ISBN
Formato digital
979-13-87837-54-9
Fecha de publicación
06-10-2025
Licencia
D. R. © Copyright 2025. Alma Y. Alanis, Jorge Galvez, Omar Avalos, Eduardo Méndez-Palos, Jorge D. Rios, Adriana Peña Perez-Negron & Gabriel Martínez Soltero
Todos los contenidos de esta obra se comparten bajo la licencia Creative Commons Atri-bución/Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0). Esto implica que no está autorizado el uso comercial de la obra original ni de las eventuales obras derivadas, las cuales deberán distribuirse bajo la misma licencia que rige la obra original. No obstante, se permite a terceros compartir el contenido siempre y cuando se reconozca debidamente la autoría y la publicación original en esta editorial.
Pedro Misraim Gómez Rodríguez
Universidad de Guadalajara
0009-0006-3396-6292
Alma Yolanda Alanis García
Universidad de Guadalajara
0000-0001-9600-779X
Óscar Didier Sánchez Sánchez
Universidad Autónoma de Guadalajara
0000-0001-8215-6348
Hugo G. Venegas
Universidad de Guadalajara
0000-0002-4522-4097
Eduardo Méndez Palos
Universidad de Guadalajara
0000-0002-3267-024X
Acerca de
Hypertension, a significant cardiovascular disease, necessitates continuous monitoring and precise predictive models to optimize treatment strategies. This research proposes a Digital Twin for Hypertension, integrating a mathematical model based on Olufsen’s differential equations with artificial neural networks to dynamically adjust vascular resistance, arterial compliance, and blood flow inertia. This model is implemented in MATLAB®, utilizing the Runge-Kutta method to solve 11 differential equations.In contrast with conventional blood pressure monitoring techniques, which rely on discrete measurements, this approach provides real-time estimation of blood pressure fluctuations, allowing for improved risk stratification and personalized treatment recommendations. Expected results suggest that the use of the digital twin can enhance hypertension management by adapting to individual physiological conditions and predicting pressure variations more accurately than conventional methods.
Compared to existing compartmental models, this approach leverages neural networks to optimize parameter estimation, improving prediction accuracy and adaptability. However, challenges remain about acquiring real-time data and achieving adequate computational efficiency, issues which will be addressed in future research. Potential applications include integrating wearable devices for continuous blood pressure monitoring and simulation of pharmacological interventions. It is submitted that this research makes a significant contribution to electronics and communication by demonstrating how mathematical modelling and artificial intelligence can be combined to enhance medical simulations. Future research will focus on real-time validation, optimization of computational performance, and extending the model’s applicability to personalized medicine. The digital twin framework has been identified as a valuable instrument for enhancing hypertension diagnosis, treatment, and research in cardiovascular health.
Referencias
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