Neuro-fuzzy control for artificial pancreas: In silico development and validation
Type 1 Diabetes Mellitus (DMT1) is currently one of the most harmful diseases that affect people of any age, including children from birth. Exogenous insulin injections remain the most common treatment for these patients, however, it is not the optimal one. The scientific community has endeavored to...
- Autores:
-
Rios, Y.
García-Rodríguez, J.
Sanchez, E.
Alanis, A.
Ruiz-Velázquez, E.
Pardo, A.
- Tipo de recurso:
- Fecha de publicación:
- 2020
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/12400
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/12400
- Palabra clave:
- Glucose; Hypoglycemia;
Insulin Dependent Diabetes Mellitus
LEMB
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
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Neuro-fuzzy control for artificial pancreas: In silico development and validation |
| dc.title.alternative.es_CO.fl_str_mv |
Control neuro-fuzzy para páncreas artificial: Desarrollo y validación in-silico |
| title |
Neuro-fuzzy control for artificial pancreas: In silico development and validation |
| spellingShingle |
Neuro-fuzzy control for artificial pancreas: In silico development and validation Glucose; Hypoglycemia; Insulin Dependent Diabetes Mellitus LEMB |
| title_short |
Neuro-fuzzy control for artificial pancreas: In silico development and validation |
| title_full |
Neuro-fuzzy control for artificial pancreas: In silico development and validation |
| title_fullStr |
Neuro-fuzzy control for artificial pancreas: In silico development and validation |
| title_full_unstemmed |
Neuro-fuzzy control for artificial pancreas: In silico development and validation |
| title_sort |
Neuro-fuzzy control for artificial pancreas: In silico development and validation |
| dc.creator.fl_str_mv |
Rios, Y. García-Rodríguez, J. Sanchez, E. Alanis, A. Ruiz-Velázquez, E. Pardo, A. |
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Rios, Y. García-Rodríguez, J. Sanchez, E. Alanis, A. Ruiz-Velázquez, E. Pardo, A. |
| dc.subject.keywords.es_CO.fl_str_mv |
Glucose; Hypoglycemia; Insulin Dependent Diabetes Mellitus |
| topic |
Glucose; Hypoglycemia; Insulin Dependent Diabetes Mellitus LEMB |
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LEMB |
| description |
Type 1 Diabetes Mellitus (DMT1) is currently one of the most harmful diseases that affect people of any age, including children from birth. Exogenous insulin injections remain the most common treatment for these patients, however, it is not the optimal one. The scientific community has endeavored to optimize i nsulin administration using electronic devices and thus improve the diabetics life expectancy. There are numerous limitations for this biomedical evolution to become a reality such as the control algorithms validation, experimentation with electronic devices, and applicability in patients age transcendence, among others. This work presents the prototyping of a neuro-fuzzy intelligent controller on the Texas Instruments LAUNCHXL-F28069M development board to form a hardware in the loop (HIL) scheme. That is, the embedded controller sends the insulin delivery rate data to the computer where it is captured by the Uva/Padova software and integrated into the metabolic simulation of virtual diabetic patients treated with an insulin pump. The main task of the embedded intelligent algorithm is to determine the optimal insulin infusion rate for each of the 30 virtual patients who follow a meal protocol. The novelty of this work focuses on overcoming current limitations through a first intelligent control algorithm a pproach applicable to artificial pancreas (A P) and an alyzing the feasibility of this proposal in age transcendence since the results correspond to in-silico tests in populations of 10 adults, 10 adolescents and 10 children. © 2020 Universitat Politecnica de Valencia. All rights reserved. |
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2020 |
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2020 |
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2023-07-21T20:52:43Z |
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2023-07-21T20:52:43Z |
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2023 |
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Rios, Y., García-Rodríguez, J., Sanchez, E., Alanis, A., Ruiz-Velazquez, E., & Pardo, A. (2020). Neuro-fuzzy control for artificial pancreas: in silico development and validation. Revista Iberoamericana De Automatica e Informatica Industrial, 17(4), 390-400. |
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https://hdl.handle.net/20.500.12585/12400 |
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10.7326/0003-4819-157-5-201209040-00508 |
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Universidad Tecnológica de Bolívar |
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Repositorio Universidad Tecnológica de Bolívar |
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Rios, Y., García-Rodríguez, J., Sanchez, E., Alanis, A., Ruiz-Velazquez, E., & Pardo, A. (2020). Neuro-fuzzy control for artificial pancreas: in silico development and validation. Revista Iberoamericana De Automatica e Informatica Industrial, 17(4), 390-400. 10.7326/0003-4819-157-5-201209040-00508 Universidad Tecnológica de Bolívar Repositorio Universidad Tecnológica de Bolívar |
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Rios, Y.e5931605-385a-49c3-aa72-10bea548d473García-Rodríguez, J.dad588f6-98cc-4964-8952-54fae7514277Sanchez, E.e8f403d0-19da-43e0-9b66-7b894d1f9fa0Alanis, A.196ea685-074f-4634-b57b-b34966aa28edRuiz-Velázquez, E.c52110a4-da5c-4ebe-acb8-77dc239458f5Pardo, A.8ce2fae6-f9b5-436d-9e84-8cb7a13cf4602023-07-21T20:52:43Z2023-07-21T20:52:43Z20202023Rios, Y., García-Rodríguez, J., Sanchez, E., Alanis, A., Ruiz-Velazquez, E., & Pardo, A. (2020). Neuro-fuzzy control for artificial pancreas: in silico development and validation. Revista Iberoamericana De Automatica e Informatica Industrial, 17(4), 390-400.https://hdl.handle.net/20.500.12585/1240010.7326/0003-4819-157-5-201209040-00508Universidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarType 1 Diabetes Mellitus (DMT1) is currently one of the most harmful diseases that affect people of any age, including children from birth. Exogenous insulin injections remain the most common treatment for these patients, however, it is not the optimal one. The scientific community has endeavored to optimize i nsulin administration using electronic devices and thus improve the diabetics life expectancy. There are numerous limitations for this biomedical evolution to become a reality such as the control algorithms validation, experimentation with electronic devices, and applicability in patients age transcendence, among others. This work presents the prototyping of a neuro-fuzzy intelligent controller on the Texas Instruments LAUNCHXL-F28069M development board to form a hardware in the loop (HIL) scheme. That is, the embedded controller sends the insulin delivery rate data to the computer where it is captured by the Uva/Padova software and integrated into the metabolic simulation of virtual diabetic patients treated with an insulin pump. The main task of the embedded intelligent algorithm is to determine the optimal insulin infusion rate for each of the 30 virtual patients who follow a meal protocol. The novelty of this work focuses on overcoming current limitations through a first intelligent control algorithm a pproach applicable to artificial pancreas (A P) and an alyzing the feasibility of this proposal in age transcendence since the results correspond to in-silico tests in populations of 10 adults, 10 adolescents and 10 children. © 2020 Universitat Politecnica de Valencia. All rights reserved.11 páginasapplication/pdfspahttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://purl.org/coar/access_right/c_abf2RIAI - Revista Iberoamericana de Automatica e Informatica IndustrialNeuro-fuzzy control for artificial pancreas: In silico development and validationControl neuro-fuzzy para páncreas artificial: Desarrollo y validación in-silicoinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/drafthttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/version/c_b1a7d7d4d402bccehttp://purl.org/coar/resource_type/c_2df8fbb1Glucose; Hypoglycemia;Insulin Dependent Diabetes MellitusLEMBCartagena de IndiasAlanis, A.Y., Sanchez, E.N., Loukianov, A.G. Discrete-time adaptive backstepping nonlinear control via high-order neural networks (2007) IEEE Transactions on Neural Networks, 18 (4), pp. 1185-1195. Cited 151 times. doi: 10.1109/TNN.2007.899170Yang, W., Dall, T.M., Halder, P., Gallo, P., Kowal, S.L., Hogan, P.F., Petersen, M. Economic costs of diabetes in the U.S. in 2012 (2013) Diabetes Care, 36 (4), pp. 1033-1046. Cited 1864 times. http://care.diabetesjournals.org/content/36/4/1033.full.pdf doi: 10.2337/dc12-2625Brown, J.B., Pedula, K.L., Bakst, A.W. The progressive cost of complications in type 2 diabetes mellitus (1999) Archives of Internal Medicine, 159 (16), pp. 1873-1880. Cited 197 times. doi: 10.1001/archinte.159.16.1873Chang, F.-J., Chiang, Y.-M., Chang, L.-C. Multi-step-ahead neural networks for flood forecasting (2007) Hydrological Sciences Journal, 52 (1), pp. 114-130. Cited 115 times. doi: 10.1623/hysj.52.1.114Chen, P.-A., Chang, L.-C., Chang, F.-J. Reinforced recurrent neural networks for multi-step-ahead flood forecasts (2013) Journal of Hydrology, 497, pp. 71-79. Cited 108 times. doi: 10.1016/j.jhydrol.2013.05.038The Effect of Intensive Treatment of Diabetes on the Development and Progression of Long-Term Complications in Insulin-Dependent Diabetes Mellitus (Open Access) (1993) New England Journal of Medicine, 329 (14), pp. 977-986. Cited 23060 times. doi: 10.1056/NEJM199309303291401Geman, O., Chiuchisan, I., Toderean, R. Application of Adaptive Neuro-Fuzzy Inference System for diabetes classification and prediction (2017) 2017 E-Health and Bioengineering Conference, EHB 2017, art. no. 7995505, pp. 639-642. Cited 30 times. ISBN: 978-153860358-1 doi: 10.1109/EHB.2017.7995505Karahoca, A., Karahoca, D., Kara, A. Diagnosis of diabetes by using adaptive neuro fuzzy inference systems (Open Access) (2009) ICSCCW 2009 - 5th International Conference on Soft Computing, Computing with Words and Perceptions in System Analysis, Decision and Control, art. no. 5379497. Cited 11 times. ISBN: 978-142443428-2 doi: 10.1109/ICSCCW.2009.5379497Kim, S. Burden of hospitalizations primarily due to uncontrolled diabetes: Implications of inadequate primary health care in the United States (Open Access) (2007) Diabetes Care, 30 (5), pp. 1281-1282. Cited 99 times. http://care.diabetesjournals.org/cgi/reprint/30/5/1281 doi: 10.2337/dc06-2070Kovatchev, B.P., Breton, M., Dalla Man, C., Cobelli, C. In silico preclinical trials: A proof of concept in closed-loop control of type 1 diabetes (Open Access) (2009) Journal of Diabetes Science and Technology, 3 (1), pp. 44-55. Cited 595 times. http://dst.sagepub.com/content/by/year doi: 10.1177/193229680900300106Kropff, J., Del Favero, S., Place, J., Toffanin, C., Visentin, R., Monaro, M., Messori, M., (...), Magni, L. 2 month evening and night closed-loop glucose control in patients with type 1 diabetes under free-living conditions: A randomised crossover trial (2015) The Lancet Diabetes and Endocrinology, 3 (12), pp. 939-947. Cited 185 times. http://www.journals.elsevier.com/the-lancet-diabetes-and-endocrinology doi: 10.1016/S2213-8587(15)00335-6Lekkas, S., Mikhailov, L. Evolving fuzzy medical diagnosis of Pima Indians diabetes and of dermatological diseases (Open Access) (2010) Artificial Intelligence in Medicine, 50 (2), pp. 117-126. Cited 76 times. doi: 10.1016/j.artmed.2010.05.007Li, W., Todorov, E., Liu, D. Inverse optimality design for biological movement systems (2011) IFAC Proceedings Volumes (IFAC-PapersOnline), 44 (1 PART 1), pp. 9662-9667. Cited 25 times. http://www.ifac-papersonline.net/browser?browse=c ISBN: 978-390266193-7 doi: 10.3182/20110828-6-IT-1002.00877Nath, A., Dey, R., Balas, V.E. Closed loop blood glucose regulation of type 1 diabetic patient using Takagi-Sugeno fuzzy logic control (2018) Advances in Intelligent Systems and Computing, 634, pp. 286-296. Cited 10 times. http://www.springer.com/series/11156 ISBN: 978-331962523-2 doi: 10.1007/978-3-319-62524-9_23Ornelas, F., Sanchez, E.N., Loukianov, A.G. Discrete-time nonlinear systems inverse optimal control: A control Lyapunov function approach (Open Access) (2011) Proceedings of the IEEE International Conference on Control Applications, art. no. 6044461, pp. 1431-1436. Cited 33 times. ISBN: 978-145771062-9 doi: 10.1109/CCA.2011.6044461Ornelas-Tellez, F., Sanchez, E.N., Loukianov, A.G., Navarro-Lopez, E.M. Speed-gradient inverse optimal control for discrete-time nonlinear systems (2011) Proceedings of the IEEE Conference on Decision and Control, art. no. 6160374, pp. 290-295. Cited 27 times. ISBN: 978-161284800-6 doi: 10.1109/CDC.2011.6160374Pes, P., Herrero, P., Reddy, M., Xenou, M., Oliver, N., Johnston, D., Toumazou, C., (...), Georgiou, P. An advanced bolus calculator for type 1 diabetes: System architecture and usability results (Open Access) (2016) IEEE Journal of Biomedical and Health Informatics, 20 (1), art. no. 7174940, pp. 11-17. Cited 40 times. http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6221020 doi: 10.1109/JBHI.2015.2464088Rios, Y.Y., Garcia-Rodriguez, J.A., Sanchez, E.N., Alanis, A.Y., Ruiz-Velazquez, E. Rapid Prototyping of Neuro-Fuzzy Inverse Optimal Control as Applied to T1DM Patients (2018) 2018 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2018, art. no. 8625241. Cited 7 times. http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=8609898 ISBN: 978-153864625-0 doi: 10.1109/LA-CCI.2018.8625241Rios, Y.Y., García-Rodríguez, J.A., Sánchez, O.D., Sanchez, E.N., Alanis, A.Y., Ruiz-Velázquez, E., Arana-Daniel, N. Inverse Optimal Control Using A Neural Multi-Step Predictor for T1DM Treatment (Open Access) (2018) Proceedings of the International Joint Conference on Neural Networks, 2018-July, art. no. 8489197. Cited 9 times. ISBN: 978-150906014-6 doi: 10.1109/IJCNN.2018.8489197Sanchez, E.N., Ornelas-Tellez, F. Discrete-time inverse optimal control for nonlinear systems (2017) Discrete-Time Inverse Optimal Control for Nonlinear Systems, pp. 1-232. Cited 38 times. http://www.tandfebooks.com/doi/book/10.1201/b14779 ISBN: 978-146658088-6; 978-146658087-9 doi: 10.1201/b14779Thabit, H., Hovorka, R. Coming of age: the artificial pancreas for type 1 diabetes (Open Access) (2016) Diabetologia, 59 (9), pp. 1795-1805. Cited 171 times. link.springer.de/link/service/journals/00125/index.htm doi: 10.1007/s00125-016-4022-4Turksoy, K., Samadi, S., Feng, J., Littlejohn, E., Quinn, L., Cinar, A. Meal detection in patients with type 1 diabetes: A new module for the multivariable adaptive artificial pancreas control system (Open Access) (2016) IEEE Journal of Biomedical and Health Informatics, 20 (1), art. no. 7124410, pp. 47-54. Cited 102 times. http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6221020 doi: 10.1109/JBHI.2015.2446413Yeh, H.-C., Brown, T.T., Maruthur, N., Ranasinghe, P., Berger, Z., Suh, Y.D., Wilson, L.M., (...), Golden, S.H. Comparative effectiveness and safety of methods of insulin delivery and glucose monitoring for diabetes mellitus: A systematic review and meta-analysis (Open Access) (2012) Annals of Internal Medicine, 157 (5), pp. 336-347. 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