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Biosignal Processing and Classification Using Computational Learning and Intelligence
Principles, Algorithms, and Applications
Examines the latest techniques of biosignal processing and classification applied to a variety of clinical diagnoses
Alejandro A. Torres-García (Edited by), Carlos Alberto Reyes Garcia (Edited by), Luis Villasenor-Pineda (Edited by), Omar Mendoza-Montoya (Edited by)
9780128201251, Elsevier Science
Paperback, published 22 September 2021
536 pages
23.4 x 19 x 3.3 cm, 0.45 kg
Biosignal 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.
PART 1 INTRODUCTION 1. Introduction to this book 2. Biosignals analysis (heart, phonatory system, and muscles) 3. Neuroimaging techniques PART 2 BIOSIGNAL PROCESSING: FROM BIOSIGNALS TO FEATURES’ DATASETS 4. Pre-processing and feature extraction 5. Dimensionality reduction PART 3 COMPUTATIONAL LEARNING (MACHINE LEARNING) 6. A brief introduction to supervised, unsupervised, and reinforcement learning 7. Assessing classifier’s performance PART 4 COMPUTATIONAL INTELLIGENCE 8. Fuzzy logic and fuzzy systems 9. Neural networks and deep learning 10. Spiking neural networks and dendrite morphological neural networks: an introduction 11. Bio-inspired algorithms PART 5 APPLICATIONS AND REVIEWS 12. A survey on EEG-based imagined speech classification 13. P300-based brain–computer interface for communication and control 14. EEG-based subject identification with multi-class classification 15. Emotion recognition: from speech and facial expressions 16. Trends and applications of ECG analysis and classification 17. Analysis and processing of infant cry for diagnosis purposes 18. Physics augmented classification of fNIRS signals 19. Evaluation of mechanical variables by registration and analysis of electromyographic activity 20. A review on machine learning techniques for acute leukemia classification 21. Attention deficit and hyperactivity disorder classification with EEG and machine learning 22. Representation for event-related fMRI
Subject Areas: Biomedical engineering [MQW]
