Applied Biomedical Engineering Using Artificial Intelligence and Cognitive Models
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Applied Biomedical Engineering Using Artificial Intelligence and Cognitive Models focuses on the relationship between three different multidisciplinary branches of engineering: Biomedical Engineering, Cognitive Science and Computer Science through Artificial Intelligence models. These models will be used to study how the nervous system and musculoskeletal system obey movement orders from the brain, as well as the mental processes of the information during cognition when injuries and neurologic diseases are present in the human body. The interaction between these three areas are studied in this book with the objective of obtaining AI models on injuries and neurologic diseases of the human body, studying diseases of the brain, spine and the nerves that connect them with the musculoskeletal system. There are more than 600 diseases of the nervous system, including brain tumors, epilepsy, Parkinson's disease, stroke, and many others. These diseases affect the human cognitive system that sends orders from the central nervous system (CNS) through the peripheral nervous systems (PNS) to do tasks using the musculoskeletal system. These actions can be detected by many Bioinstruments (Biomedical Instruments) and cognitive device data, allowing us to apply AI using Machine Learning-Deep Learning-Cognitive Computing models through algorithms to analyze, detect, classify, and forecast the process of various illnesses, diseases, and injuries of the human body. Applied Biomedical Engineering Using Artificial Intelligence and Cognitive Models provides readers with the study of injuries, illness, and neurological diseases of the human body through Artificial Intelligence using Machine Learning (ML), Deep Learning (DL) and Cognitive Computing (CC) models based on algorithms developed with MATLAB® and IBM Watson®.
Key Features
- Provides an introduction to Cognitive science, cognitive computing and human cognitive relation to help in the solution of AI Biomedical engineering problems
- Explain different Artificial Intelligence (AI) including evolutionary algorithms to emulate natural evolution, reinforced learning, Artificial Neural Network (ANN) type and cognitive learning and to obtain many AI models for Biomedical Engineering problems
- Includes coverage of the evolution Artificial Intelligence through Machine Learning (ML), Deep Learning (DL), Cognitive Computing (CC) using MATLAB® as a programming language with many add-on MATLAB® toolboxes, and AI based commercial products cloud services as: IBM (Cognitive Computing, IBM Watson®, IBM Watson Studio®, IBM Watson Studio Visual Recognition®), and others
- Provides the necessary tools to accelerate obtaining results for the analysis of injuries, illness, and neurologic diseases that can be detected through the static, kinetics and kinematics, and natural body language data and medical imaging techniques applying AI using ML-DL-CC algorithms with the objective of obtaining appropriate conclusions to create solutions that improve the quality of life of patients
Readership
Reference for Academic research on: Biomedical Engineering, Biomechatronics, Applied Informatics, Bioinformatics, Biomechanics, Neural Engineering, Clinical Engineering, Rehabilitation Engineering, Applied Mathematics, computational intelligence, Medical devices and many others, besides specific practical applications in Human computer interaction, cognitive robotics, cognitive ergonomics and cognitive engineering.
Table of Contents
- Cover image
- Title page
- Table of Contents
- Copyright
- Dedication
- About the author
- Foreword
- Preface
- Acknowledgment
- Chapter 1. Biomedical engineering and the evolution of artificial intelligence
- Abstract
- 1.1 Introduction
- 1.2 Biomedical engineering
- 1.3 Artificial intelligence
- 1.4 Machine learning
- 1.5 Deep learning
- 1.6 Cognitive science
- 1.7 Neuroscience, cognitive science, and AI models
- References
- Chapter 2. Introduction to Cognitive Science, Cognitive Computing, and Human Cognitive relation to help in the solution of Artificial Intelligence Biomedical Engineering problems
- Abstract
- 2.1 Introduction
- 2.2 Brain, spinal cord, and nerves
- 2.3 Neurons and neural pathways in cognition
- 2.4 Cognitive science
- 2.5 Natural Language Processing
- 2.6 MATLAB® toolboxes solution for natural language processing
- 2.7 Cloud service and AI
- 2.8 IBM Cloud, IBM Watson, and Cognitive apps
- 2.9 The future of the relationship between cognitive science, cognitive computing, and human cognition
- References
- Further reading
- Chapter 3. Artificial Intelligence Models Applied to Biomedical Engineering
- Abstract
- 3.1 Introduction artificial intelligence and biomedical engineering
- 3.2 AI optimization in biomedical engineering
- 3.3 Evolutionary algorithms for AI optimization in BME
- 3.4 IBM Watson Studio for artificial intelligence
- 3.5 Examples of applications of evolutionary algorithms with other AI tools in biomedical engineering
- References
- Chapter 4. Machine Learning Models Applied to Biomedical Engineering
- Abstract
- 4.1 Introduction
- 4.2 Choosing the best ML model
- 4.3 ML clusters, classification, and regression models
- 4.4 Naive Bayes family models for supervised learning
- 4.5 k-Nearest neighbor family models for supervised learning
- 4.6 Decision trees family models for supervised learning
- 4.7 Support vector machine family members
- 4.8 Artificial neural network family models
- 4.9 Discriminant analysis family models
- 4.10 Logistic regression classifier
- 4.11 Ensemble classifiers family models
- 4.12 IBM ML Solution: IBM Watson SPSS
- References
- Further reading
- Chapter 5. Deep Learning Models Principles Applied to Biomedical Engineering
- Abstract
- 5.1 Deep learning based on artificial neural networks
- 5.2 Feed forward neural networks types
- 5.3 Shallow neural network
- 5.4 Backpropagation neural networks types
- 5.5 Transfer learning from pretrained deep learning networks
- References
- Chapter 6. Deep Learning Models Evolution Applied to Biomedical Engineering
- Abstract
- 6.1 Deep learning models evolution
- 6.2 Recurrent neural networks types
- 6.3 Memory augmented neural networks types
- 6.4 Modular Neural Networks types
- 6.5 Evolutionary Deep Neural Networks types
- References
- Further reading
- Chapter 7. Cognitive learning and reasoning models applied to biomedical engineering
- Abstract
- 7.1 Introduction
- 7.2 Artificial intelligence and Cognitive Computing Agents System (AI-CCAS)
- 7.3 Inference engine and research example
- 7.4 Action generation
- 7.5 Business intelligence in healthcare
- 7.6 Learning and reasoning relationship of biomedical engineering, cognitive science, and computer science through artificial intelligent models
- 7.7 Cognitive Learning and Reasoning research example applying AI-CCAS framework
- 7.8 Challenge research for “Applied Biomedical Engineering using Artificial Intelligence and Cognitive Models”
- References
- Further Reading
- Index
Product details
- No. of pages: 704
- Language: English
- Copyright: © Academic Press 2021
- Published: November 29, 2021
- Imprint: Academic Press
- Paperback ISBN: 9780128207185
- eBook ISBN: 9780128209349
About the Author
Jorge Garza Ulloa
Dr. Jorge Garza-Ulloa is the CEO/Director of Garzaulloa.org Research Consulting Services. He graduated from the doctoral program in Electrical & Computer Eng. at University of Texas El Paso (UTEP). Dr. Garza-Ulloa has focused his research in the development of mathematical models for Electrical and Computer Engineering: Biomedical applications, such as his Mathematical Model for the Validation of the Ground Reaction Force Sensor in Human Gait Analysis, the Mathematical model to predict Transition-to-Fatigue during isometric exercise on muscles of the lower extremities, his mathematical model to be used in the Assessment and evaluation of dynamic behavior of muscles with special reference to subjects with Diabetes Mellitus (dissertation) . Besides: his natural fuzzy logic model for differential analysis in muscle/joint activation point using infer T equation for muscle/joint, Extension of the T-Fuzzy inference equation using Fuzzy Mapping for Human Gait Analysis to identify the eight-basic pathologic gait attributed to neurological conditions (Academic press book: Applied Biomechatronic Using Mathematical Models), many others. In this book introduces: Cognitive Learning models and its relationship with neuroscience of reasoning under Cognitive Learning- Reasoning (CL&R) applying Cognitive Computing (CC), and many others to be integrated at the Proposed General Architecture framework of a Cognitive Computing Agents System (AI-CCAS). Dr. Garza-Ulloa has been the recipient of numerous honors and awards including a University of Texas at El Paso Graduate School Research Award, Research Schellenberg Foundation, Stern Foundation, Elsevier grants, and others. Dr. Garza-Ulloa is currently teaching at the University of Texas at El Paso USA and continue his Biomedical Engineering research at Research Consultant Services at El Paso Texas, USA.
Affiliations and Expertise
Research Consulting Services (garzaulloa.org) / University of Texas, El Paso, TX, USA
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