Biosignal Processing and Classification Using Computational Learning and Intelligence
Principles, Algorithms, and Applications
- 1st Edition - September 18, 2021
- Imprint: Academic Press
- Editors: Alejandro A. Torres-García, Carlos Alberto Reyes Garcia, Luis Villasenor-Pineda, Omar Mendoza-Montoya
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 8 2 0 1 2 5 - 1
- eBook ISBN:9 7 8 - 0 - 1 2 - 8 2 0 4 2 8 - 3
Biosignal Processing and Classification Using Computational Learning and Intelligence: Principles, Algorithms and Applications posits an approach for biosignal processing and… Read more
Purchase options
Institutional subscription on ScienceDirect
Request a sales quoteBiosignal Processing and Classification Using Computational Learning and Intelligence: Principles, Algorithms and Applications posits an approach for biosignal processing and classification using computational learning and intelligence, highlighting that the term biosignal refers to all kinds of signals that can be continuously measured and monitored in living beings. The book is composed of five relevant parts. Part One is an introduction to biosignals and Part Two describes the relevant techniques for biosignal processing, feature extraction and feature selection/dimensionality reduction. Part Three presents the fundamentals of computational learning (machine learning). Then, the main techniques of computational intelligence are described in Part Four. The authors focus primarily on the explanation of the most used methods in the last part of this book, which is the most extensive portion of the book. This part consists of a recapitulation of the newest applications and reviews in which these techniques have been successfully applied to the biosignals’ domain, including EEG-based Brain-Computer Interfaces (BCI) focused on P300 and Imagined Speech, emotion recognition from voice and video, leukemia recognition, infant cry recognition, EEGbased ADHD identification among others.
- Provides coverage of the fundamentals of signal processing, including sensing the heart, sending the brain, sensing human acoustic, and sensing other organs
- Includes coverage biosignal pre-processing techniques such as filtering, artifiact removal, and feature extraction techniques such as Fourier transform, wavelet transform, and MFCC
- Covers the latest techniques in machine learning and computational intelligence, including Supervised Learning, common classifiers, feature selection, dimensionality reduction, fuzzy logic, neural networks, Deep Learning, bio-inspired algorithms, and Hybrid Systems
- Written by engineers to help engineers, computer scientists, researchers, and clinicians understand the technology and applications of computational learning to biosignal processing
Biomedical Engineers and researchers in neural engineering, medical imaging, machine learning, neural networks, and computational intelligence. Students, researchers and clinicians in oncology, epilepsy, and a variety of other specialties
- Cover image
- Title page
- Table of Contents
- Copyright
- List of figures
- List of contributors
- About the authors
- Alejandro A. Torres-García
- Carlos A. Reyes-García
- Luis Villaseñor-Pineda
- Omar Mendoza-Montoya
- Part 1: Introduction
- Chapter 1: Introduction to this book
- Abstract
- 1.1. Brief description of the contents of this book
- 1.2. What are the applications and reviews presented in this book?
- 1.3. Who should read this book?
- Chapter 2: Biosignals analysis (heart, phonatory system, and muscles)
- Abstract
- 2.1. Introduction
- 2.2. Sensing the heart
- 2.3. Sensing human acoustics
- 2.4. Electromyography
- References
- Chapter 3: Neuroimaging techniques
- Abstract
- Acknowledgements
- 3.1. Introduction
- 3.2. The electroencephalogram
- 3.3. Functional near-infrared spectroscopy (fNIRS)
- 3.4. Biosignals in conventional and functional magnetic resonance imaging
- 3.5. Conclusion
- References
- Part 2: Biosignal processing: From biosignals to features' datasets
- Chapter 4: Pre-processing and feature extraction
- Abstract
- 4.1. Preprocessing
- 4.2. Time-domain feature extraction techniques
- 4.3. Frequency-domain feature extraction techniques
- 4.4. Time-frequency-domain feature extraction techniques
- 4.5. Wavelet transform
- 4.6. Empirical mode decomposition
- 4.7. Extracting features
- References
- Chapter 5: Dimensionality reduction
- Abstract
- Acknowledgements
- 5.1. Introduction
- 5.2. Background
- 5.3. Feature selection
- 5.4. Feature transformation
- 5.5. Final remarks
- References
- Part 3: Computational learning (machine learning)
- Chapter 6: A brief introduction to supervised, unsupervised, and reinforcement learning
- Abstract
- 6.1. Brief history of the area
- 6.2. Machine learning
- 6.3. Conclusions
- References
- Chapter 7: Assessing classifier's performance
- Abstract
- 7.1. Introduction
- 7.2. Evaluation methods
- 7.3. Bootstrapping sampling
- 7.4. Evaluation metrics of a classifier
- 7.5. Statistical significance
- 7.6. Interpreting results
- 7.7. A brief summary
- References
- Part 4: Computational intelligence
- Chapter 8: Fuzzy logic and fuzzy systems
- Abstract
- 8.1. Fuzzy logic
- 8.2. Fuzzy relations
- 8.3. Fuzzy inference systems
- References
- Chapter 9: Neural networks and deep learning
- Abstract
- 9.1. Introduction
- 9.2. Convolutional neural networks
- 9.3. Recurrent neural networks
- 9.4. Conclusions
- References
- Chapter 10: Spiking neural networks and dendrite morphological neural networks: an introduction
- Abstract
- Acknowledgements
- 10.1. Introduction
- 10.2. Spiking neural networks
- 10.3. Dendrite morphological neural networks
- 10.4. Chapter conclusions
- References
- Chapter 11: Bio-inspired algorithms
- Abstract
- 11.1. Introduction
- 11.2. Genetic algorithms (GA)
- 11.3. Particle swarm optimization (PSO)
- 11.4. Ant colony optimization (ACO)
- 11.5. Cuckoo search (CS)
- 11.6. Artificial bee colony (ABC)
- 11.7. Flower pollination algorithm (FPA)
- 11.8. Summary
- References
- Part 5: Applications and reviews
- Chapter 12: A survey on EEG-based imagined speech classification
- Abstract
- Acknowledgements
- 12.1. Introduction
- 12.2. Background: the indirect methods
- 12.3. Imagined speech classification
- 12.4. Discussion
- 12.5. Conclusions
- 12.6. Future work
- References
- Chapter 13: P300-based brain–computer interface for communication and control
- Abstract
- 13.1. Introduction
- 13.2. The P300-oddball paradigm for brain–computer interfaces
- 13.3. Hardware and software components of the BCI
- 13.4. System evaluation
- 13.5. Discussion
- References
- Chapter 14: EEG-based subject identification with multi-class classification
- Abstract
- Acknowledgements
- 14.1. Introduction
- 14.2. Method
- 14.3. Experiments and results
- 14.4. Discussion
- References
- Chapter 15: Emotion recognition: from speech and facial expressions
- Abstract
- 15.1. Introduction
- 15.2. Emotion models
- 15.3. Data generation
- 15.4. Speech emotion recognition
- 15.5. Facial expression recognition
- 15.6. Conclusions
- References
- Chapter 16: Trends and applications of ECG analysis and classification
- Abstract
- 16.1. Introduction
- 16.2. ECG signal preprocessing
- 16.3. Methods for heartbeat segmentation
- 16.4. Example of preprocessing in ECG signal and detection of QRS complex
- References
- Chapter 17: Analysis and processing of infant cry for diagnosis purposes
- Abstract
- 17.1. Infant cry anatomical background
- 17.2. Nature of the infant cry
- 17.3. Qualitative features
- 17.4. Infant cry analysis techniques
- 17.5. Relevant acoustic characteristics for the analysis of infant crying
- 17.6. Patterns and classes
- 17.7. One example of infant cry recognition with deep learning
- References
- Chapter 18: Physics augmented classification of fNIRS signals
- Abstract
- Acknowledgements
- 18.1. Introduction
- 18.2. Improving classification by designing ad-hoc quantization
- 18.3. Improving classification by altering the cross-talk during image acquisition
- 18.4. Improving classification by means of regularization of the inverse problem
- 18.5. Improving classification by altering the experimental design
- 18.6. Conclusions
- References
- Chapter 19: Evaluation of mechanical variables by registration and analysis of electromyographic activity
- Abstract
- Acknowledgements
- 19.1. Introduction
- 19.2. Methodology
- 19.3. Signal processing: feature extraction
- 19.4. Results
- 19.5. Conclusions
- References
- Chapter 20: A review on machine learning techniques for acute leukemia classification
- Abstract
- 20.1. Introduction
- 20.2. Acute leukemia classification
- 20.3. Machine learning techniques applied to classify acute leukemia
- 20.4. Remarks and directions
- 20.5. Conclusions
- References
- Chapter 21: Attention deficit and hyperactivity disorder classification with EEG and machine learning
- Abstract
- 21.1. Introduction
- 21.2. EEG analysis
- 21.3. Diagnosis of attention deficit and hyperactivity disorder
- 21.4. Research review
- 21.5. Conclusions
- References
- Chapter 22: Representation for event-related fMRI
- Abstract
- Acknowledgements
- 22.1. Background
- 22.2. Related works
- 22.3. Materials and methods
- 22.4. Results
- 22.5. Discussion
- 22.6. Conclusion
- References
- Index
- Edition: 1
- Published: September 18, 2021
- No. of pages (Paperback): 536
- No. of pages (eBook): 536
- Imprint: Academic Press
- Language: English
- Paperback ISBN: 9780128201251
- eBook ISBN: 9780128204283
AT
Alejandro A. Torres-García
Dr. Alejandro A. Torres-García is a researcher and a member of the Mexican National System of Researchers Level-1 (2021-2023). His research interests are; biosignals processing and analysis, brain-computer interfaces, silent speech interfaces, machine learning, computational intelligence, and computational thinking. He holds a Ph. D degree in Computer Sciences from the Instituto Nacional de Astrofísica Óptica y Electrónica in Puebla, Mexico. Also, he was an ERCIM postdoctoral researcher at the Norwegian University of Science and Technology in Trondheim, Norway (2019-2020). He has published one book, two book chapters, and about 30 articles in scientific journals and proceedings of national and international conferences. Furthermore, he has done shorts stays as visiting researcher at Freie Universität Berlin (Germany in 2014 and 2015), Università Degli Studi di Firenze (Italy in 2016), Universidad de Jaén (Spain in 2017 and 2018), and Institut National de Recherche en Informatique et en Automatique (INRIA, FRANCE in 2019). He is also a member of the CONACYT thematic networks on Applied Computational Intelligence, and Language Technologies.
Affiliations and expertise
Research Project Collaborator, Instituto Nacional de Astrofísica Optica y Electronica, Puebla, MexicoCR
Carlos Alberto Reyes Garcia
Carlos Alberto Reyes García Garcia is a full-time researcher in the Department of Computer Science, the head of the Bio signal Processing and Medical Computing laboratory, and is the founding Coordinator of the Graduate Program in Biomedical Sciences and Technologies as of August of 2017. at the Instituto Nacional de Astrofísica Óptica y Electrónica in Puebla, Mexico since January 2001. He holds a PhD degree in computer science with a specialty in artificial intelligence from Florida State University in Tallahassee, Florida. He is a Level II National Researcher of the National System of Researchers (SNI). He is the national president of the Thematic Network on Applied Computational Intelligence from 2016 to date, IEEE Senior Member and AMEXCOMP invited member He was President of the board of directors of the Mexican Society of Artificial Intelligence (SMIA) and now is an Emeritus Member. His areas of particular research interest are; Computational Intelligence, Bio signal Processing and Classification, Processing, Analysis and Classification of Speech, Analysis and Recognition of Baby's Cry, and Classification of Patterns in General.
Affiliations and expertise
Full-Time Researcher, Department of Computer Science, Instituto Nacional de Astrofisica Optica y Electronica, Puebla, MexicoLV
Luis Villasenor-Pineda
Dr. Luis Villaseñor-Pineda is a full-time researcher in the Department of Computer Science at the Instituto Nacional de Astrofísica Óptica y Electrónica in Puebla, Mexico. He obtained his Ph.D. degree in Computer Science from the Université Joseph Fourier (now Université Grenoble Alpes), France. His research interests focus on finding solutions to provide the computer with capabilities to process human language, including written language, spoken language and new forms of interaction, such as brain-computer interfaces based on imagined speech. He is the author of more than 150 refereed articles on these topics. In addition, he is a member of the National System of Researchers (Level II), the Mexican Academy of Sciences (AMC), the Artificial Intelligence Society (SMIA), the Mexican Academy of Computational Sciences (AMEXCOMP) and the Mexican Association for Natural Language Processing (AMPLN), of which he was president from 2018-2020.
Affiliations and expertise
Lead Researcher, Department of Computer Science, Instituto Nacional de Astrofisica Optica y Electronica in Puebla, MexicoOM
Omar Mendoza-Montoya
Dr. Omar Mendoza Montoya, is a professor and researcher in the Department of Computer Science at Tecnologico de Monterrey campus Guadalajara, Mexico. He holds a Ph.D. in Computer Science from the Freie Universität Berlin. He was a member of the BrainModes Research Group at the Charité-Medical University of Berlin. His research activities involve the development of brain-computer interfaces for assistive technology, neurorehabilitation, and therapy. At the moment, he leads multiple projects focusing on robotic applications controlled by biosignals for people with mobility limitations and neurological conditions, such as amyotrophic lateral sclerosis (ALS). Other of his interests are signal processing, numerical analysis, optimization, statistical learning, mathematical modeling, and neuroimaging.
Affiliations and expertise
Researcher/Professor, School of Engineering and Science, Tecnologico de Monterrey, Monterrey, N.L., MexicoRead Biosignal Processing and Classification Using Computational Learning and Intelligence on ScienceDirect