Reducción de la incertidumbre en el monitoreo de los niveles de glucosa para personas con diabetes mellitus tipo 1 en presencia de mediciones asíncronas y pérdida de datos
Ilustraciones
- Autores:
-
Hernández Gonzalez, Cristian Mateo
- Tipo de recurso:
- Fecha de publicación:
- 2021
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/81158
- Palabra clave:
- 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería
Redes neuronales (Computación) - Aplicaciones
Procesamiento de señales
Estimación de estado
Red Neuronal
Datos esporádicos
Type 1 diabetes mellitus
Articial Pancreas
State estimation
Neural network
Sporadic measurements
- Rights
- openAccess
- License
- Reconocimiento 4.0 Internacional
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| dc.title.spa.fl_str_mv |
Reducción de la incertidumbre en el monitoreo de los niveles de glucosa para personas con diabetes mellitus tipo 1 en presencia de mediciones asíncronas y pérdida de datos |
| dc.title.translated.eng.fl_str_mv |
Reducing uncertainty in monitoring glucose levels for people with type 1 diabetes mellitus in the presence of asynchronous measurements and data loss |
| title |
Reducción de la incertidumbre en el monitoreo de los niveles de glucosa para personas con diabetes mellitus tipo 1 en presencia de mediciones asíncronas y pérdida de datos |
| spellingShingle |
Reducción de la incertidumbre en el monitoreo de los niveles de glucosa para personas con diabetes mellitus tipo 1 en presencia de mediciones asíncronas y pérdida de datos 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería Redes neuronales (Computación) - Aplicaciones Procesamiento de señales Estimación de estado Red Neuronal Datos esporádicos Type 1 diabetes mellitus Articial Pancreas State estimation Neural network Sporadic measurements |
| title_short |
Reducción de la incertidumbre en el monitoreo de los niveles de glucosa para personas con diabetes mellitus tipo 1 en presencia de mediciones asíncronas y pérdida de datos |
| title_full |
Reducción de la incertidumbre en el monitoreo de los niveles de glucosa para personas con diabetes mellitus tipo 1 en presencia de mediciones asíncronas y pérdida de datos |
| title_fullStr |
Reducción de la incertidumbre en el monitoreo de los niveles de glucosa para personas con diabetes mellitus tipo 1 en presencia de mediciones asíncronas y pérdida de datos |
| title_full_unstemmed |
Reducción de la incertidumbre en el monitoreo de los niveles de glucosa para personas con diabetes mellitus tipo 1 en presencia de mediciones asíncronas y pérdida de datos |
| title_sort |
Reducción de la incertidumbre en el monitoreo de los niveles de glucosa para personas con diabetes mellitus tipo 1 en presencia de mediciones asíncronas y pérdida de datos |
| dc.creator.fl_str_mv |
Hernández Gonzalez, Cristian Mateo |
| dc.contributor.advisor.none.fl_str_mv |
Isaza Hurtado, Jhon Alexander Bolaños Martínez, Freddy |
| dc.contributor.author.none.fl_str_mv |
Hernández Gonzalez, Cristian Mateo |
| dc.contributor.researchgroup.spa.fl_str_mv |
Grupo de Investigación en Tecnologías Aplicadas Gita |
| dc.subject.ddc.spa.fl_str_mv |
620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería |
| topic |
620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería Redes neuronales (Computación) - Aplicaciones Procesamiento de señales Estimación de estado Red Neuronal Datos esporádicos Type 1 diabetes mellitus Articial Pancreas State estimation Neural network Sporadic measurements |
| dc.subject.lemb.none.fl_str_mv |
Redes neuronales (Computación) - Aplicaciones Procesamiento de señales |
| dc.subject.proposal.spa.fl_str_mv |
Estimación de estado Red Neuronal Datos esporádicos |
| dc.subject.proposal.eng.fl_str_mv |
Type 1 diabetes mellitus Articial Pancreas State estimation Neural network Sporadic measurements |
| description |
Ilustraciones |
| publishDate |
2021 |
| dc.date.issued.none.fl_str_mv |
2021-12-16 |
| dc.date.accessioned.none.fl_str_mv |
2022-03-08T20:32:54Z |
| dc.date.available.none.fl_str_mv |
2022-03-08T20:32:54Z |
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Trabajo de grado - Maestría |
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info:eu-repo/semantics/masterThesis |
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info:eu-repo/semantics/acceptedVersion |
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http://purl.org/redcol/resource_type/TM |
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acceptedVersion |
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https://repositorio.unal.edu.co/handle/unal/81158 |
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Universidad Nacional de Colombia |
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Repositorio Institucional Universidad Nacional de Colombia |
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https://repositorio.unal.edu.co/ |
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https://repositorio.unal.edu.co/handle/unal/81158 https://repositorio.unal.edu.co/ |
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Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia |
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spa |
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spa |
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[Abuin et al., 2020] Abuin, P., Rivadeneira, P. S., Ferramosca, A., and González, A. H. (2020). Artificial pancreas under stable pulsatile mpc: Improving the closed-loop per- formance. Journal of Process Control, 92:246–260. [Academia, 2020] Academia, R. (2020). Diccionario de la lengua española. accessed in 20- 05-2021 to urlhttps://dle.rae.es//. [Adair, 2021] Adair, D. H. C. (2021). Blood sugar testing. accessed in 20-05-2021 to urlhttps://adair.lphamo.org/blood-sugar/. [Adeel et al., 2020] Adeel, M., Rahman, M. M., Caligiuri, I., Canzonieri, V., Rizzolio, F., and Daniele, S. (2020). Recent advances of electrochemical and optical enzyme-free glucose sensors operating at physiological conditions. Biosensors and Bioelectronics, page 112331. Aggarwal et al., 2018] Aggarwal, C. C. et al. (2018). Neural networks and deep learning. Springer, 10:978–3. [Aguirre Zapata, 2018] Aguirre Zapata, E. (2018). Estimación de estados para una clase de sistemas no lineales con retardo en la medición. caso de estudio: mecanismo homeostático de la glucosa en pacientes con diabetes mellitus tipo 1. Repositorio Instituto Tecnológico Metropolitano [Ajit Gopalakrishnan et al., 2011] Ajit Gopalakrishnan, N. S. K. et al. (2011). Incorporating delayed and infrequent measurements in extended kalman filter based nonlinear state estimation. Journal of Process Control, 21:119–129 [Albers et al., 2017] Albers, D. J., Levine, M., Gluckman, B., Ginsberg, H., Hripcsak, G., and Mamykina, L. (2017). Personalized glucose forecasting for type 2 diabetes using data assimilation. PLoS computational biology, 13(4) [Ali et al., 2015] Ali, J. M., Hoang, N. H., Hussain, M. A., and Dochain, D. (2015). Review and classification of recent observers applied in chemical process systems. Computers & Chemical Engineering, 76:27–41 [Alor Sandoval, 2000] Alor Sandoval, P. F. (2000). 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(2019). Committed moving horizon estimation for meal detection and estimation in type 1 diabetes. In 2019 American Control Conference (ACC), pages 4765–4772. IEEE [Copp et al., 2018] Copp, D. A., Gondhalekar, R., and Hespanha, J. P. (2018). Simultaneous model predictive control and moving horizon estimation for blood glucose regulation in type 1 diabetes. Optimal Control Applications and Methods, 39(2):904–918. [Davis et al., 2021] Davis, G. M., Migdal, A., Urrutia, M. a., Zamudio-Coronado, k. W., Perez-Guzman, M. C., Albury, B. S., Cardona, S., Galindo, R. J., Idrees, T., Vellanki P., et al. (2021). 619-p: Comparison of dexcom g6 continuous glucose monitoring and point-of-care blood glucose testing in hospitalized patients with diabetes. 70(1) [Derdzinski et al., 2019] Derdzinski, M., Welsh, J., Puhr, S., Walker, T. C., Parker, A., and Jimenez, A. (2019). 391-p: Hypoglycemia reductions with the dexcom g6 cgm system’s predictive alert. 68(1) [Eiben and Smith, 2015] Eiben, A. 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Univariate data-driven models for glucose level prediction of cgm sensor dataset for t1dm management. S ̄adhan ̄a, 45(1):46 [Ramosaj and Pauly, 2019] Ramosaj, B. and Pauly, M. (2019). Predicting missing values: a comparative study on non-parametric approaches for imputation. Computational Statistics, 34(4):1741–1764 [Rawlings and Bakshi, 2006] Rawlings, J. B. and Bakshi, B. R. (2006). Particle filtering and moving horizon estimation. Computers & chemical engineering, 30(10-12):1529–1541 [Razavi-Far et al., 2020] Razavi-Far, R., Cheng, B., Saif, M., and Ahmadi, M. (2020). Similarity-learning information-fusion schemes for missing data imputation. Knowledge Based Systems, 187:104805 [Ruan et al., 2016] Ruan, Y., Wilinska, M. E., Thabit, H., and Hovorka, R. (2016). Modeling day-to-day variability of glucose–insulin regulation over 12-week home use of closed-loop insulin delivery. IEEE Transactions on Biomedical Engineering, 64(6):1412–1419. [Rutledge et al., 2020] Rutledge, K., Yong, S. Z., and Ozay, N. (2020). Finite horizon constrained control and bounded-error estimation in the presence of missing data. Nonlinear Analysis: Hybrid Systems, 36:100854. [Samadi et al., 2017] Samadi, S., Turksoy, K., Hajizadeh, I., Feng, J., Sevil, M., and Cinar, A. (2017). Meal detection and carbohydrate estimation using continuous glucose sensor data. IEEE journal of biomedical and health informatics, 21(3):619–627 [Sanchis et al., 2007] Sanchis, R., Pe ̃narrocha, I., and Albertos, P. (2007). Design of robust output predictors under scarce measurements with time-varying delays. Automatica, 43(2):281–289 [Shiloh et al., 2002] Shiloh, S., Salton, E., and Sharabi, D. (2002). Individual differences in rational and intuitive thinking styles as predictors of heuristic responses and framing effects. Personality and Individual Differences, 32(3):415–429 [Song and Zheng, 2019] Song, X. and Zheng, W. X. (2019). 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Type-1 diabetes patient decision simulator for in silico testing safety and effectiveness of insulin treatments. IEEE Transactions on Biomedical Engineering, 65(6):1281–1290 [Villaverde, 2019] Villaverde, A. F. (2019). Observability and structural identifiability of nonlinear biological systems. Complexity, 2019 [Wang et al., 2018] Wang, H., Li, C., Wang, Y., Li, Q., and Ban, X. (2018). A new state estimation method with radar measurement missing. Complexity, 2018 [Webster and Eren, 2018] Webster, J. G. and Eren, H. (2018). Measurement, Instrumentation, and Sensors Handbook: Two-Volume Set. CRC press [Welch et al., 2001] Welch, G., Bishop, G., et al. (2001). An introduction to the kalman filter. Proc of SIGGRAPH, Course, 8(27599-23175):41 [Wilinska et al., 2010] Wilinska, M. E., Chassin, L. J., Acerini, C. L., Allen, J. M., Dunger, D. B., and Hovorka, R. (2010). Simulation environment to evaluate closed-loop insulin delivery systems in type 1 diabetes. Journal of diabetes science and technology, 4(1):132– 144 [Wynn et al., 2014] Wynn, A., Vukov, M., and Diehl, M. (2014). Convergence guarantees for moving horizon estimation based on the real-time iteration scheme. IEEE Transactions on Automatic Control, 59(8):2215–2221 Yu et al., 2018] Yu, X., Rashid, M., Feng, J., Hobbs, N., Hajizadeh, I., Samadi, S., Sevil, M., Lazaro, C., Maloney, Z., Littlejohn, E., et al. (2018). Online glucose prediction using computationally efficient sparse kernel filtering algorithms in type-1 diabetes. IEEE Transactions on Control Systems Technology [Zhang et al., 2017] Zhang, D., Nguang, S. K., Srinivasan, D., and Yu, L. (2017). Distri- buted filtering for discrete-time t–s fuzzy systems with incomplete measurements. IEEE Transactions on Fuzzy Systems, 26(3):1459–1471 [Zhao et al., 2019] Zhao, L., Wang, J., Yu, T., Chen, K., and Su, A. (2019). Incorporating delayed measurements in an improved high-degree cubature kalman filter for the nonlinear state estimation of chemical processes. ISA transactions, 86:122–133. |
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Reconocimiento 4.0 Internacionalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Isaza Hurtado, Jhon Alexander3d32e88de0744d07cb9ce2c5d5801386600Bolaños Martínez, Freddy26acc197708015c3bb4c52ad1cc87d7a600Hernández Gonzalez, Cristian Mateo0ce3ef5be7d1e66e90e248567a8d4631Grupo de Investigación en Tecnologías Aplicadas Gita2022-03-08T20:32:54Z2022-03-08T20:32:54Z2021-12-16https://repositorio.unal.edu.co/handle/unal/81158Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/IlustracionesLa diabetes Mellitus tipo 1 es una enfermedad en la cual el sistema homeostático de la glucosa se ve interrumpido debido a una respuesta autoinmune del cuerpo donde se destruyen las células beta del páncreas. Los tratamientos disponibles para ésta enfermedad son la terapia funcional de insulina y el tratamiento por medio de páncreas artificial. Estos dos tratamientos tienen como fundamento el monitoreo continuo de los niveles de glucosa. Dicho monitoreo se realiza por medio de monitores continuos de glucosa y/o glucómetros. El monitor continuo de glucosa toma mediciones periódicas mientras que el glucómetro toma mediciones esporádicas sincrónicas y asíncronas. Sin embargo, éste monitoreo no es preciso debido a fallas de los sensores, desconexión o mala manipulación del usuario, generando incertidumbre. Para abordar éste problema, en éste trabajo se diseñaron 3 técnicas para disminuir la incertidumbre en el monitoreo de los niveles de glucosa. Primero se plantearon cuatro instancias de medición (menor, mayor, intermedia y nula), con el fin de generar una representación matemática, gráfica y verbal de los problemas que se presentan en la medición. Como segundo paso se implementó un estimador tipo filtro de Kalman con aumento de estado el cual fusiona mediciones periódicas y esporádicas sincrónicas. De igual forma, se implementó un estimador basado en métodos de optimización (estimador de horizonte móvil) para fusiones datos periódicos y esporádicos. Además, basados en el aprendizaje de maquina se diseñó una Red Neuronal, la cual es capaz de entregar una señal aproximada de los datos reales, cuando se pierde la señal. Estos dos métodos, el estimador de horizonte móvil y la Red Neuronal se integraron con el fin de abordar las cuatro instancias de medición. Dicha integración permitió disminuir la incertidumbre en el monitoreo de los niveles de glucosa mejorando el índice de convergencia respecto a los método presentes en la literatura, permitiendo tener una aproximación más confiable de los niveles de glucosa para ejercer acciones de control, diagnosticar e implementar una terapia. (texto tomado de la fuente)Type 1 Diabetes Mellitus is a disease in which the glucose homeostatic system is disrupted due to an autoimmune response of the body where the beta cells of the pancreas are destroyed. The treatments available for this disease are functional insulin therapy and artificial pancreas treatment. These two treatments are based on continuous monitoring of glucose levels. Said monitoring is carried out by means of continuous glucose monitors and/or glucometers. The continuous glucose monitor takes periodic measurements while the glucometer takes sporadic synchronous and asynchronous measurements. However, this monitoring is not accurate due to sensor failures, disconnection or mishandling by the user, generating uncertainty. To address these problems in this work, 3 techniques were designed to reduce the uncertainty in the monitoring of glucose levels. First, four instances of measurement were proposed (minor, major, intermediate and null), in order to generate a athematical, graphic and verbal representation of the problems that arise in the measurement. As a second step, a Kalman filter-type estimator with increased state was implemented, which merges periodic and sporadic synchronous measurements. Similarly, an estimator based on optimization methods (mobile horizon estimator) was implemented for periodic and sporadic data mergers. In addition, based on machine learning, a Neural Network was designed, which is capable of delivering an approximate signal of the real data, when the signal is lost. These two methods, the mobile horizon estimator and the Neural Network, were integrated in order to address the four measurement instances. This integration made it possible to reduce the uncertainty in the monitoring of glucose levels, improving the convergence index with respect to the methods present in the literature, allowing to have a more reliable approximation of glucose levels to carry out control actions, diagnose and implement a therapMaestríaMagister en Ingeniería - Automatización IndustrialSistemas dinámicosÁrea Curricular de Ingeniería Eléctrica e Ingeniería de Controlxiii, 77 páginasapplication/pdfspaUniversidad Nacional de ColombiaMedellín - Minas - Maestría en Ingeniería - Automatización IndustrialDepartamento de Ingeniería Eléctrica y AutomáticaFacultad de MinasMedellín, ColombiaUniversidad Nacional de Colombia - Sede Medellín620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingenieríaRedes neuronales (Computación) - AplicacionesProcesamiento de señalesEstimación de estadoRed NeuronalDatos esporádicosType 1 diabetes mellitusArticial PancreasState estimationNeural networkSporadic measurementsReducción de la incertidumbre en el monitoreo de los niveles de glucosa para personas con diabetes mellitus tipo 1 en presencia de mediciones asíncronas y pérdida de datosReducing uncertainty in monitoring glucose levels for people with type 1 diabetes mellitus in the presence of asynchronous measurements and data lossTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TM[Abuin et al., 2020] Abuin, P., Rivadeneira, P. 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Incorporating delayed measurements in an improved high-degree cubature kalman filter for the nonlinear state estimation of chemical processes. ISA transactions, 86:122–133.Desarrollo de un sistema integral de gestión y control de pacientes diabéticos tipo 1 para el tratamiento con y sin bomba de insulina, subvención110180763081InvestigadoresORIGINAL1214727829.2021.pdf1214727829.2021.pdfTesis Maestría en Ingeniería - Automatización Industrialapplication/pdf4815597https://repositorio.unal.edu.co/bitstream/unal/81158/3/1214727829.2021.pdf092316d9ed1d700e1b383345f921d83eMD53LICENSElicense.txtlicense.txttext/plain; charset=utf-84074https://repositorio.unal.edu.co/bitstream/unal/81158/4/license.txt8153f7789df02f0a4c9e079953658ab2MD54THUMBNAIL1214727829.2021.pdf.jpg1214727829.2021.pdf.jpgGenerated Thumbnailimage/jpeg5315https://repositorio.unal.edu.co/bitstream/unal/81158/5/1214727829.2021.pdf.jpg780a53e5bf38e897620bfb998f184080MD55unal/81158oai:repositorio.unal.edu.co:unal/811582023-08-09 08:03:02.458Repositorio Institucional Universidad Nacional de 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