Biosignal Processing and Classification Using Computational Learning and Intelligence Book

Biosignal Processing and Classification Using Computational Learning and Intelligence


  • Author : Alejandro Antonio Torres Garcia
  • Publisher : Elsevier
  • Release Date : 2021-09-22
  • Genre: Science
  • Pages : 536
  • ISBN 10 : 9780128201251
  • Total Read : 76
  • File Size : 13,7 Mb

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Biosignal Processing and Classification Using Computational Learning and Intelligence Summary:

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 understa

Biosignal Processing and Classification Using Computational Learning and Intelligence Book

Biosignal Processing and Classification Using Computational Learning and Intelligence


  • Author : Alejandro Antonio Torres Garcia
  • Publisher : Academic Press
  • Release Date : 2021-09-18
  • Genre: Science
  • Pages : 536
  • ISBN 10 : 9780128204283
  • Total Read : 84
  • File Size : 12,6 Mb

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Biosignal Processing and Classification Using Computational Learning and Intelligence Summary:

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 unders

Machine Learning in Bio Signal Analysis and Diagnostic Imaging Book

Machine Learning in Bio Signal Analysis and Diagnostic Imaging


  • Author : Nilanjan Dey
  • Publisher : Academic Press
  • Release Date : 2018-11-30
  • Genre: Science
  • Pages : 345
  • ISBN 10 : 9780128160879
  • Total Read : 60
  • File Size : 11,8 Mb

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Machine Learning in Bio Signal Analysis and Diagnostic Imaging Summary:

Machine Learning in Bio-Signal Analysis and Diagnostic Imaging presents original research on the advanced analysis and classification techniques of biomedical signals and images that cover both supervised and unsupervised machine learning models, standards, algorithms, and their applications, along with the difficulties and challenges faced by healthcare professionals in analyzing biomedical signals and diagnostic images. These intelligent recommender systems are designed based on machine learning, soft computing, computer vision, artificial intelligence and data mining techniques. Classification and clustering techniques, such as PCA, SVM, techniques, Naive Bayes, Neural Network, Decision trees, and Association Rule Mining are among the approaches presented. The design of high accuracy decision support systems assists and eases the job of healthcare practitioners and suits a variety of applications. Integrating Machine Learning (ML) technology with human visual psychometrics helps to meet the demands of radiologists in improving the efficiency and quality of diagnosis in dealing with unique and complex diseases in real time by reducing human errors and allowing fast and rigorous analysis. The book's target audience includes professors and students in biomedical engineering and medical schools, researchers and engineers. Examines a variety of machine learning techniques applied to bio-signal analysis and diagnostic imaging Discusses various methods of using intelligent systems based on machine learning, soft computing, computer vision, artificial intelligence and data mining Covers the most recent research on machine learning in imaging analysis and includes applications to a number of domains

Advanced Methods in Biomedical Signal Processing and Analysis Book

Advanced Methods in Biomedical Signal Processing and Analysis


  • Author : Kunal Pal
  • Publisher : Academic Press
  • Release Date : 2022-09-15
  • Genre: Technology & Engineering
  • Pages : 434
  • ISBN 10 : 9780323859547
  • Total Read : 80
  • File Size : 12,7 Mb

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Advanced Methods in Biomedical Signal Processing and Analysis Summary:

Advanced Methods in Biomedical Signal Processing and Analysis presents state-of-the-art methods in biosignal processing, including recurrence quantification analysis, heart rate variability, analysis of the RRI time-series signals, joint time-frequency analyses, wavelet transforms and wavelet packet decomposition, empirical mode decomposition, modeling of biosignals, Gabor Transform, empirical mode decomposition. The book also gives an understanding of feature extraction, feature ranking, and feature selection methods, while also demonstrating how to apply artificial intelligence and machine learning to biosignal techniques. Gives advanced methods in signal processing Includes machine and deep learning methods Presents experimental case studies

Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques Book

Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques


  • Author : Abdulhamit Subasi
  • Publisher : Academic Press
  • Release Date : 2019-03-16
  • Genre: Business & Economics
  • Pages : 456
  • ISBN 10 : 9780128176733
  • Total Read : 71
  • File Size : 8,6 Mb

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Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques Summary:

Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques: A MATLAB Based Approach presents how machine learning and biomedical signal processing methods can be used in biomedical signal analysis. Different machine learning applications in biomedical signal analysis, including those for electrocardiogram, electroencephalogram and electromyogram are described in a practical and comprehensive way, helping readers with limited knowledge. Sections cover biomedical signals and machine learning techniques, biomedical signals, such as electroencephalogram (EEG), electromyogram (EMG) and electrocardiogram (ECG), different signal-processing techniques, signal de-noising, feature extraction and dimension reduction techniques, such as PCA, ICA, KPCA, MSPCA, entropy measures, and other statistical measures, and more. This book is a valuable source for bioinformaticians, medical doctors and other members of the biomedical field who need a cogent resource on the most recent and promising machine learning techniques for biomedical signals analysis. Provides comprehensive knowledge in the application of machine learning tools in biomedical signal analysis for medical diagnostics, brain computer interface and man/machine interaction Explains how to apply machine learning techniques to EEG, ECG and EMG signals Gives basic knowledge on predictive modeling in biomedical time series and advanced knowledge in machine learning for biomedical time series

Machine Learning for Intelligent Decision Science Book

Machine Learning for Intelligent Decision Science


  • Author : Jitendra Kumar Rout
  • Publisher : Springer Nature
  • Release Date : 2020-04-02
  • Genre: Technology & Engineering
  • Pages : 209
  • ISBN 10 : 9789811536892
  • Total Read : 85
  • File Size : 8,8 Mb

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Machine Learning for Intelligent Decision Science Summary:

The book discusses machine learning-based decision-making models, and presents intelligent, hybrid and adaptive methods and tools for solving complex learning and decision-making problems under conditions of uncertainty. Featuring contributions from data scientists, practitioners and educators, the book covers a range of topics relating to intelligent systems for decision science, and examines recent innovations, trends, and practical challenges in the field. The book is a valuable resource for academics, students, researchers and professionals wanting to gain insights into decision-making.

Biomedical Signal Processing and Artificial Intelligence in Healthcare Book

Biomedical Signal Processing and Artificial Intelligence in Healthcare


  • Author : Walid A. Zgallai
  • Publisher : Academic Press
  • Release Date : 2020-07-29
  • Genre: Technology & Engineering
  • Pages : 268
  • ISBN 10 : 9780128189474
  • Total Read : 73
  • File Size : 18,8 Mb

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Biomedical Signal Processing and Artificial Intelligence in Healthcare Summary:

Biomedical Signal Processing and Artificial Intelligence in Healthcare is a new volume in the Developments in Biomedical Engineering and Bioelectronics series. This volume covers the basics of biomedical signal processing and artificial intelligence. It explains the role of machine learning in relation to processing biomedical signals and the applications in medicine and healthcare. The book provides background to statistical analysis in biomedical systems. Several types of biomedical signals are introduced and analyzed, including ECG and EEG signals. The role of Deep Learning, Neural Networks, and the implications of the expansion of artificial intelligence is covered. Biomedical Images are also introduced and processed, including segmentation, classification, and detection. This book covers different aspects of signals, from the use of hardware and software, and making use of artificial intelligence in problem solving. Dr Zgallai’s book has up to date coverage where readers can find the latest information, easily explained, with clear examples and illustrations. The book includes examples on the application of signal and image processing employing artificial intelligence to Alzheimer, Parkinson, ADHD, autism, and sleep disorders, as well as ECG and EEG signals. Developments in Biomedical Engineering and Bioelectronics is a 10-volume series which covers recent developments, trends and advances in this field. Edited by leading academics in the field, and taking a multidisciplinary approach, this series is a forum for cutting-edge, contemporary review articles and contributions from key ‘up-and-coming’ academics across the full subject area. The series serves a wide audience of university faculty, researchers and students, as well as industry practitioners. Coverage of the subject area and the latest advances and applications in biomedical signal processing and Artificial Intelligence. Contributions by recognized researchers and field leaders. On-line presentat

Introduction to Computational Health Informatics Book

Introduction to Computational Health Informatics


  • Author : Arvind Kumar Bansal
  • Publisher : CRC Press
  • Release Date : 2020-01-08
  • Genre: Medical
  • Pages : 664
  • ISBN 10 : 9781000761597
  • Total Read : 85
  • File Size : 6,6 Mb

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Introduction to Computational Health Informatics Summary:

This class-tested textbook is designed for a semester-long graduate or senior undergraduate course on Computational Health Informatics. The focus of the book is on computational techniques that are widely used in health data analysis and health informatics and it integrates computer science and clinical perspectives. This book prepares computer science students for careers in computational health informatics and medical data analysis. Features Integrates computer science and clinical perspectives Describes various statistical and artificial intelligence techniques, including machine learning techniques such as clustering of temporal data, regression analysis, neural networks, HMM, decision trees, SVM, and data mining, all of which are techniques used widely used in health-data analysis Describes computational techniques such as multidimensional and multimedia data representation and retrieval, ontology, patient-data deidentification, temporal data analysis, heterogeneous databases, medical image analysis and transmission, biosignal analysis, pervasive healthcare, automated text-analysis, health-vocabulary knowledgebases and medical information-exchange Includes bioinformatics and pharmacokinetics techniques and their applications to vaccine and drug development