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Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques

Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques
A MATLAB Based Approach

by Abdulhamit Subasi

  • Publisher : Academic Press
  • Release : 2019-03-16
  • Pages : 456
  • ISBN : 0128176733
  • Language : En, Es, Fr & De
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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 Machine Learning for Data Analysis Using Python

Practical Machine Learning for Data Analysis Using Python
A Book

by Abdulhamit Subasi

  • Publisher : Academic Press
  • Release : 2020-06-05
  • Pages : 534
  • ISBN : 0128213809
  • Language : En, Es, Fr & De
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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 of machine learning knowledge to solve different real-world case studies across various fields, including biomedical signal analysis, healthcare, security, economics, and finance. Moreover, it covers a wide range of machine learning models, including regression, classification, and forecasting. The goal of the book is to help a broad range of readers, including IT professionals, analysts, developers, data scientists, engineers, and graduate students, to solve their own real-world problems. Offers a comprehensive overview of the application of machine learning tools in data analysis across a wide range of subject areas Teaches readers how to apply machine learning techniques to biomedical signals, financial data, and healthcare data Explores important classification and regression algorithms as well as other machine learning techniques Explains how to use Python to handle data extraction, manipulation, and exploration techniques, as well as how to visualize data spread across multiple dimensions and extract useful features

Mechano-Electric Correlations in the Human Physiological System

Mechano-Electric Correlations in the Human Physiological System
A Book

by A. Bakiya,K. Kamalanand,R. L. J. De Britto

  • Publisher : CRC Press
  • Release : 2021-04-16
  • Pages : 112
  • ISBN : 1000374734
  • Language : En, Es, Fr & De
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The aim of Mechano-Electric Correlations in the Human Physiological System is to present the mechanical and electrical properties of human soft tissues and the mathematical models related to the evaluation of these properties in time, as well as their biomedical applications. This book also provides an overview of the bioelectric signals of soft tissues from various parts of the human body. In addition, this book presents the basic dielectric and viscoelastic characteristics of soft tissues, an introduction to the measurement and characteristics of bioelectric signals and their relationship with the mechanical activity, electromyography and the correlation of electromyograms with the muscle activity in normal and certain clinical conditions. The authors also present a case study on the effect of lymphatic filariasis on the mechanical and electrical activity of the muscle. Features: Explains the basics of electrical and mechanical properties of soft tissues in time and frequency domain along with the mathematical models of soft tissue mechanics Explores the correlation of electrical properties with the mechanical properties of biological soft tissues using computational techniques Provides a detailed introduction to electrophysiological signals along with the types, applications, properties, problems and associated mathematical models Explains the electromechanics of muscles using electromyography recordings from various muscles of the human physiological system Presents a case study on the effect of lymphatic filariasis on the mechanical and electrical activity of the muscle Mechano-Electric Correlations in the Human Physiological System is intended for biomedical engineers, researchers and medical scientists as well graduate and undergraduate students working on the mechanical properties of soft tissues.

Biomedical Engineering and its Applications in Healthcare

Biomedical Engineering and its Applications in Healthcare
A Book

by Sudip Paul

  • Publisher : Springer Nature
  • Release : 2019-11-08
  • Pages : 738
  • ISBN : 9811337055
  • Language : En, Es, Fr & De
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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 tools and techniques for medical diagnostics and treatment and recent advances. It also provides comprehensive and integrated information on rehabilitation engineering, including the design of artificial body parts, and the underlying principles, and standards. It also presents a conceptual framework to clarify the relationship between ethical policies in medical practice and philosophical moral reasoning. Lastly, the book highlights a number of challenges associated with modern healthcare technologies.

Statistics, Data Mining, and Machine Learning in Astronomy

Statistics, Data Mining, and Machine Learning in Astronomy
A Practical Python Guide for the Analysis of Survey Data

by Željko Ivezić,Andrew J. Connolly,Jacob T VanderPlas,Alexander Gray

  • Publisher : Princeton University Press
  • Release : 2014-01-12
  • Pages : 560
  • ISBN : 0691151687
  • Language : En, Es, Fr & De
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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 a practical handbook for graduate students and advanced undergraduates in physics and astronomy, and as an indispensable reference for researchers. Statistics, Data Mining, and Machine Learning in Astronomy presents a wealth of practical analysis problems, evaluates techniques for solving them, and explains how to use various approaches for different types and sizes of data sets. For all applications described in the book, Python code and example data sets are provided. The supporting data sets have been carefully selected from contemporary astronomical surveys (for example, the Sloan Digital Sky Survey) and are easy to download and use. The accompanying Python code is publicly available, well documented, and follows uniform coding standards. Together, the data sets and code enable readers to reproduce all the figures and examples, evaluate the methods, and adapt them to their own fields of interest. Describes the most useful statistical and data-mining methods for extracting knowledge from huge and complex astronomical data sets Features real-world data sets from contemporary astronomical surveys Uses a freely available Python codebase throughout Ideal for students and working astronomers

VIII Latin American Conference on Biomedical Engineering and XLII National Conference on Biomedical Engineering

VIII Latin American Conference on Biomedical Engineering and XLII National Conference on Biomedical Engineering
Proceedings of CLAIB-CNIB 2019, October 2-5, 2019, Cancún, México

by César A. González Díaz,Christian Chapa González,Eric Laciar Leber,Hugo A. Vélez,Norma P. Puente,Dora-Luz Flores,Adriano O. Andrade,Héctor A. Galván,Fabiola Martínez,Renato García,Citlalli J. Trujillo,Aldo R. Mejía

  • Publisher : Springer Nature
  • Release : 2019-09-30
  • Pages : 1518
  • ISBN : 3030306488
  • Language : En, Es, Fr & De
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This book gathers the joint proceedings of the VIII Latin American Conference on Biomedical Engineering (CLAIB 2019) and the XLII National Conference on Biomedical Engineering (CNIB 2019). It reports on the latest findings and technological outcomes in the biomedical engineering field. Topics include: biomedical signal and image processing; biosensors, bioinstrumentation and micro-nanotechnologies; biomaterials and tissue engineering. Advances in biomechanics, biorobotics, neurorehabilitation, medical physics and clinical engineering are also discussed. A special emphasis is given to practice-oriented research and to the implementation of new technologies in clinical settings. The book provides academics and professionals with extensive knowledge on and a timely snapshot of cutting-edge research and developments in the field of biomedical engineering.

Advances in Automation, Signal Processing, Instrumentation, and Control

Advances in Automation, Signal Processing, Instrumentation, and Control
Select Proceedings of i-CASIC 2020

by Venkata Lakshmi Narayana Komanapalli

  • Publisher : Springer Nature
  • Release : 2021
  • Pages : 329
  • ISBN : 9811582211
  • Language : En, Es, Fr & De
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The Handbook of Cuffless Blood Pressure Monitoring

The Handbook of Cuffless Blood Pressure Monitoring
A Practical Guide for Clinicians, Researchers, and Engineers

by Josep Solà,Ricard Delgado-Gonzalo

  • Publisher : Springer Nature
  • Release : 2019-08-21
  • Pages : 239
  • ISBN : 3030247015
  • Language : En, Es, Fr & De
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This book is the first comprehensive overview of the emerging field of cuffless blood pressure monitoring. Increasing clinical evidence proves that longitudinal measurements of blood pressure allow for earlier detection and better management of multiple medical conditions and for superior prediction of cardiovascular events. Unfortunately, today’s clinical and industry standards for blood pressure monitoring still require the inflation of a pneumatic cuff around a limb each time a measurement is taken. Over the last decades clinicians, scientists and device manufacturers have explored the feasibility of technologies that reduce or even completely eliminate the need of cuffs, initiating the era of cuffless blood pressure monitoring. Among the existing literature, this book is intended to be a practical guide to navigate across this emerging field. The chapters of the handbook have been elaborated by experts and key opinion leaders in the domain, and will guide the reader along the clinical, scientific, technical, and regulatory aspects of cuffless blood pressure monitoring.

Kernel Methods for Remote Sensing Data Analysis

Kernel Methods for Remote Sensing Data Analysis
A Book

by Gustau Camps-Valls,Lorenzo Bruzzone

  • Publisher : John Wiley & Sons
  • Release : 2009-09-03
  • Pages : 434
  • ISBN : 0470749008
  • Language : En, Es, Fr & De
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Kernel methods have long been established as effective techniques in the framework of machine learning and pattern recognition, and have now become the standard approach to many remote sensing applications. With algorithms that combine statistics and geometry, kernel methods have proven successful across many different domains related to the analysis of images of the Earth acquired from airborne and satellite sensors, including natural resource control, detection and monitoring of anthropic infrastructures (e.g. urban areas), agriculture inventorying, disaster prevention and damage assessment, and anomaly and target detection. Presenting the theoretical foundations of kernel methods (KMs) relevant to the remote sensing domain, this book serves as a practical guide to the design and implementation of these methods. Five distinct parts present state-of-the-art research related to remote sensing based on the recent advances in kernel methods, analysing the related methodological and practical challenges: Part I introduces the key concepts of machine learning for remote sensing, and the theoretical and practical foundations of kernel methods. Part II explores supervised image classification including Super Vector Machines (SVMs), kernel discriminant analysis, multi-temporal image classification, target detection with kernels, and Support Vector Data Description (SVDD) algorithms for anomaly detection. Part III looks at semi-supervised classification with transductive SVM approaches for hyperspectral image classification and kernel mean data classification. Part IV examines regression and model inversion, including the concept of a kernel unmixing algorithm for hyperspectral imagery, the theory and methods for quantitative remote sensing inverse problems with kernel-based equations, kernel-based BRDF (Bidirectional Reflectance Distribution Function), and temperature retrieval KMs. Part V deals with kernel-based feature extraction and provides a review of the principles of several multivariate analysis methods and their kernel extensions. This book is aimed at engineers, scientists and researchers involved in remote sensing data processing, and also those working within machine learning and pattern recognition.

Body Sensor Networking, Design and Algorithms

Body Sensor Networking, Design and Algorithms
A Book

by Saeid Sanei,Delaram Jarchi,Anthony G. Constantinides

  • Publisher : John Wiley & Sons
  • Release : 2020-04-30
  • Pages : 416
  • ISBN : 111939001X
  • Language : En, Es, Fr & De
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A complete guide to the state of the art theoretical and manufacturing developments of body sensor network, design, and algorithms In Body Sensor Networking, Design, and Algorithms, professionals in the field of Biomedical Engineering and e-health get an in-depth look at advancements, changes, and developments. When it comes to advances in the industry, the text looks at cooperative networks, noninvasive and implantable sensor microelectronics, wireless sensor networks, platforms, and optimization—to name a few. Each chapter provides essential information needed to understand the current landscape of technology and mechanical developments. It covers subjects including Physiological Sensors, Sleep Stage Classification, Contactless Monitoring, and much more. Among the many topics covered, the text also includes additions such as: ● Over 120 figures, charts, and tables to assist with the understanding of complex topics ● Design examples and detailed experimental works ● A companion website featuring MATLAB and selected data sets Additionally, readers will learn about wearable and implantable devices, invasive and noninvasive monitoring, biocompatibility, and the tools and platforms for long-term, low-power deployment of wireless communications. It’s an essential resource for understanding the applications and practical implementation of BSN when it comes to elderly care, how to manage patients with chronic illnesses and diseases, and use cases for rehabilitation.

Guide to Brain-Computer Music Interfacing

Guide to Brain-Computer Music Interfacing
A Book

by Eduardo Reck Miranda,Julien Castet

  • Publisher : Springer
  • Release : 2014-10-03
  • Pages : 313
  • ISBN : 1447165845
  • Language : En, Es, Fr & De
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This book presents a world-class collection of Brain-Computer Music Interfacing (BCMI) tools. The text focuses on how these tools enable the extraction of meaningful control information from brain signals, and discusses how to design effective generative music techniques that respond to this information. Features: reviews important techniques for hands-free interaction with computers, including event-related potentials with P300 waves; explores questions of semiotic brain-computer interfacing (BCI), and the use of machine learning to dig into relationships among music and emotions; offers tutorials on signal extraction, brain electric fields, passive BCI, and applications for genetic algorithms, along with historical surveys; describes how BCMI research advocates the importance of better scientific understanding of the brain for its potential impact on musical creativity; presents broad coverage of this emerging, interdisciplinary area, from hard-core EEG analysis to practical musical applications.

Graduate Programs in Engineering and Applied Sciences 1984

Graduate Programs in Engineering and Applied Sciences 1984
A Book

by Diane Conley

  • Publisher : Unknown Publisher
  • Release : 1983
  • Pages : 799
  • ISBN : 9780878662210
  • Language : En, Es, Fr & De
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EEG-Based Diagnosis of Alzheimer Disease

EEG-Based Diagnosis of Alzheimer Disease
A Review and Novel Approaches for Feature Extraction and Classification Techniques

by Nilesh Kulkarni,Vinayak Bairagi

  • Publisher : Academic Press
  • Release : 2018-04-13
  • Pages : 110
  • ISBN : 0128153938
  • Language : En, Es, Fr & De
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EEG-Based Diagnosis of Alzheimer Disease: A Review and Novel Approaches for Feature Extraction and Classification Techniques provides a practical and easy-to-use guide for researchers in EEG signal processing techniques, Alzheimer’s disease, and dementia diagnostics. The book examines different features of EEG signals used to properly diagnose Alzheimer’s Disease early, presenting new and innovative results in the extraction and classification of Alzheimer’s Disease using EEG signals. This book brings together the use of different EEG features, such as linear and nonlinear features, which play a significant role in diagnosing Alzheimer’s Disease. Includes the mathematical models and rigorous analysis of various classifiers and machine learning algorithms from a perspective of clinical deployment Covers the history of EEG signals and their measurement and recording, along with their uses in clinical diagnostics Analyzes spectral, wavelet, complexity and other features of early and efficient Alzheimer’s Disease diagnostics Explores support vector machine-based classification to increase accuracy

Documentation Abstracts

Documentation Abstracts
A Book

by Anonim

  • Publisher : Unknown Publisher
  • Release : 1996
  • Pages : 329
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Inventive Communication and Computational Technologies

Inventive Communication and Computational Technologies
Proceedings of ICICCT 2019

by G. Ranganathan,Joy Chen,Álvaro Rocha

  • Publisher : Springer Nature
  • Release : 2020-01-29
  • Pages : 1415
  • ISBN : 9811501467
  • Language : En, Es, Fr & De
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This book gathers selected papers presented at the Inventive Communication and Computational Technologies conference (ICICCT 2019), held on 29–30 April 2019 at Gnanamani College of Technology, Tamil Nadu, India. The respective contributions highlight recent research efforts and advances in a new paradigm called ISMAC (IoT in Social, Mobile, Analytics and Cloud contexts). Topics covered include the Internet of Things, Social Networks, Mobile Communications, Big Data Analytics, Bio-inspired Computing and Cloud Computing. The book is chiefly intended for academics and practitioners working to resolve practical issues in this area.

The biomedical engineering handbook

The biomedical engineering handbook
A Book

by Joseph D. Bronzino

  • Publisher : Unknown Publisher
  • Release : 1995
  • Pages : 2862
  • ISBN : 9780849383465
  • Language : En, Es, Fr & De
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The first handbook ever written for the biomedical engineering field, this text contains comprehensive information on every aspect of biomedical engineering. It reflects the current perception of the field as one that encompasses emerging and expanding disciplines of investigation and application. It includes a complete review of the major physiological systems and accepted practices.

Biomedical Sensors and Smart Sensing: A Beginner’s Guide

Biomedical Sensors and Smart Sensing: A Beginner’s Guide
A Book

by Ayan Kumar Panja,Amartya Mukherjee,Nilanjan Dey

  • Publisher : Academic Press
  • Release : 2021-06-01
  • Pages : 160
  • ISBN : 0128230738
  • Language : En, Es, Fr & De
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Primers in Biomedical Imaging Devices and Systems is a 10-volume series which covers the key principles, background, and advancements in biomedicine-related technologies. It explores the essential fundamental techniques required to analyze and process signals and images for diagnosis, scientific discovery, and medical applications. The volumes in this book series cover a wide range of interdisciplinary areas, combining foundational content with practical case studies to demonstrate the applications of the technology in real-world situations. The series considers various medical devices, electronics, circuits, sensors, and algorithms. Several applications ranging from the basic biological science to clinical practice are included to facilitate ongoing research. Biomedical Sensors and Smart Sensing: A Beginner’s Guide series covers a wide range of interdisciplinary applications including use in imaging modalities, nuclear medicine, computed tomographic systems, x-ray systems, magnetic resonance imaging, ultrasound, and virtual reality. The wide scope of technology considered in this series makes it an ideal source for researchers, graduate students, engineers and medical practitioners involved with medical device development and application in the healthcare industry. Covers a variety of sensing and signal processing techniques Introduces different approaches related to the communication and intelligent data processing approach for early detection and the prediction of diseases Includes practical case studies

The British National Bibliography

The British National Bibliography
A Book

by Arthur James Wells

  • Publisher : Unknown Publisher
  • Release : 2001
  • Pages : 329
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Dissertation Abstracts International

Dissertation Abstracts International
The sciences and engineering. B

by Anonim

  • Publisher : Unknown Publisher
  • Release : 2008
  • Pages : 329
  • ISBN : 9876543210XXX
  • Language : En, Es, Fr & De
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Practical Data Science Cookbook

Practical Data Science Cookbook
A Book

by Prabhanjan Tattar,Tony Ojeda,Sean Patrick Murphy,Benjamin Bengfort,Abhijit Dasgupta

  • Publisher : Packt Publishing Ltd
  • Release : 2017-06-29
  • Pages : 434
  • ISBN : 178712326X
  • Language : En, Es, Fr & De
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Over 85 recipes to help you complete real-world data science projects in R and Python About This Book Tackle every step in the data science pipeline and use it to acquire, clean, analyze, and visualize your data Get beyond the theory and implement real-world projects in data science using R and Python Easy-to-follow recipes will help you understand and implement the numerical computing concepts Who This Book Is For If you are an aspiring data scientist who wants to learn data science and numerical programming concepts through hands-on, real-world project examples, this is the book for you. Whether you are brand new to data science or you are a seasoned expert, you will benefit from learning about the structure of real-world data science projects and the programming examples in R and Python. What You Will Learn Learn and understand the installation procedure and environment required for R and Python on various platforms Prepare data for analysis by implement various data science concepts such as acquisition, cleaning and munging through R and Python Build a predictive model and an exploratory model Analyze the results of your model and create reports on the acquired data Build various tree-based methods and Build random forest In Detail As increasing amounts of data are generated each year, the need to analyze and create value out of it is more important than ever. Companies that know what to do with their data and how to do it well will have a competitive advantage over companies that don't. Because of this, there will be an increasing demand for people that possess both the analytical and technical abilities to extract valuable insights from data and create valuable solutions that put those insights to use. Starting with the basics, this book covers how to set up your numerical programming environment, introduces you to the data science pipeline, and guides you through several data projects in a step-by-step format. By sequentially working through the steps in each chapter, you will quickly familiarize yourself with the process and learn how to apply it to a variety of situations with examples using the two most popular programming languages for data analysis—R and Python. Style and approach This step-by-step guide to data science is full of hands-on examples of real-world data science tasks. Each recipe focuses on a particular task involved in the data science pipeline, ranging from readying the dataset to analytics and visualization