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Modeling, identification and nonlinear model predictive control of type I diabetic patient.
Med Eng Phys. 2006 Apr; 28(3):240-50.ME

Abstract

Patients with type I diabetes nearly always need therapy with insulin. The most desirable treatment would be to mimic the operation of a normal pancreas. In this work a patient affected with this pathology is modeled and identified with a neural network, and a control strategy known as Nonlinear Model Predictive Control is evaluated as an approach to command an insulin pump using the subcutaneous route. A method for dealing with the problems related with the multiple insulin injections simulation and a multilayer neural network identification of the patient model is presented. The controller performance of the proposed strategy is tested under charge and reference disturbances (setpoint). Simulating an initial blood glucose concentration of 250 mg/dl a stable value of 97.0 mg/dl was reached, with a minimum level of 76.1 mg/dl. The results of a simulated 50 g oral glucose tolerance test show a maximum glucose concentration of 142.6 mg/dl with an undershoot of 76.0 mg/dl. According to the simulation results, stable close-loop control is achieved and physiological levels are reached with reasonable delays, avoiding the undesirable low glucose levels. Further studies are needed in order to deal with noise and robustness aspects, issues which are out of the scope of this work.

Authors+Show Affiliations

Schlotthauer G
Universidad Nacional de Entre Ríos, Facultad de Ingeniería, Bioingeniería, C.C. 47, Suc. 3, Paraná (3100), E.R. Argentina. gschlott@bioingenieria.edu.ar
Gamero LG
No affiliation info available
Torres ME
No affiliation info available
Nicolini GA
No affiliation info available

MeSH

Blood GlucoseComputer SimulationDiabetes Mellitus, Type 1Diagnosis, Computer-AssistedDrug Therapy, Computer-AssistedFeedbackHumansInsulinModels, BiologicalNonlinear DynamicsReproducibility of ResultsSensitivity and Specificity

Pub Type(s)

Evaluation Study
Journal Article
Research Support, Non-U.S. Gov't

Language

eng

PubMed ID

15964233

Citation

Schlotthauer, Gastón, et al. "Modeling, Identification and Nonlinear Model Predictive Control of Type I Diabetic Patient." Medical Engineering & Physics, vol. 28, no. 3, 2006, pp. 240-50.
Schlotthauer G, Gamero LG, Torres ME, et al. Modeling, identification and nonlinear model predictive control of type I diabetic patient. Med Eng Phys. 2006;28(3):240-50.
Schlotthauer, G., Gamero, L. G., Torres, M. E., & Nicolini, G. A. (2006). Modeling, identification and nonlinear model predictive control of type I diabetic patient. Medical Engineering & Physics, 28(3), 240-50.
Schlotthauer G, et al. Modeling, Identification and Nonlinear Model Predictive Control of Type I Diabetic Patient. Med Eng Phys. 2006;28(3):240-50. PubMed PMID: 15964233.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Modeling, identification and nonlinear model predictive control of type I diabetic patient. AU - Schlotthauer,Gastón, AU - Gamero,Lucas G, AU - Torres,María E, AU - Nicolini,Guido A, Y1 - 2005/06/16/ PY - 2003/10/15/received PY - 2005/03/02/revised PY - 2005/04/08/accepted PY - 2005/6/21/pubmed PY - 2006/4/1/medline PY - 2005/6/21/entrez SP - 240 EP - 50 JF - Medical engineering & physics JO - Med Eng Phys VL - 28 IS - 3 N2 - Patients with type I diabetes nearly always need therapy with insulin. The most desirable treatment would be to mimic the operation of a normal pancreas. In this work a patient affected with this pathology is modeled and identified with a neural network, and a control strategy known as Nonlinear Model Predictive Control is evaluated as an approach to command an insulin pump using the subcutaneous route. A method for dealing with the problems related with the multiple insulin injections simulation and a multilayer neural network identification of the patient model is presented. The controller performance of the proposed strategy is tested under charge and reference disturbances (setpoint). Simulating an initial blood glucose concentration of 250 mg/dl a stable value of 97.0 mg/dl was reached, with a minimum level of 76.1 mg/dl. The results of a simulated 50 g oral glucose tolerance test show a maximum glucose concentration of 142.6 mg/dl with an undershoot of 76.0 mg/dl. According to the simulation results, stable close-loop control is achieved and physiological levels are reached with reasonable delays, avoiding the undesirable low glucose levels. Further studies are needed in order to deal with noise and robustness aspects, issues which are out of the scope of this work. SN - 1350-4533 UR - https://www.unboundmedicine.com/medline/citation/15964233/Modeling_identification_and_nonlinear_model_predictive_control_of_type_I_diabetic_patient_ DB - PRIME DP - Unbound Medicine ER -