Biosignal Processing and Classification Using Computational Learning and Intelligence

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

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

PART 1 INTRODUCTION

CHAPTER 1 Introduction to this book

Alejandro A. Torres-García, Omar Mendoza-Montoya, Carlos

A. Reyes-García, and Luis Villaseñor-Pineda

CHAPTER 2 Biosignals analysis (heart, phonatory system, and muscles)

Rita Q. Fuentes-Aguilar, Humberto Pérez-Espinosa, and María

A. Filigrana-de-la-Cruz

CHAPTER 3 Neuroimaging techniques

Thalía Harmony, María E. Mónica Carlier, and

Manuel Hinojosa-Rodríguez

CHAPTER 4 Pre-processing and feature extraction

Alejandro A. Torres-García, Omar Mendoza-Montoya, Marta Molinas,

Javier M. Antelis, Luis A. Moctezuma, and Tonatiuh Hernández-Del-Toro

CHAPTER 5 Dimensionality reduction

Hugo Jair Escalante and Eduardo F. Morales

Eduardo F. Morales and Hugo Jair Escalante

CHAPTER 7 Assessing classifier’s performance

Tonatiuh Hernández-Del-Toro, Fernando Martínez-Santiago, and

Arturo Montejo-Ráez

CHAPTER 8 Fuzzy logic and fuzzy systems

Carlos A. Reyes-García and Alejandro A. Torres-García

CHAPTER 9 Neural networks and deep learning

A. Pastor López-Monroy and Jesús S. García-Salinas

introduction

Humberto Sossa and Carlos D. Virgilio-G.

CHAPTER 11 Bio-inspired algorithms

Fernando Wario, Omar Avalos, and Jorge Gálvez

CHAPTER 12 A survey on EEG-based imagined speech classification

Alejandro A. Torres-García, Carlos A. Reyes-García, and

Luis Villaseñor-Pineda

CHAPTER 13 P300-based brain–computer interface for communication and control

Omar Mendoza-Montoya, Javier M. Antelis, and Jonathan Delijorge

CHAPTER 14 EEG-based subject identification with multi-class classification

Luis A. Moctezuma and Marta Molinas

Humberto Pérez-Espinosa, Ramón Zatarain-Cabada, and María

Lucía Barrón-Estrada

CHAPTER 16 Trends and applications of ECG analysis and classification

María A. Filigrana-de-la-Cruz

CHAPTER 17 Analysis and processing of infant cry for diagnosis purposes

Mario Mandujano Valdes, Orion F. Reyes-Galaviz, Sergio D. Cano Ortiz,

and Carlos A. Reyes-García

CHAPTER 18 Physics augmented classification of fNIRS signals

Felipe Orihuela-Espina, Michelle Rojas-Cisneros, Samuel

A. Montero-Hernández, Jesús S. García-Salinas, Bibiana Cuervo-Soto, and

Javier Herrera-Vega

electromyographic activity

Rita Q. Fuentes-Aguilar and Alejandro Garcia-Gonzalez

CHAPTER 20 A review on machine learning techniques for acute leukemia classification

Alejandro Rosales-Pérez

machine learning

Claudia Lizbeth Martínez González, Efraín José Martínez Ortiz,

Jesús Jaime Moreno Escobar, and Juan Alfredo Durand Rivera

CHAPTER 22 Representation for event-related fMRI

Claudia Cruz-Martínez and Carlos A. Reyes-García

Acknowledgments

References

Index