Biosignal Processing and Classification Using Computational Learning and Intelligence

Principles, Algorithms, and Applications

Alejandro A. Torres-García, Carlos Alberto Reyes Garcia, Luis Villasenor-Pineda, Omar Mendoza-Montoya

• Publisher: Academic Press
• Year: 2021
• ISBN:
  9780128204283
• Topic: Biological Sciences
• Subtopic: Biotechnology
• Index: Biological Sciences

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.

• 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

Table of contents

• Cover image
• Title page
• Table of Contents
• Copyright
• List of figures
• List of contributors
• About the authors
• Part 1: Introduction
• Chapter 1: Introduction to this book
• Chapter 2: Biosignals analysis (heart, phonatory system, and muscles)
• Chapter 3: Neuroimaging techniques
• Part 2: Biosignal processing: From biosignals to features' datasets
• Chapter 4: Pre-processing and feature extraction
• Chapter 5: Dimensionality reduction
• Part 3: Computational learning (machine learning)
• Chapter 6: A brief introduction to supervised, unsupervised, and reinforcement learning
• Chapter 7: Assessing classifier's performance
• Part 4: Computational intelligence
• Chapter 8: Fuzzy logic and fuzzy systems
• Chapter 9: Neural networks and deep learning
• Chapter 10: Spiking neural networks and dendrite morphological neural networks: an introduction
• Chapter 11: Bio-inspired algorithms
• Part 5: Applications and reviews
• Chapter 12: A survey on EEG-based imagined speech classification
• Chapter 13: P300-based brain–computer interface for communication and control
• Chapter 14: EEG-based subject identification with multi-class classification
• Chapter 15: Emotion recognition: from speech and facial expressions
• Chapter 16: Trends and applications of ECG analysis and classification
• Chapter 17: Analysis and processing of infant cry for diagnosis purposes
• Chapter 18: Physics augmented classification of fNIRS signals
• Chapter 19: Evaluation of mechanical variables by registration and analysis of electromyographic activity
• Chapter 20: A review on machine learning techniques for acute leukemia classification
• Chapter 21: Attention deficit and hyperactivity disorder classification with EEG and machine learning
• Chapter 22: Representation for event-related fMRI
• Index