depending on a switching logic based on glucose measurements and predictions. The controller parameters are tuned by optimization and then related to standard parameters from the open-loop therapy. This module is implemented in a hybrid artificial pancreas system proposed by our research group for validation. This hybrid system extended with the add-on module is compared with the hybrid controller with carbohydrate counting errors (hybrid) and the hybrid controller with an alternative unannounced meal compensation module based on a meal detection algorithm (meal detector). The validation used the educational version of the UVa/Padova simulator to simulate the three controllers under two scenarios: one with only meals and another with meals and exercise. The exercise was modeled as a temporal increase of the insulin sensitivity resulting in the glucose drop usually related to an aerobic exercise. RESULTS For the scenario with only meals, the three controllers achieved similar time in range (proposed: 85.1 [77.9,88.1]%, hybrid: 84.0 [75.9,86.4]%, meal detector: 81.9 [79.3,83.8]%, median [interquartile range]) with low time in moderate hypoglycemia. Under the scenario with meals and exercise, the proposed module reduces 4.61% the time in hypoglycemia achieved with the other controllers, suggesting an acceptable amount of rescues (27.2 [23.7, 31.0] g). CONCLUSIONS The proposed add-on module achieved promising results: it outperformed the meal-detector-based controller, even achieving a postprandial performance as good as the hybrid controller (with carbohydrate counting errors). Also, the rescue suggestion feature of the module mitigated exercise-induced hypoglycemia with admissible rescue amounts.
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Affiliation(s)
- Iván Sala-Mira
- Instituto Universitario de Automática e Informática Industrial, Universitat Politécnica de Valéncia, Valencia 46022, Spain
| | - Pedro Garcia
- Instituto Universitario de Automática e Informática Industrial, Universitat Politécnica de Valéncia, Valencia 46022, Spain
| | - José-Luis Díez
- Instituto Universitario de Automática e Informática Industrial, Universitat Politécnica de Valéncia, Valencia 46022, Spain; Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas, Instituto de Salud Carlos III, Spain
| | - Jorge Bondia
- Instituto Universitario de Automática e Informática Industrial, Universitat Politécnica de Valéncia, Valencia 46022, Spain; Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas, Instituto de Salud Carlos III, Spain.
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Fushimi E, De Battista H, Garelli F. A Dual-Hormone Multicontroller for Artificial Pancreas Systems. IEEE J Biomed Health Inform 2022; 26:4743-4750. [PMID: 35704538 DOI: 10.1109/jbhi.2022.3182581] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Artificial pancreas (AP) algorithms can be divided into single-hormone (SH) and dual-hormone (DH). SH algorithms regulate glycemia using insulin as their control input. On the other hand, DH algorithms also use glucagon to counteract insulin. While SH-AP systems are already commercially available, DH-AP systems are still in an earlier research phase. DH-AP systems have been questioned since the added complexity of glucagon infusion does not always guarantee hypoglycemia prevention and might significantly raise insulin delivery. In this work, a DH multicontroller is proposed based on a SH linear quadratic gaussian (LQG) algorithm with an additional LQG controller to deliver glucagon. This strategy has a switched structure that allows activating one of the following three controllers when necessary: a conservative insulin LQG controller to modulate basal delivery ( K1), an aggressive insulin LQG controller to counteract meals ( K2), or a glucagon LQG controller to avoid imminent hypoglycemia ( K3). Here, an in silico study of the benefits of incorporating controller K3 is carried out. Intra-patient variability and mixed meals are considered. Results indicate that the proposed switched, dual-hormone strategy yields to a reduction in hypoglycemia without increasing hyperglycemia, with no significant rise in insulin delivery.
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Rodríguez-Sarmiento DL, León-Vargas F, Garelli F. Practical constraint definition in safety schemes for artificial pancreas systems. Int J Artif Organs 2022; 45:535-542. [DOI: 10.1177/03913988221095586] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Introduction: Artificial pancreas systems usually define an insulin-on-board constraint ([Formula: see text]) for safety schemes to limit the insulin infusion and avoid hypoglycemia during the closed-loop performance. Several methods have been proposed with impractical considerations requiring information from the prandial events or complex procedures for ambulatory use. Methods: This paper presents a simple method that consists of two novel rules that allow finding an [Formula: see text] based only on common clinical parameters that do not require patient intervention. The method robustness was evaluated using a control system coupled to a safety layer under demanding scenarios implemented on the FDA-approved simulator for preclinical studies. Results: The method maintains a safe performance, even in the face of interpatient variability, hybrid and fully automatic implementations of an artificial pancreas system, and uncertain settings. Both proposed rules work as effectively or even better and without the patient intervention than other methods that have already been clinically validated. Conclusion: This method can be used to define a constant [Formula: see text] that ensures performance and safety of the control system, even under scenarios with incorrect clinical data. Unlike other methods, this method only requires reliable information that is easily obtained from the patient, such as their total daily dose of insulin or body mass.
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Affiliation(s)
- David L Rodríguez-Sarmiento
- Doctorate in Health Sciences, Universidad Antonio Nariño, Bogotá, Colombia
- Mechanical, Electronics and Biomedical Engineering Faculty, Universidad Antonio Nariño, Bogotá, Colombia
| | - Fabian León-Vargas
- Mechanical, Electronics and Biomedical Engineering Faculty, Universidad Antonio Nariño, Bogotá, Colombia
| | - Fabricio Garelli
- Engineering Faculty, Universidad Nacional de La Plata, Buenos Aires, Argentina
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Garelli F, Fushimi E, Rosales N, Arambarri D, Mendoza L, Serafini MC, Moscoso-Vásquez M, Stasi M, Duette P, García-Arabehety J, Giunta JN, De Battista H, Sánchez-Peña R, Grosembacher L. First Outpatient Clinical Trial of a Full Closed-Loop Artificial Pancreas System in South America. J Diabetes Sci Technol 2022:19322968221096162. [PMID: 35549733 DOI: 10.1177/19322968221096162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND The first two studies of an artificial pancreas (AP) system carried out in Latin America took place in 2016 (phase 1) and 2017 (phase 2). They evaluated a hybrid algorithm from the University of Virginia (UVA) and the automatic regulation of glucose (ARG) algorithm in an inpatient setting using an AP platform developed by the UVA. The ARG algorithm does not require carbohydrate (CHO) counting and does not deliver meal priming insulin boluses. Here, the first outpatient trial of the ARG algorithm using an own AP platform and doubling the duration of previous phases is presented. METHOD Phase 3 involved the evaluation of the ARG algorithm in five adult participants (n = 5) during 72 hours of closed-loop (CL) and 72 hours of open-loop (OL) control in an outpatient setting. This trial was performed with an own AP and remote monitoring platform developed from open-source resources, called InsuMate. The meals tested ranged its CHO content from 38 to 120 g and included challenging meals like pasta. Also, the participants performed mild exercise (3-5 km walks) daily. The clinical trial is registered in ClinicalTrials.gov with identifier: NCT04793165. RESULTS The ARG algorithm showed an improvement in the time in hyperglycemia (52.2% [16.3%] OL vs 48.0% [15.4%] CL), time in range (46.9% [15.6%] OL vs 50.9% [14.4%] CL), and mean glucose (188.9 [25.5] mg/dl OL vs 186.2 [24.7] mg/dl CL) compared with the OL therapy. No severe hyperglycemia or hypoglycemia episodes occurred during the trial. The InsuMate platform achieved an average of more than 95% of the time in CL. CONCLUSION The results obtained demonstrated the feasibility of outpatient full CL regulation of glucose levels involving the ARG algorithm and the InsuMate platform.
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Affiliation(s)
- Fabricio Garelli
- Grupo de Control Aplicado, Instituto LEICI (UNLP-CONICET), Facultad de Ingeniería, Universidad Nacional de La Plata, La Plata, Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas, Argentina
| | - Emilia Fushimi
- Grupo de Control Aplicado, Instituto LEICI (UNLP-CONICET), Facultad de Ingeniería, Universidad Nacional de La Plata, La Plata, Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas, Argentina
| | - Nicolás Rosales
- Grupo de Control Aplicado, Instituto LEICI (UNLP-CONICET), Facultad de Ingeniería, Universidad Nacional de La Plata, La Plata, Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas, Argentina
| | - Delfina Arambarri
- Grupo de Control Aplicado, Instituto LEICI (UNLP-CONICET), Facultad de Ingeniería, Universidad Nacional de La Plata, La Plata, Argentina
| | - Leandro Mendoza
- Grupo de Control Aplicado, Instituto LEICI (UNLP-CONICET), Facultad de Ingeniería, Universidad Nacional de La Plata, La Plata, Argentina
| | - María Cecilia Serafini
- Grupo de Control Aplicado, Instituto LEICI (UNLP-CONICET), Facultad de Ingeniería, Universidad Nacional de La Plata, La Plata, Argentina
- Comisión de Investigaciones Científicas de la Provincia de Buenos Aires, Buenos Aires, Argentina
| | - Marcela Moscoso-Vásquez
- Consejo Nacional de Investigaciones Científicas y Técnicas, Argentina
- Instituto Tecnológico de Buenos Aires, Buenos Aires, Argentina
| | | | | | | | | | - Hernán De Battista
- Grupo de Control Aplicado, Instituto LEICI (UNLP-CONICET), Facultad de Ingeniería, Universidad Nacional de La Plata, La Plata, Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas, Argentina
| | - Ricardo Sánchez-Peña
- Consejo Nacional de Investigaciones Científicas y Técnicas, Argentina
- Instituto Tecnológico de Buenos Aires, Buenos Aires, Argentina
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Rosales N, De Battista H, Garelli F. Hypoglycemia prevention: PID-type controller adaptation for glucose rate limiting in Artificial Pancreas System. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2021.103106] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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A Hybrid Automata Approach for Monitoring the Patient in the Loop in Artificial Pancreas Systems. SENSORS 2021; 21:s21217117. [PMID: 34770425 PMCID: PMC8587755 DOI: 10.3390/s21217117] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 10/18/2021] [Accepted: 10/23/2021] [Indexed: 11/16/2022]
Abstract
The use of automated insulin delivery systems has become a reality for people with type 1 diabetes (T1D), with several hybrid systems already on the market. One of the particularities of this technology is that the patient is in the loop. People with T1D are the plant to control and also a plant operator, because they may have to provide information to the control loop. The most immediate information provided by patients that affects performance and safety are the announcement of meals and exercise. Therefore, to ensure safety and performance, the human factor impact needs to be addressed by designing fault monitoring strategies. In this paper, a monitoring system is developed to diagnose potential patient modes and faults. The monitoring system is based on the residual generation of a bank of observers. To that aim, a linear parameter varying (LPV) polytopic representation of the system is adopted and a bank of Kalman filters is designed using linear matrix inequalities (LMI). The system uncertainty is propagated using a zonotopic-set representation, which allows determining confidence bounds for each of the observer outputs and residuals. For the detection of modes, a hybrid automaton model is generated and diagnosis is performed by interpreting the events and transitions within the automaton. The developed system is tested in simulation, showing the potential benefits of using the proposed approach for artificial pancreas systems.
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Nuñez S, Inthamoussou FA, Valenciaga F, De Battista H, Garelli F. Potentials of constrained sliding mode control as an intervention guide to manage COVID19 spread. Biomed Signal Process Control 2021; 67:102557. [PMID: 33727950 PMCID: PMC7945868 DOI: 10.1016/j.bspc.2021.102557] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2020] [Revised: 12/18/2020] [Accepted: 03/07/2021] [Indexed: 11/27/2022]
Abstract
This work evaluates the potential of using sliding mode reference conditioning (SMRC) techniques as a guide for non-pharmaceutical intervention (NPI) to control the COVID-19 pandemic. In particular, for the epidemiological problem addressed here, it is used to compute the contact rate reduction requirement in order to limit the infectious population to a given threshold. The SMRC controller allows the desired output variable limit and its approaching rate to be tuned explicitly. Implementation issues are taken into account and a periodically update of the NPI is proposed for the real life application. The strategy is evaluated under different scenarios where its distinctive features are exhibited.
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Affiliation(s)
- Sebastián Nuñez
- Grupo de Control Aplicado, Instituto LEICI, Facultad de Ingeniería, Universidad Nacional de La Plata - CONICET, Argentina
| | - Fernando A Inthamoussou
- Grupo de Control Aplicado, Instituto LEICI, Facultad de Ingeniería, Universidad Nacional de La Plata - CONICET, Argentina
| | - Fernando Valenciaga
- Grupo de Control Aplicado, Instituto LEICI, Facultad de Ingeniería, Universidad Nacional de La Plata - CONICET, Argentina
| | - Hernán De Battista
- Grupo de Control Aplicado, Instituto LEICI, Facultad de Ingeniería, Universidad Nacional de La Plata - CONICET, Argentina
| | - Fabricio Garelli
- Grupo de Control Aplicado, Instituto LEICI, Facultad de Ingeniería, Universidad Nacional de La Plata - CONICET, Argentina
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Viñals C, Beneyto A, Martín-SanJosé JF, Furió-Novejarque C, Bertachi A, Bondia J, Vehi J, Conget I, Giménez M. Artificial Pancreas With Carbohydrate Suggestion Performance for Unannounced and Announced Exercise in Type 1 Diabetes. J Clin Endocrinol Metab 2021; 106:55-63. [PMID: 32852548 DOI: 10.1210/clinem/dgaa562] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/06/2020] [Accepted: 08/14/2020] [Indexed: 12/16/2022]
Abstract
OBJECTIVE To evaluate the safety and performance of a new multivariable closed-loop (MCL) glucose controller with automatic carbohydrate recommendation during and after unannounced and announced exercise in adults with type 1 diabetes (T1D). RESEARCH DESIGN AND METHODS A randomized, 3-arm, crossover clinical trial was conducted. Participants completed a heavy aerobic exercise session including three 15-minute sets on a cycle ergometer with 5 minutes rest in between. In a randomly determined order, we compared MCL control with unannounced (CLNA) and announced (CLA) exercise to open-loop therapy (OL). Adults with T1D, insulin pump users, and those with hemoglobin (Hb)A1c between 6.0% and 8.5% were eligible. We investigated glucose control during and 3 hours after exercise. RESULTS Ten participants (aged 40.8 ± 7.0 years; HbA1c of 7.3 ± 0.8%) participated. The use of the MCL in both closed-loop arms decreased the time spent <70 mg/dL of sensor glucose (0.0%, [0.0-16.8] and 0.0%, [0.0-19.2] vs 16.2%, [0.0-26.0], (%, [percentile 10-90]) CLNA and CLA vs OL respectively; P = 0.047, P = 0.063) and the number of hypoglycemic events when compared with OL (CLNA 4 and CLA 3 vs OL 8; P = 0.218, P = 0.250). The use of the MCL system increased the proportion of time within 70 to 180 mg/dL (87.8%, [51.1-100] and 91.9%, [58.7-100] vs 81.1%, [65.4-87.0], (%, [percentile 10-90]) CLNA and CLA vs OL respectively; P = 0.227, P = 0.039). This was achieved with the administration of similar doses of insulin and a reduced amount of carbohydrates. CONCLUSIONS The MCL with automatic carbohydrate recommendation performed well and was safe during and after both unannounced and announced exercise, maintaining glucose mostly within the target range and reducing the risk of hypoglycemia despite a reduced amount of carbohydrate intake.Register Clinicaltrials.gov: NCT03577158.
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Affiliation(s)
- Clara Viñals
- Diabetes Unit, Endocrinology and Nutrition Department Hospital Clínic de Barcelona, Spain
| | - Aleix Beneyto
- Institute of Informatics and Applications, University of Girona, Girona, Spain
| | - Juan-Fernando Martín-SanJosé
- Instituto Universitario de Automática e Informática Industrial, Universitat Politècnica de València, València, Spain
| | - Clara Furió-Novejarque
- Instituto Universitario de Automática e Informática Industrial, Universitat Politècnica de València, València, Spain
| | - Arthur Bertachi
- Federal University of Technology-Paraná (UTFPR), Guarapuava, Brazil
| | - Jorge Bondia
- Instituto Universitario de Automática e Informática Industrial, Universitat Politècnica de València, València, Spain
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Madrid, Spain
| | - Josep Vehi
- Institute of Informatics and Applications, University of Girona, Girona, Spain
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Madrid, Spain
| | - Ignacio Conget
- Diabetes Unit, Endocrinology and Nutrition Department Hospital Clínic de Barcelona, Spain
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Madrid, Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Marga Giménez
- Diabetes Unit, Endocrinology and Nutrition Department Hospital Clínic de Barcelona, Spain
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Madrid, Spain
- Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
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12
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Automatic glycemic regulation for the pediatric population based on switched control and time-varying IOB constraints: an in silico study. Med Biol Eng Comput 2020; 58:2325-2337. [PMID: 32710375 DOI: 10.1007/s11517-020-02213-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2020] [Accepted: 06/25/2020] [Indexed: 10/23/2022]
Abstract
Artificial pancreas (AP) systems have shown to improve glucose regulation in type 1 diabetes (T1D) patients. However, full closed-loop performance remains a challenge particularly in children and adolescents, since these age groups often present the worst glycemic control. In this work, an algorithm based on switched control and time-varying IOB constraints is presented. The proposed control strategy is evaluated in silico using the FDA-approved UVA/ Padova simulator and its performance contrasted with the previously introduced Automatic Regulation of Glucose (ARG) algorithm in the pediatric population. The effect of unannounced meals is also explored. Results indicate that the proposed strategy achieves lower hypo- and hyperglycemia than the ARG for both announced and unannounced meals. Graphical Abstract Block diagram and illustrative example of insulin and glucose evolution over time for the proposed algorithm (ARGAE).
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Zhu T, Li K, Chen J, Herrero P, Georgiou P. Dilated Recurrent Neural Networks for Glucose Forecasting in Type 1 Diabetes. JOURNAL OF HEALTHCARE INFORMATICS RESEARCH 2020; 4:308-324. [PMID: 35415447 PMCID: PMC8982716 DOI: 10.1007/s41666-020-00068-2] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2018] [Revised: 01/10/2020] [Accepted: 01/15/2020] [Indexed: 12/20/2022]
Abstract
Diabetes is a chronic disease affecting 415 million people worldwide. People with type 1 diabetes mellitus (T1DM) need to self-administer insulin to maintain blood glucose (BG) levels in a normal range, which is usually a very challenging task. Developing a reliable glucose forecasting model would have a profound impact on diabetes management, since it could provide predictive glucose alarms or insulin suspension at low-glucose for hypoglycemia minimisation. Recently, deep learning has shown great potential in healthcare and medical research for diagnosis, forecasting and decision-making. In this work, we introduce a deep learning model based on a dilated recurrent neural network (DRNN) to provide 30-min forecasts of future glucose levels. Using dilation, the DRNN model gains a much larger receptive field in terms of neurons aiming at capturing long-term dependencies. A transfer learning technique is also applied to make use of the data from multiple subjects. The proposed approach outperforms existing glucose forecasting algorithms, including autoregressive models (ARX), support vector regression (SVR) and conventional neural networks for predicting glucose (NNPG) (e.g. RMSE = NNPG, 22.9 mg/dL; SVR, 21.7 mg/dL; ARX, 20.1 mg/dl; DRNN, 18.9 mg/dL on the OhioT1DM dataset). The results suggest that dilated connections can improve glucose forecasting performance efficiently.
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Dynamic Rule-Based Algorithm to Tune Insulin-on-Board Constraints for a Hybrid Artificial Pancreas System. JOURNAL OF HEALTHCARE ENGINEERING 2020; 2020:1414597. [PMID: 32399164 PMCID: PMC7201789 DOI: 10.1155/2020/1414597] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2018] [Revised: 04/16/2019] [Accepted: 05/14/2019] [Indexed: 11/18/2022]
Abstract
The artificial pancreas (AP) is a system intended to control blood glucose levels through automated insulin infusion, reducing the burden of subjects with type 1 diabetes to manage their condition. To increase patients' safety, some systems limit the allowed amount of insulin active in the body, known as insulin-on-board (IOB). The safety auxiliary feedback element (SAFE) layer has been designed previously to avoid overreaction of the controller and thus avoiding hypoglycemia. In this work, a new method, so-called “dynamic rule-based algorithm,” is presented in order to adjust the limits of IOB in real time. The algorithm is an extension of a previously designed method which aimed to adjust the limits of IOB for a meal with 60 grams of carbohydrates (CHO). The proposed method is intended to be applied on hybrid AP systems during 24 h operation. It has been designed by combining two different strategies to set IOB limits for different situations: (1) fasting periods and (2) postprandial periods, regardless of the size of the meal. The UVa/Padova simulator is considered to assess the performance of the method, considering challenging scenarios. In silico results showed that the method is able to reduce the time spent in hypoglycemic range, improving patients' safety, which reveals the feasibility of the approach to be included in different control algorithms.
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15
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Fushimi E, Colmegna P, De Battista H, Garelli F, Sánchez-Peña R. Artificial Pancreas: Evaluating the ARG Algorithm Without Meal Announcement. J Diabetes Sci Technol 2019; 13:1035-1043. [PMID: 31339059 PMCID: PMC6835180 DOI: 10.1177/1932296819864585] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Either under standard basal-bolus treatment or hybrid closed-loop control, subjects with type 1 diabetes are required to count carbohydrates (CHOs). However, CHO counting is not only burdensome but also prone to errors. Recently, an artificial pancreas algorithm that does not require premeal insulin boluses-the so-called automatic regulation of glucose (ARG)-was introduced. In its first pilot clinical study, although the exact CHO counting was not required, subjects still needed to announce the meal time and classify the meal size. METHOD An automatic switching signal generator (SSG) is proposed in this work to remove the manual mealtime announcement from the control strategy. The SSG is based on a Kalman filter and works with continuous glucose monitoring readings only. RESULTS The ARG algorithm with unannounced meals (ARGum) was tested in silico under the effect of different types of mixed meals and intrapatient variability, and contrasted with the ARG algorithm with announced meals (ARGam). Simulations reveal that, for slow-absorbing meals, the time in the euglycemic range, [70-180] mg/dL, increases using the unannounced strategy (ARGam: 78.1 [68.6-80.2]% (median [IQR]) and ARGum: 87.8 [84.5-90.6]%), while similar results were found with fast-absorbing meals (ARGam: 87.4 [86.0-88.9]% and ARGum: 87.6 [86.1-88.8]%). On the other hand, when intrapatient variability is considered, time in euglycemia is also comparable (ARGam: 81.4 [75.4-83.5]% and ARGum: 80.9 [77.0-85.1]%). CONCLUSION In silico results indicate that it is feasible to perform an in vivo evaluation of the ARG algorithm with unannounced meals.
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Affiliation(s)
- Emilia Fushimi
- Grupo de Control Aplicado (GCA), Instituto LEICI (UNLP-CONICET), Facultad de Ingeniería, Universidad Nacional de La Plata (UNLP), Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) Argentina
- Emilia Fushimi. Instituto LEICI (Grupo de Control Aplicado), Depto. Electrotecnia, Facultad de Ingeniería, Universidad Nacional de La Plata (UNLP),, Calle 48 y116, La Plata 1900, Argentina.
| | - Patricio Colmegna
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) Argentina
- University of Virginia (UVA), Center for Diabetes Technology, Charlottesville, VA, USA
- Universidad Nacional de Quilmes (UNQ), Argentina
| | - Hernán De Battista
- Grupo de Control Aplicado (GCA), Instituto LEICI (UNLP-CONICET), Facultad de Ingeniería, Universidad Nacional de La Plata (UNLP), Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) Argentina
| | - Fabricio Garelli
- Grupo de Control Aplicado (GCA), Instituto LEICI (UNLP-CONICET), Facultad de Ingeniería, Universidad Nacional de La Plata (UNLP), Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) Argentina
| | - Ricardo Sánchez-Peña
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) Argentina
- Universidad Nacional de Quilmes (UNQ), Argentina
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Moscardó V, Díez JL, Bondia J. Parallel Control of an Artificial Pancreas with Coordinated Insulin, Glucagon, and Rescue Carbohydrate Control Actions. J Diabetes Sci Technol 2019; 13:1026-1034. [PMID: 31631688 PMCID: PMC6835176 DOI: 10.1177/1932296819879093] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
BACKGROUND An artificial pancreas with insulin and glucagon delivery has the potential to reduce the risk of hypo- and hyperglycemia in people with type 1 diabetes. However, a maximum dose of glucagon of 1 mg/d is recommended, potentially still requiring rescue carbohydrates in some situations. This work presents a parallel control structure with intrinsic insulin, glucagon, and rescue carbohydrates coordination to overcome glucagon limitations when needed. METHODS The coordinated controller that combines insulin, glucagon, and rescue carbohydrate suggestions (DH-CC-CHO) was compared with the insulin and glucagon delivery coordinated controller (DH-CC). The impact of carbohydrate quantization for practical delivery was also assessed. An in silico study using the UVA-Padova simulator, extended to include exercise and various sources of variability, was performed. RESULTS DH-CC and DH-CC-CHO performed similarly with regard to mean glucose (126.25 [123.43; 130.73] vs 127.92 [123.99; 132.97] mg/dL, P = .088), time in range (93.04 [90.00; 95.92] vs 92.91 [90.05; 95.75]%, P = .508), time above 180 mg/dL (4.94 [2.72; 7.53] vs 4.99 [2.93; 7.24]%, P = .966), time below 70 mg/dL (0.61 [0.09; 1.75] vs 0.96 [0.23; 2.17]%, P = .1364), insulin delivery (43.50 [38.68; 51.75] vs 42.86 [38.58; 51.36] U/d, P = .383), and glucagon delivery (0.75 [0.40; 1.83] vs 0.76 [0.43; 0.99] mg/d, P = .407). Time below 54 mg/dL was different (0.00 [0.00; 0.05] vs 0.00 [0.00; 0.16]%, P = .036), although non-clinically significant. This was due to the carbs quantization effect in a specific patient, as no statistical difference was found when carbs were not quantized (0.00 [0.00; 0.05] vs 0.00 [0.00; 0.00]%, P = .265). CONCLUSIONS The new strategy of automatic rescue carbohydrates suggestion in coordination with insulin and glucagon delivery to overcome constraints on daily glucagon delivery was successfully evaluated in an in silico proof of concept.
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Affiliation(s)
- Vanessa Moscardó
- Instituto Universitario de Automática e Informática Industrial, Universitat Politècnica de València, Valencia, Spain
| | - José Luis Díez
- Instituto Universitario de Automática e Informática Industrial, Universitat Politècnica de València, Valencia, Spain
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Av. Monforte de Lemos, Madrid, Spain
| | - Jorge Bondia
- Instituto Universitario de Automática e Informática Industrial, Universitat Politècnica de València, Valencia, Spain
- Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Av. Monforte de Lemos, Madrid, Spain
- Jorge Bondia, Instituto Universitario de Automática e Informática Industrial, Universitat Politècnica de València, C/Camino de Vera, s/n, 46022 Valencia, Spain.
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Moscardó V, Herrero P, Díez JL, Giménez M, Rossetti P, Georgiou P, Bondia J. Coordinated dual-hormone artificial pancreas with parallel control structure. Comput Chem Eng 2019. [DOI: 10.1016/j.compchemeng.2019.06.012] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
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18
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Ramkissoon CM, Bertachi A, Beneyto A, Bondia J, Vehi J. Detection and Control of Unannounced Exercise in the Artificial Pancreas Without Additional Physiological Signals. IEEE J Biomed Health Inform 2019; 24:259-267. [PMID: 30763250 DOI: 10.1109/jbhi.2019.2898558] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The purpose of this study was to develop an algorithm that detects aerobic exercise and triggers disturbance rejection actions to prevent exercise-induced hypoglycemia. This approach can provide a solution to poor glycemic control during and after aerobic exercise, a major hindrance in the participation of exercise by patients with type 1 diabetes. This novel exercise-induced hypoglycemia reduction algorithm (EHRA) detects exercise using a threshold on a disturbance term, a parameter estimated from an augmented minimal model using an unscented Kalman filter. After detection, the EHRA triggers the following three actions: First, a carbohydrate suggestion, second, a reduction in basal insulin and the insulin-on-board maximum limit, and finally, a 30% reduction of the next insulin meal bolus. The EHRA was tested in silico using a 15-day scenario with 8 exercise sessions of 50 min at [Formula: see text] on alternating days. The EHRA was able to obtain improved results when compared to strategies with and without exercise announcement. The unannounced, announced, and EHRA strategies all obtained an overall percentage of time in range (70-180 mg/dl) of 94% and a percentage of time 70 mg/dl of 2%, 0%, and 0%, respectively. The EHRA was tested for robustness during exercise sessions of +25% and -25% intensity and results suggest that the EHRA is able to account for variability in exercise intensity, duration, and patient dynamics such as glucose uptake rate and insulin sensitivity.
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19
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Beneyto A, Vehi J. Postprandial fuzzy adaptive strategy for a hybrid proportional derivative controller for the artificial pancreas. Med Biol Eng Comput 2018; 56:1973-1986. [PMID: 29725915 DOI: 10.1007/s11517-018-1832-1] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2017] [Accepted: 04/19/2018] [Indexed: 11/24/2022]
Abstract
This paper presents a support fuzzy adaptive system for a hybrid proportional derivative controller that will refine its parameters during postprandial periods to enhance performance. Even though glucose controllers have improved over the last decade, tuning them and keeping them tuned are still major challenges. Changes in a patient's lifestyle, stress, exercise, or other activities may modify their blood glucose system, making it necessary to retune or change the insulin dosing algorithm. This paper presents a strategy to adjust the parameters of a proportional derivative controller using the so-called safety auxiliary feedback element loop for type 1 diabetic patients. The main parameters, such as the insulin on board limit and proportional gain are tuned using postprandial performance indexes and the information given by the controller itself. The adaptive and robust performance of the control algorithm was assessed "in silico" on a cohort of virtual patients under challenging realistic scenarios considering mixed meals, circadian variations, time-varying uncertainties, sensor errors, and other disturbances. The results showed that an adaptive strategy can significantly improve the performance of postprandial glucose control, individualizing the tuning by directly taking into account the intra-patient variability of type 1 patients. Graphical Abstract title: Postprandial glycaemia improvement via fuzzy adaptive control A fuzzy inference engine was implemented within a clinically tested artificial pancreas control system. The aim of the fuzzy system was to adapt controller parameters to improve postprandial blood glucose control while ensuring safety. Results show a significant improvement over time of the postprandial glucose response due to the adaptation, thus demonstrating the usefulness of the fuzzy adaptive system.
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Affiliation(s)
- Aleix Beneyto
- Institut d'Informàtica i Aplicacions, Universitat de Girona, Campus de Montilivi, s/n, Edifici P4, 17071, Girona, Spain
| | - Josep Vehi
- Institut d'Informàtica i Aplicacions, Universitat de Girona, Campus de Montilivi, s/n, Edifici P4, 17071, Girona, Spain. .,CIBERDEM, Girona, Spain.
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20
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Artificial pancreas clinical trials: Moving towards closed-loop control using insulin-on-board constraints. Biomed Signal Process Control 2018. [DOI: 10.1016/j.bspc.2018.05.009] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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21
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Hajizadeh I, Rashid M, Samadi S, Feng J, Sevil M, Hobbs N, Lazaro C, Maloney Z, Brandt R, Yu X, Turksoy K, Littlejohn E, Cengiz E, Cinar A. Adaptive and Personalized Plasma Insulin Concentration Estimation for Artificial Pancreas Systems. J Diabetes Sci Technol 2018; 12:639-649. [PMID: 29566547 PMCID: PMC6154239 DOI: 10.1177/1932296818763959] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
BACKGROUND The artificial pancreas (AP) system, a technology that automatically administers exogenous insulin in people with type 1 diabetes mellitus (T1DM) to regulate their blood glucose concentrations, necessitates the estimation of the amount of active insulin already present in the body to avoid overdosing. METHOD An adaptive and personalized plasma insulin concentration (PIC) estimator is designed in this work to accurately quantify the insulin present in the bloodstream. The proposed PIC estimation approach incorporates Hovorka's glucose-insulin model with the unscented Kalman filtering algorithm. Methods for the personalized initialization of the time-varying model parameters to individual patients for improved estimator convergence are developed. Data from 20 three-days-long closed-loop clinical experiments conducted involving subjects with T1DM are used to evaluate the proposed PIC estimation approach. RESULTS The proposed methods are applied to the clinical data containing significant disturbances, such as unannounced meals and exercise, and the results demonstrate the accurate real-time estimation of the PIC with the root mean square error of 7.15 and 9.25 mU/L for the optimization-based fitted parameters and partial least squares regression-based testing parameters, respectively. CONCLUSIONS The accurate real-time estimation of PIC will benefit the AP systems by preventing overdelivery of insulin when significant insulin is present in the bloodstream.
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Affiliation(s)
- Iman Hajizadeh
- Department of Chemical and Biological
Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Mudassir Rashid
- Department of Chemical and Biological
Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Sediqeh Samadi
- Department of Chemical and Biological
Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Jianyuan Feng
- Department of Chemical and Biological
Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Mert Sevil
- Department of Biomedical Engineering,
Illinois Institute of Technology, Chicago, IL, USA
| | - Nicole Hobbs
- Department of Biomedical Engineering,
Illinois Institute of Technology, Chicago, IL, USA
| | - Caterina Lazaro
- Department of Electrical and Computer
Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Zacharie Maloney
- Department of Biomedical Engineering,
Illinois Institute of Technology, Chicago, IL, USA
| | - Rachel Brandt
- Department of Biomedical Engineering,
Illinois Institute of Technology, Chicago, IL, USA
| | - Xia Yu
- School of Information Science and
Technology, Northeastern University, Shenyang, China
| | - Kamuran Turksoy
- Department of Biomedical Engineering,
Illinois Institute of Technology, Chicago, IL, USA
| | - Elizabeth Littlejohn
- Department of Pediatrics and Medicine,
Section of Endocrinology, Kovler Diabetes Center, University of Chicago, Chicago,
IL, USA
| | - Eda Cengiz
- Department of Pediatrics, Yale
University School of Medicine, New Haven, CT, USA
| | - Ali Cinar
- Department of Chemical and Biological
Engineering, Illinois Institute of Technology, Chicago, IL, USA
- Department of Biomedical Engineering,
Illinois Institute of Technology, Chicago, IL, USA
- Ali Cinar, PhD, Illinois Institute of
Technology, Department of Chemical and Biological Engineering, 10 W 33rd St,
Chicago, IL 60616, USA.
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22
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Bertachi A, Beneyto A, Ramkissoon CM, Vehí J. Assessment of Mitigation Methods to Reduce the Risk of Hypoglycemia for Announced Exercise in a Uni-hormonal Artificial Pancreas. Diabetes Technol Ther 2018; 20:285-295. [PMID: 29608335 DOI: 10.1089/dia.2017.0392] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
BACKGROUND Moderate physical activity improves overall health conditions in subjects with type 1 diabetes. However, insulin management during and after exercise is challenging due to the effects of exercise on glycemic control. Artificial pancreas (AP) systems aim to automatically control blood glucose levels, but exercise-induced hypoglycemia is a major challenge for these systems, especially in uni-hormonal configurations. The aim of this work was to evaluate the ability of several feed-forward (FF) actions to prevent exercise-induced hypoglycemia in a closed-loop setting. METHODS A closed-loop control algorithm combined with FF actions aimed at eliminating exercise-induced hypoglycemia was evaluated in silico using the UVa/Padova type 1 diabetes simulator. The simulator was modified with an exercise model fitted to clinical data. The FF actions were evaluated in two scenarios: (1) exercise sessions during postprandial period and (2) exercise sessions during fasting period. RESULTS The mitigation methods proposed in this work were able to minimize the occurrence of hypoglycemic events related with exercise in both scenarios. The time spent in hypoglycemic range in the 2-h period after exercise decreased from 33.3% to 0.0% (P < 0.01) and from 41.3% to 0.0% (P < 0.01) in both scenarios tested. Besides that, the occurrence of hypoglycemic events after exercise sessions was also reduced. CONCLUSIONS The combination of the FF actions presented in this article within an AP system showed to be an effective strategy to mitigate the risk of hypoglycemia in front of aerobic exercise.
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Affiliation(s)
- Arthur Bertachi
- 1 Institute of Informatics and Applications, University of Girona , Girona, Spain
- 2 Federal University of Technology - Paraná (UTFPR) , Guarapuava, Brazil
| | - Aleix Beneyto
- 1 Institute of Informatics and Applications, University of Girona , Girona, Spain
| | | | - Josep Vehí
- 1 Institute of Informatics and Applications, University of Girona , Girona, Spain
- 3 Centro de Investigación Biomédica en Red de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM) , Madrid, Spain
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23
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Djouima M, Azar AT, Drid S, Mehdi D. Higher Order Sliding Mode Control for Blood Glucose Regulation of Type 1 Diabetic Patients. INTERNATIONAL JOURNAL OF SYSTEM DYNAMICS APPLICATIONS 2018. [DOI: 10.4018/ijsda.2018010104] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Type 1 diabetes mellitus (T1DM) treatment depends on the delivery of exogenous insulin to obtain near normal glucose levels. This article proposes a method for blood glucose level regulation in type 1 diabetics. The control strategy is based on comparing the first order sliding mode control (FOSMC) with a higher order SMC based on the super twisting control algorithm. The higher order sliding mode is used to overcome chattering, which can induce some undesirable and harmful phenomena for human health. In order to test the controller in silico experiments, Bergman's minimal model is used for studying the dynamic behavior of the glucose and insulin inside human body. Simulation results are presented to validate the effectiveness and the good performance of this control technique. The obtained results clearly reveal improved performance of the proposed higher order SMC in regulating the blood glucose level within the normal glycemic range in terms of accuracy and robustness.
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Affiliation(s)
- Mounir Djouima
- Electronics Department, LEA, University of Batna 2, Mostafa Benboulaid, Batna, Algeria
| | - Ahmad Taher Azar
- Faculty of Computers and Information, Benha University, Benha, Egypt & School of Engineering and Applied Sciences, Nile University, Giza, Egypt
| | - Saïd Drid
- LSP-IE, University of Batna 2, Batna, Mostafa Benboulaid, Algeria
| | - Driss Mehdi
- University of Poitiers, Poitiers Cedex, France
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24
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Hajizadeh I, Rashid M, Turksoy K, Samadi S, Feng J, Frantz N, Sevil M, Cengiz E, Cinar A. Plasma Insulin Estimation in People with Type 1 Diabetes Mellitus. Ind Eng Chem Res 2017. [DOI: 10.1021/acs.iecr.7b01618] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
| | | | | | | | | | | | | | - Eda Cengiz
- Department
of Pediatrics, Yale University School of Medicine, New Haven, Connecticut 06437-2411, United States
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25
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Modeling the Error of the Medtronic Paradigm Veo Enlite Glucose Sensor. SENSORS 2017; 17:s17061361. [PMID: 28604634 PMCID: PMC5492301 DOI: 10.3390/s17061361] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/24/2017] [Revised: 05/16/2017] [Accepted: 06/03/2017] [Indexed: 02/04/2023]
Abstract
Continuous glucose monitors (CGMs) are prone to inaccuracy due to time lags, sensor drift, calibration errors, and measurement noise. The aim of this study is to derive the model of the error of the second generation Medtronic Paradigm Veo Enlite (ENL) sensor and compare it with the Dexcom SEVEN PLUS (7P), G4 PLATINUM (G4P), and advanced G4 for Artificial Pancreas studies (G4AP) systems. An enhanced methodology to a previously employed technique was utilized to dissect the sensor error into several components. The dataset used included 37 inpatient sessions in 10 subjects with type 1 diabetes (T1D), in which CGMs were worn in parallel and blood glucose (BG) samples were analyzed every 15 ± 5 min Calibration error and sensor drift of the ENL sensor was best described by a linear relationship related to the gain and offset. The mean time lag estimated by the model is 9.4 ± 6.5 min. The overall average mean absolute relative difference (MARD) of the ENL sensor was 11.68 ± 5.07% Calibration error had the highest contribution to total error in the ENL sensor. This was also reported in the 7P, G4P, and G4AP. The model of the ENL sensor error will be useful to test the in silico performance of CGM-based applications, i.e., the artificial pancreas, employing this kind of sensor.
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Rossetti P, Quirós C, Moscardó V, Comas A, Giménez M, Ampudia-Blasco FJ, León F, Montaser E, Conget I, Bondia J, Vehí J. Closed-Loop Control of Postprandial Glycemia Using an Insulin-on-Board Limitation Through Continuous Action on Glucose Target. Diabetes Technol Ther 2017; 19:355-362. [PMID: 28459603 DOI: 10.1089/dia.2016.0443] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
BACKGROUND Postprandial (PP) control remains a challenge for closed-loop (CL) systems. Few studies with inconsistent results have systematically investigated the PP period. OBJECTIVE To compare a new CL algorithm with current pump therapy (open loop [OL]) in the PP glucose control in type 1 diabetes (T1D) subjects. METHODS A crossover randomized study was performed in two centers. Twenty T1D subjects (F/M 13/7, age 40.7 ± 10.4 years, disease duration 22.6 ± 9.9 years, and A1c 7.8% ± 0.7%) underwent an 8-h mixed meal test on four occasions. In two (CL1/CL2), after meal announcement, a bolus was given followed by an algorithm-driven basal infusion based on continuous glucose monitoring (CGM). Alternatively, in OL1/OL2 conventional pump therapy was used. Main outcome measures were as follows: glucose variability, estimated with the coefficient of variation (CV) of the area under the curve (AUC) of plasma glucose (PG) and CGM values, and from the analysis of the glucose time series; mean, maximum (Cmax), and time to Cmax glucose concentrations and time in range (<70, 70-180, >180 mg/dL). RESULTS CVs of the glucose AUCs were low and similar in all studies (around 10%). However, CL achieved greater reproducibility and better PG control in the PP period: CL1 = CL2<OL1<OL2 (PGmean 123 ± 47 and 125 ± 44 vs. 152 ± 53 and 159 ± 54 mg/dL) and Cmax OL 217.1 ± 67.0 mg/dL versus CL 183.3 ± 63.9 mg/dL, P < 0.0001. Time-in-range was higher with CL versus OL (80% vs. 64%; P < 0.001). Neither the time below 70 mg/dL (CL 6.1% vs. OL 3.2%; P > 0.05) nor the need for oral glucose was significantly different (CL 40.0% vs. OL 22.5% of meals; P = 0.054). CONCLUSIONS This novel CL algorithm effectively and consistently controls PP glucose excursions without increasing hypoglycemia. Study registered at ClinicalTrials.gov : study number NCT02100488.
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Affiliation(s)
- Paolo Rossetti
- 1 Internal Medicine Department, Hospital Francesc de Borja , Gandía, Spain
| | - Carmen Quirós
- 2 Diabetes Unit, Endocrinology Department, Hospital Clínic i Universitari , Barcelona, Spain
| | - Vanessa Moscardó
- 3 Instituto Universitario de Automática e Informática Industrial, Universitat Politècnica de València , Valencia, Spain
| | - Anna Comas
- 4 Institute of Informatics and Applications, University of Girona , Girona, Spain
| | - Marga Giménez
- 2 Diabetes Unit, Endocrinology Department, Hospital Clínic i Universitari , Barcelona, Spain
| | - F Javier Ampudia-Blasco
- 5 Diabetes Reference Unit, Endocrinology and Nutrition Department, Hospital Clínico Universitario de Valencia , Valencia, Spain
| | - Fabián León
- 4 Institute of Informatics and Applications, University of Girona , Girona, Spain
| | - Eslam Montaser
- 3 Instituto Universitario de Automática e Informática Industrial, Universitat Politècnica de València , Valencia, Spain
| | - Ignacio Conget
- 2 Diabetes Unit, Endocrinology Department, Hospital Clínic i Universitari , Barcelona, Spain
| | - Jorge Bondia
- 3 Instituto Universitario de Automática e Informática Industrial, Universitat Politècnica de València , Valencia, Spain
| | - Josep Vehí
- 4 Institute of Informatics and Applications, University of Girona , Girona, Spain
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Blauw H, Keith-Hynes P, Koops R, DeVries JH. A Review of Safety and Design Requirements of the Artificial Pancreas. Ann Biomed Eng 2016; 44:3158-3172. [PMID: 27352278 PMCID: PMC5093196 DOI: 10.1007/s10439-016-1679-2] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2016] [Accepted: 06/13/2016] [Indexed: 01/03/2023]
Abstract
As clinical studies with artificial pancreas systems for automated blood glucose control in patients with type 1 diabetes move to unsupervised real-life settings, product development will be a focus of companies over the coming years. Directions or requirements regarding safety in the design of an artificial pancreas are, however, lacking. This review aims to provide an overview and discussion of safety and design requirements of the artificial pancreas. We performed a structured literature search based on three search components—type 1 diabetes, artificial pancreas, and safety or design—and extended the discussion with our own experiences in developing artificial pancreas systems. The main hazards of the artificial pancreas are over- and under-dosing of insulin and, in case of a bi-hormonal system, of glucagon or other hormones. For each component of an artificial pancreas and for the complete system we identified safety issues related to these hazards and proposed control measures. Prerequisites that enable the control algorithms to provide safe closed-loop control are accurate and reliable input of glucose values, assured hormone delivery and an efficient user interface. In addition, the system configuration has important implications for safety, as close cooperation and data exchange between the different components is essential.
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Affiliation(s)
- Helga Blauw
- Department of Endocrinology, Academic Medical Center, University of Amsterdam, P.O Box 22660, 1100 DD, Amsterdam, The Netherlands. .,Inreda Diabetic BV, Goor, The Netherlands.
| | - Patrick Keith-Hynes
- TypeZero Technologies, LLC, Charlottesville, VA, USA.,Center for Diabetes Technology, University of Virginia, Charlottesville, VA, USA
| | | | - J Hans DeVries
- Department of Endocrinology, Academic Medical Center, University of Amsterdam, P.O Box 22660, 1100 DD, Amsterdam, The Netherlands
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León-Vargas F, Garelli F, De Battista H, Vehí J. Postprandial response improvement via safety layer in closed-loop blood glucose controllers. Biomed Signal Process Control 2015. [DOI: 10.1016/j.bspc.2014.10.003] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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29
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Abstract
Continuous subcutaneous insulin infusion pumps and continuous glucose monitors enable individuals with type 1 diabetes to achieve tighter blood glucose control and are critical components in a closed-loop artificial pancreas. Insulin infusion sets can fail and continuous glucose monitor sensor signals can suffer from a variety of anomalies, including signal dropout and pressure-induced sensor attenuations. In addition to hardware-based failures, software and human-induced errors can cause safety-related problems. Techniques for fault detection, safety analyses, and remote monitoring techniques that have been applied in other industries and applications, such as chemical process plants and commercial aircraft, are discussed and placed in the context of a closed-loop artificial pancreas.
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van Heusden K, West N, Umedaly A, Ansermino J, Merchant R, Dumont G. Safety, constraints and anti-windup in closed-loop anesthesia. ACTA ACUST UNITED AC 2014. [DOI: 10.3182/20140824-6-za-1003.01337] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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