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Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques

Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques
  • Author : Abdulhamit Subasi
  • Publsiher : Academic Press
  • Release : 16 March 2019
  • ISBN : 0128176733
  • Pages : 456 pages
  • Rating : 4/5 from 21 ratings
GET THIS BOOKPractical 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


Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques

Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques
  • Author : Abdulhamit Subasi
  • Publisher : Academic Press
  • Release : 16 March 2019
GET THIS BOOKPractical Guide for Biomedical Signals Analysis Using Machine Learning Techniques

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,


Practical Machine Learning for Data Analysis Using Python

Practical Machine Learning for Data Analysis Using Python
  • Author : Abdulhamit Subasi
  • Publisher : Academic Press
  • Release : 05 June 2020
GET THIS BOOKPractical Machine Learning for Data Analysis Using Python

Practical Machine Learning for Data Analysis Using Python is a problem solver’s guide for creating real-world intelligent systems. It provides a comprehensive approach with concepts, practices, hands-on examples, and sample code. The book teaches readers the vital skills required to understand and solve different problems with machine learning. It teaches machine learning techniques necessary to become a successful practitioner, through the presentation of real-world case studies in Python machine learning ecosystems. The book also focuses on building a foundation


Practical Biomedical Signal Analysis Using MATLAB®

Practical Biomedical Signal Analysis Using MATLAB®
  • Author : Katarzyn J. Blinowska,Jaroslaw Zygierewicz
  • Publisher : CRC Press
  • Release : 12 September 2011
GET THIS BOOKPractical Biomedical Signal Analysis Using MATLAB®

Practical Biomedical Signal Analysis Using MATLAB® presents a coherent treatment of various signal processing methods and applications. The book not only covers the current techniques of biomedical signal processing, but it also offers guidance on which methods are appropriate for a given task and different types of data. The first several chapters of the text describe signal analysis techniques—including the newest and most advanced methods—in an easy and accessible way. MATLAB routines are listed when available and freely


Signal Processing and Machine Learning for Biomedical Big Data

Signal Processing and Machine Learning for Biomedical Big Data
  • Author : Ervin Sejdic,Tiago H. Falk
  • Publisher : CRC Press
  • Release : 04 July 2018
GET THIS BOOKSignal Processing and Machine Learning for Biomedical Big Data

This will be a comprehensive, multi-contributed reference work that will detail the latest research and developments in biomedical signal processing related to big data medical analysis. It will describe signal processing, machine learning, and parallel computing strategies to revolutionize the world of medical analytics and diagnosis as presented by world class researchers and experts in this important field. The chapters will desribe tools that can be used by biomedical and clinical practitioners as well as industry professionals. It will give


Biomedical Signal Analysis

Biomedical Signal Analysis
  • Author : Rangaraj M. Rangayyan
  • Publisher : John Wiley & Sons
  • Release : 24 April 2015
GET THIS BOOKBiomedical Signal Analysis

The book will help assist a reader in the development of techniques for analysis of biomedical signals and computer aided diagnoses with a pedagogical examination of basic and advanced topics accompanied by over 350 figures and illustrations. Wide range of filtering techniques presented to address various applications 800 mathematical expressions and equations Practical questions, problems and laboratory exercises Includes fractals and chaos theory with biomedical applications



Machine Learning for Audio, Image and Video Analysis

Machine Learning for Audio, Image and Video Analysis
  • Author : Francesco Camastra,Alessandro Vinciarelli
  • Publisher : Springer
  • Release : 21 July 2015
GET THIS BOOKMachine Learning for Audio, Image and Video Analysis

This second edition focuses on audio, image and video data, the three main types of input that machines deal with when interacting with the real world. A set of appendices provides the reader with self-contained introductions to the mathematical background necessary to read the book. Divided into three main parts, From Perception to Computation introduces methodologies aimed at representing the data in forms suitable for computer processing, especially when it comes to audio and images. Whilst the second part, Machine


Biomedical Signal and Image Processing

Biomedical Signal and Image Processing
  • Author : Kayvan Najarian,Robert Splinter
  • Publisher : CRC Press
  • Release : 19 April 2016
GET THIS BOOKBiomedical Signal and Image Processing

Written for senior-level and first year graduate students in biomedical signal and image processing, this book describes fundamental signal and image processing techniques that are used to process biomedical information. The book also discusses application of these techniques in the processing of some of the main biomedical signals and images, such as EEG, ECG, MRI, and CT. New features of this edition include the technical updating of each chapter along with the addition of many more examples, the majority of


Biomedical Engineering and its Applications in Healthcare

Biomedical Engineering and its Applications in Healthcare
  • Author : Sudip Paul
  • Publisher : Springer Nature
  • Release : 08 November 2019
GET THIS BOOKBiomedical Engineering and its Applications in Healthcare

This book illustrates the significance of biomedical engineering in modern healthcare systems. Biomedical engineering plays an important role in a range of areas, from diagnosis and analysis to treatment and recovery and has entered the public consciousness through the proliferation of implantable medical devices, such as pacemakers and artificial hips, as well as the more futuristic technologies such as stem cell engineering and 3-D printing of biological organs. Starting with an introduction to biomedical engineering, the book then discusses various


Statistics, Data Mining, and Machine Learning in Astronomy

Statistics, Data Mining, and Machine Learning in Astronomy
  • Author : Željko Ivezić,Andrew J. Connolly,Jacob T VanderPlas,Alexander Gray
  • Publisher : Princeton University Press
  • Release : 12 January 2014
GET THIS BOOKStatistics, Data Mining, and Machine Learning in Astronomy

As telescopes, detectors, and computers grow ever more powerful, the volume of data at the disposal of astronomers and astrophysicists will enter the petabyte domain, providing accurate measurements for billions of celestial objects. This book provides a comprehensive and accessible introduction to the cutting-edge statistical methods needed to efficiently analyze complex data sets from astronomical surveys such as the Panoramic Survey Telescope and Rapid Response System, the Dark Energy Survey, and the upcoming Large Synoptic Survey Telescope. It serves as


Bioelectrical Signal Processing in Cardiac and Neurological Applications

Bioelectrical Signal Processing in Cardiac and Neurological Applications
  • Author : Leif Sörnmo,Pablo Laguna
  • Publisher : Academic Press
  • Release : 02 March 2021
GET THIS BOOKBioelectrical Signal Processing in Cardiac and Neurological Applications

The analysis of bioelectrical signals continues to receive wide attention in research as well as commercially because novel signal processing techniques have helped to uncover valuable information for improved diagnosis and therapy. This book takes a unique problem-driven approach to biomedical signal processing by considering a wide range of problems in cardiac and neurological applications-the two "heavyweight" areas of biomedical signal processing. The interdisciplinary nature of the topic is reflected in how the text interweaves physiological issues with related methodological


Source Separation and Machine Learning

Source Separation and Machine Learning
  • Author : Jen-Tzung Chien
  • Publisher : Academic Press
  • Release : 01 November 2018
GET THIS BOOKSource Separation and Machine Learning

Source Separation and Machine Learning presents the fundamentals in adaptive learning algorithms for Blind Source Separation (BSS) and emphasizes the importance of machine learning perspectives. It illustrates how BSS problems are tackled through adaptive learning algorithms and model-based approaches using the latest information on mixture signals to build a BSS model that is seen as a statistical model for a whole system. Looking at different models, including independent component analysis (ICA), nonnegative matrix factorization (NMF), nonnegative tensor factorization (NTF), and


Data Mining: Practical Machine Learning Tools and Techniques

Data Mining: Practical Machine Learning Tools and Techniques
  • Author : Ian H. Witten,Eibe Frank,Mark A. Hall
  • Publisher : Elsevier
  • Release : 03 February 2011
GET THIS BOOKData Mining: Practical Machine Learning Tools and Techniques

Data Mining: Practical Machine Learning Tools and Techniques, Third Edition, offers a thorough grounding in machine learning concepts as well as practical advice on applying machine learning tools and techniques in real-world data mining situations. This highly anticipated third edition of the most acclaimed work on data mining and machine learning will teach you everything you need to know about preparing inputs, interpreting outputs, evaluating results, and the algorithmic methods at the heart of successful data mining. Thorough updates reflect


Machine Learning in Bio-Signal Analysis and Diagnostic Imaging

Machine Learning in Bio-Signal Analysis and Diagnostic Imaging
  • Author : Nilanjan Dey,Surekha Borra,Amira S. Ashour,Fuqian Shi
  • Publisher : Academic Press
  • Release : 30 November 2018
GET THIS BOOKMachine Learning in Bio-Signal Analysis and Diagnostic Imaging

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


Machine Learning Refined

Machine Learning Refined
  • Author : Jeremy Watt,Reza Borhani,Aggelos Katsaggelos
  • Publisher : Cambridge University Press
  • Release : 09 January 2020
GET THIS BOOKMachine Learning Refined

An intuitive approach to machine learning covering key concepts, real-world applications, and practical Python coding exercises.