Control Applications for Biomedical Engineering Systems

Ahmad Taher Azar

• Publisher: Academic Press
• Year: 2020
• ISBN:
  9780128174623
• Topic: Computer Science
• Subtopic: Data Modelling & Design
• Index: Computer Science

Control Applications for Biomedical Engineering Systems presents different control engineering and modeling applications in the biomedical field. It is intended for senior undergraduate or graduate students in both control engineering and biomedical engineering programs. For control engineering students, it presents the application of various techniques already learned in theoretical lectures in the biomedical arena. For biomedical engineering students, it presents solutions to various problems in the field using methods commonly used by control engineers.

• Points out theoretical and practical issues to biomedical control systems
• Brings together solutions developed under different settings with specific attention to the validation of these tools in biomedical settings using real-life datasets and experiments
• Presents significant case studies on devices and applications

1

Neuro-fuzzy inverse optimal control incorporating a multistep predictor as applied to T1DM patients

Alma Y. Alanis^a; Y.Yuliana Rios^d; J.A. García-Rodríguez^a; Edgar N. Sanchez^b; E. Ruiz-Velázquez^a; Aldo Pardo Garcia^{c a} Electronics and Computing Division, CUCEI, Universidad de Guadalajara, Guadalajara, Mexico
^b Electrical Engineering Department, CINVESTAV, Zapopan, Mexico
^c A&C, Automation and Control Group, Universidad de Pamplona, Pamplona, Colombia
^d GAICO, Grupo de Automatización y Control, Universidad Tecnológica de Bolívar, Cartagena, Bolívar, Colombia

Abstract

Emerging technologies seek to provide effective solutions to the most severe health problems such as type 1 diabetes mellitus (T1DM). In fact, the number of diabetics around the world has increased as well as the mortality rate associated with this condition. T1DM is caused by an autoimmune failure which disables the pancreas to produce insulin; therefore, glucose is not correctly metabolized to be used as efficient energy. Consequently, the most important fact is to keep the patient's blood glucose level within normal ranges in order to avoid long-term complications. Recently, engineering innovative approaches based on intelligent systems such as artificial neural networks have been proposed for control in biomedical systems. In this work, a novel neuro-fuzzy control scheme for blood glucose regulation in virtual T1DM patients is proposed. The glucose-insulin dynamics is modeled by a recurrent high-order neural network and then a neural multistep predictor is incorporated in order to know the glucose behavior within a 15-min horizon; thereby, allowing the knowledge of future values to determine the convenient basal infusion insulin rate as defined by the fuzzy membership functions. Test using the well-known Uva/Padova simulator illustrated that the propos...

Table of contents

• Cover image
• Title page
• Table of Contents
• Copyright
• Contributors
• Foreword
• Preface
• 1: Neuro-fuzzy inverse optimal control incorporating a multistep predictor as applied to T1DM patients
• 2: Blood glucose regulation in patients with type 1 diabetes by means of output-feedback sliding mode control
• 3: Impulsive MPC schemes for biomedical processes: Application to type 1 diabetes
• 4: Robust control applications in biomedical engineering: Control of depth of hypnosis
• 5: Robust control strategy for HBV treatment: Considering parametric and nonparametric uncertainties
• 6: A closed loop robust control system for electrosurgical generators
• 7: Application of a T-S unknown input observer for studying sitting control for people living with spinal cord injury
• 8: Epidemic modeling and control of HIV/AIDS dynamics in populations under external interactions: A worldwide challenge
• 9: Reinforcement learning-based control of drug dosing with applications to anesthesia and cancer therapy
• 10: Control strategies in general anesthesia administration
• 11: Computational modeling of the control mechanisms involved in the respiratory system
• 12: Intelligent decision support for lung ventilation
• 13: Customized modeling and optimal control of superovulation stage in in vitro fertilization (IVF) treatment
• 14: Models based on cellular automata for the analysis of biomedical systems
• Index