TRATAMIENTO DE PACIENTES T1DM UTILIZANDO UN ALGORITMO NEURO-DIFUSO DE CONTROL ÓPTIMO INVERSO: UN ENFOQUE DE PROTOTIPADO RÁPIDO

Resumen: La condición de Diabetes Mellitus Tipo 1 (DMT1) ocurre cuando el páncreas se comporta de manera anormal e impide la producción de insulina parcialmente o totalmente. Por lo tanto, la glucosa no es metabolizada para convertirse en una fuente natural de energía y permanece en el torrente sanguíneo. Esta enfermedad causa miles de muertes alrededor del mundo. Los sectores de salud, así como la comunidad científica, han fortalecido los esfuerzos para proporcionar tratamientos más efectivos. En este trabajo, se expone un novedoso enfoque de control neuro-difuso para la regulación de la glucosa en sangre en pacientes virtuales con DMT1. La estrategia es diseñada tal que las funciones de membresía están definidas para determinar la tasa de infusión de insulina para evitar eventos de hiperglucemia e hipoglucemia. Adicionalmente, se lleva a cabo un prototipado rápido programando la ley de control óptimo inverso en la tarjeta de desarrollo LAUNCHXL-F28069M de Texas Instruments Inc. El análisis de la variabilidad de control (Siglas en inglés CVGA) obtenido a través del simulador Uva/Padova muestra claramente un desempeño satisfactorio para la reducción de hiperglucemia e hipoglucemia en una población de 10 adultos virtuales. De esta manera, el trabajo tiene como objetivo expandir la investigación de la diabetes hacia el Páncreas Artificial (PA) como un dispositivo programable

Neuro-fuzzy control for artificial pancreas: in silico development and validation

[ES] La Diabetes Mellitus Tipo 1 (DMT1) es una de las enfermedades actuales más dañinas que afectan a personas de cualquier edad incluyendo niños desde el nacimiento. Las inyecciones de insulina exógena siguen siendo el tratamiento más común para estos pacientes, sin embargo, no es el óptimo. La comunidad científica se ha esforzado en optimizar el suministro de insulina usando dispositivos electrónicos y de esta manera mejorar la esperanza de vida de los diabéticos. Existen numerosas limitaciones para que esta evolución biomédica sea realidad tales como la validación de algoritmos controladores, experimentación con dispositivos electrónicos, aplicabilidad en pacientes de diferentes edades, entre otras. Este trabajo presenta el prototipado de un controlador inteligente neuro-fuzzy en la tarjeta LAUNCHXL-F28069M de Texas Instruments para formar un esquema de hardware en el lazo (HIL). Esto es, el controlador embebido manda los datos de la tasa de suministro de insulina al computador donde se capturan por el software Uva/Padova y se integran a la simulación metabólica de pacientes diabéticos virtuales tratados con bomba de insulina. Una tarea principal del algoritmo inteligente embebido es determinar la tasa óptima de infusión insulínica para cada uno de los 30 pacientes virtuales disponibles, los cuales llevan un protocolo de comida. La novedad de este trabajo se centra en superar las limitaciones actuales a través de un primer enfoque de algoritmo de control inteligente aplicable al páncreas artificial (PA) y analizar la factibilidad de esta propuesta en la trascendencia con la edad ya que los resultados corresponden a pruebas in-silico en poblaciones de 10 adultos, 10 adolescentes y 10 niños.[EN] Type 1 Diabetes Mellitus (DMT1) is currently one of the most harmful diseases that aect 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 insulin 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 approach applicable to artificial pancreas (AP) and analyzing 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.Rios, Y.; García-Rodríguez, J.; Sánchez, E.; Alanis, A.; Ruiz-Velázquez, E.; Pardo, A. (2020). Control neuro-fuzzy para páncreas artificial: desarrollo y validación in-silico. Revista Iberoamericana de Automática e Informática industrial. 17(4):390-400. https://doi.org/10.4995/riai.2020.13035OJS390400174Alanis, A. Y., Sanchez, E. N., Loukianov, A. G., 2007. Discrete-Time Adaptive Backstepping Nonlinear Control via High-Order Neural Networks. 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