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dc.rights.licenseReconocimiento 4.0 Internacional
dc.contributor.advisorIsaza Hurtado, Jhon Alexander
dc.contributor.advisorBolaños Martínez, Freddy
dc.contributor.authorHernández Gonzalez, Cristian Mateo
dc.date.accessioned2022-03-08T20:32:54Z
dc.date.available2022-03-08T20:32:54Z
dc.date.issued2021-12-16
dc.identifier.urihttps://repositorio.unal.edu.co/handle/unal/81158
dc.descriptionIlustraciones
dc.description.abstractLa 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)
dc.description.abstractType 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 therap
dc.format.extentxiii, 77 páginas
dc.format.mimetypeapplication/pdf
dc.language.isospa
dc.publisherUniversidad Nacional de Colombia
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subject.ddc620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería
dc.titleReducción 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.typeTrabajo de grado - Maestría
dc.type.driverinfo:eu-repo/semantics/masterThesis
dc.type.versioninfo:eu-repo/semantics/acceptedVersion
dc.publisher.programMedellín - Minas - Maestría en Ingeniería - Automatización Industrial
dc.contributor.researchgroupGrupo de Investigación en Tecnologías Aplicadas Gita
dc.description.degreelevelMaestría
dc.description.degreenameMagister en Ingeniería - Automatización Industrial
dc.description.researchareaSistemas dinámicos
dc.identifier.instnameUniversidad Nacional de Colombia
dc.identifier.reponameRepositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourlhttps://repositorio.unal.edu.co/
dc.publisher.departmentDepartamento de Ingeniería Eléctrica y Automática
dc.publisher.facultyFacultad de Minas
dc.publisher.placeMedellín, Colombia
dc.publisher.branchUniversidad Nacional de Colombia - Sede Medellín
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dc.rights.accessrightsinfo:eu-repo/semantics/openAccess
dc.subject.lembRedes neuronales (Computación) - Aplicaciones
dc.subject.lembProcesamiento de señales
dc.subject.proposalEstimación de estado
dc.subject.proposalRed Neuronal
dc.subject.proposalDatos esporádicos
dc.subject.proposalType 1 diabetes mellitus
dc.subject.proposalArticial Pancreas
dc.subject.proposalState estimation
dc.subject.proposalNeural network
dc.subject.proposalSporadic measurements
dc.title.translatedReducing uncertainty in monitoring glucose levels for people with type 1 diabetes mellitus in the presence of asynchronous measurements and data loss
dc.type.coarhttp://purl.org/coar/resource_type/c_bdcc
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.contentText
dc.type.redcolhttp://purl.org/redcol/resource_type/TM
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2
oaire.awardtitleDesarrollo de un sistema integral de gestión y control de pacientes diabéticos tipo 1 para el tratamiento con y sin bomba de insulina, subvención110180763081
dcterms.audience.professionaldevelopmentInvestigadores
dc.description.curricularareaÁrea Curricular de Ingeniería Eléctrica e Ingeniería de Control


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