Control neuro-fuzzy para páncreas artificial: desarrollo y validación in-silico

Y. Rios, J. García-Rodríguez, E. Sánchez, A. Alanis, E. Ruiz-Velázquez, A. Pardo

Resumen

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.


Palabras clave

Diabetes Mellitus Tipo 1; hardware en el lazo; controlador embebido; páncreas artificial

Clasificación por materias

Teoría y Control de Sistemas; Inteligencia computacional y técnicas de supervisión y detección de fallos; Control en bioingeniería, biología, agricultura, ecología y medicina

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