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 : 2020-08-19
  • Genre: Technology & Engineering
  • Pages : 268
  • ISBN 10 : 9780128189467

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Biomedical Signal Processing and Artificial Intelligence in Healthcare Excerpt :

Biomedical Signal Processing with Artificial Intelligence, a new volume in the Developments in Biomedical Engineering and Bioelectronics series, covers the basics of analog and digital data and data acquisition. The book explains the role of smart sensors, smart materials and wearables in relation to biomedical signals. It also 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 Machine Learning, including Deep Learning, Neural Networks, and the implications of the expansion of artificial intelligence is also covered, as are biomedical images and their segmentation, classification and detection. This book covers all aspects of signals, from acquisition, the use of hardware and software, analyzing signals, and making use of AI in problem-solving. 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. Presents comprehensive coverage and the latest advances and applications in biomedical signal processing Contains contributions from recognized researchers and field leaders Includes online presentations, tutorials, applications and algorithm examples

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

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Biomedical Signal Processing and Artificial Intelligence in Healthcare Excerpt :

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

Signal Processing and Machine Learning for Biomedical Big Data Book

Signal Processing and Machine Learning for Biomedical Big Data


  • Author : Ervin Sejdic
  • Publisher : CRC Press
  • Release Date : 2018-07-04
  • Genre: Medical
  • Pages : 606
  • ISBN 10 : 9781351061216

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Signal Processing and Machine Learning for Biomedical Big Data Excerpt :

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 signal processing researchers a glimpse into the issues faced with Big Medical Data.

Signal Processing and Machine Learning for Biomedical Big Data Book

Signal Processing and Machine Learning for Biomedical Big Data


  • Author : Ervin Sejdic
  • Publisher : CRC Press
  • Release Date : 2018
  • Genre: Machine learning
  • Pages : 606
  • ISBN 10 : 1498773451

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Signal Processing and Machine Learning for Biomedical Big Data Excerpt :

Within the healthcare domain, big data is defined as any ``high volume, high diversity biological, clinical, environmental, and lifestyle information collected from single individuals to large cohorts, in relation to their health and wellness status, at one or several time points.'' Such data is crucial because within it lies vast amounts of invaluable information that could potentially change a patient's life, opening doors to alternate therapies, drugs, and diagnostic tools. Signal Processing and Machine Learning for Biomedical Big Data thus discusses modalities; the numerous ways in which this data is captured via sensors; and various sample rates and dimensionalities. Capturing, analyzing, storing, and visualizing such massive data has required new shifts in signal processing paradigms and new ways of combining signal processing with machine learning tools. This book covers several of these aspects in two ways: firstly, through theoretical signal processing chapters where tools aimed at big data (be it biomedical or otherwise) are described; and, secondly, through application-driven chapters focusing on existing applications of signal processing and machine learning for big biomedical data. This text aimed at the curious researcher working in the field, as well as undergraduate and graduate students eager to learn how signal processing can help with big data analysis. It is the hope of Drs. Sejdic and Falk that this book will bring together signal processing and machine learning researchers to unlock existing bottlenecks within the healthcare field, thereby improving patient quality-of-life. Provides an overview of recent state-of-the-art signal processing and machine learning algorithms for biomedical big data, including applications in the neuroimaging, cardiac, retinal, genomic, sleep, patient outcome prediction, critical care, and rehabilitation domains. Provides contributed chapters from world leaders in the fields of big data and signal processing, coverin

Biomedical Signal Processing for Healthcare Applications Book

Biomedical Signal Processing for Healthcare Applications


  • Author : Varun Bajaj
  • Publisher : CRC Press
  • Release Date : 2021-07-21
  • Genre: Technology & Engineering
  • Pages : 336
  • ISBN 10 : 9781000413304

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Biomedical Signal Processing for Healthcare Applications Excerpt :

This book examines the use of biomedical signal processing—EEG, EMG, and ECG—in analyzing and diagnosing various medical conditions, particularly diseases related to the heart and brain. In combination with machine learning tools and other optimization methods, the analysis of biomedical signals greatly benefits the healthcare sector by improving patient outcomes through early, reliable detection. The discussion of these modalities promotes better understanding, analysis, and application of biomedical signal processing for specific diseases. The major highlights of Biomedical Signal Processing for Healthcare Applications include biomedical signals, acquisition of signals, pre-processing and analysis, post-processing and classification of the signals, and application of analysis and classification for the diagnosis of brain- and heart-related diseases. Emphasis is given to brain and heart signals because incomplete interpretations are made by physicians of these aspects in several situations, and these partial interpretations lead to major complications. FEATURES Examines modeling and acquisition of biomedical signals of different disorders Discusses CAD-based analysis of diagnosis useful for healthcare Includes all important modalities of biomedical signals, such as EEG, EMG, MEG, ECG, and PCG Includes case studies and research directions, including novel approaches used in advanced healthcare systems This book can be used by a wide range of users, including students, research scholars, faculty, and practitioners in the field of biomedical engineering and medical image analysis and diagnosis.

Machine Learning and the Internet of Medical Things in Healthcare Book

Machine Learning and the Internet of Medical Things in Healthcare


  • Author : Krishna Kant Singh
  • Publisher : Academic Press
  • Release Date : 2021-04-26
  • Genre: Science
  • Pages : 290
  • ISBN 10 : 9780128232170

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Machine Learning and the Internet of Medical Things in Healthcare Excerpt :

Machine Learning and the Internet of Medical Things in Healthcare discusses the applications and challenges of machine learning for healthcare applications. The book provides a platform for presenting machine learning-enabled healthcare techniques and offers a mathematical and conceptual background of the latest technology. It describes machine learning techniques along with the emerging platform of the Internet of Medical Things used by practitioners and researchers worldwide. The book includes deep feed forward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology. It also presents the concepts of the Internet of Things, the set of technologies that develops traditional devices into smart devices. Finally, the book offers research perspectives, covering the convergence of machine learning and IoT. It also presents the application of these technologies in the development of healthcare frameworks. Provides an introduction to the Internet of Medical Things through the principles and applications of machine learning Explains the functions and applications of machine learning in various applications such as ultrasound imaging, biomedical signal processing, robotics, and biomechatronics Includes coverage of the evolution of healthcare applications with machine learning, including Clinical Decision Support Systems, artificial intelligence in biomedical engineering, and AI-enabled connected health informatics, supported by real-world case studies

Signal Processing Techniques for Computational Health Informatics Book

Signal Processing Techniques for Computational Health Informatics


  • Author : Md Atiqur Rahman Ahad
  • Publisher : Springer Nature
  • Release Date : 2020-10-07
  • Genre: Technology & Engineering
  • Pages : 334
  • ISBN 10 : 9783030549329

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Signal Processing Techniques for Computational Health Informatics Excerpt :

This book focuses on signal processing techniques used in computational health informatics. As computational health informatics is the interdisciplinary study of the design, development, adoption and application of information and technology-based innovations, specifically, computational techniques that are relevant in health care, the book covers a comprehensive and representative range of signal processing techniques used in biomedical applications, including: bio-signal origin and dynamics, sensors used for data acquisition, artefact and noise removal techniques, feature extraction techniques in the time, frequency, time–frequency and complexity domain, and image processing techniques in different image modalities. Moreover, it includes an extensive discussion of security and privacy challenges, opportunities and future directions for computational health informatics in the big data age, and addresses the incorporation of recent techniques from the areas of artificial intelligence, deep learning and human–computer interaction. The systematic analysis of the state-of-the-art techniques covered here helps to further our understanding of the physiological processes involved and expandour capabilities in medical diagnosis and prognosis. In closing, the book, the first of its kind, blends state-of-the-art theory and practices of signal processing techniques inthe health informatics domain with real-world case studies building on those theories. As a result, it can be used as a text for health informatics courses to provide medics with cutting-edge signal processing techniques, or to introducehealth professionals who are already serving in this sector to some of the most exciting computational ideas that paved the way for the development of computational health informatics.

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

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Machine Learning in Bio Signal Analysis and Diagnostic Imaging Excerpt :

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

Signal and Image Processing Techniques for the Development of Intelligent Healthcare Systems Book

Signal and Image Processing Techniques for the Development of Intelligent Healthcare Systems


  • Author : E. Priya
  • Publisher : Springer Nature
  • Release Date : 2020-09-21
  • Genre: Medical
  • Pages : 283
  • ISBN 10 : 9789811561412

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Signal and Image Processing Techniques for the Development of Intelligent Healthcare Systems Excerpt :

This book comprehensively reviews the various automated and semi-automated signal and image processing techniques, as well as deep-learning-based image analysis techniques, used in healthcare diagnostics. It highlights a range of data pre-processing methods used in signal processing for effective data mining in remote healthcare, and discusses pre-processing using filter techniques, noise removal, and contrast-enhanced methods for improving image quality. The book discusses the status quo of artificial intelligence in medical applications, as well as its future. Further, it offers a glimpse of feature extraction methods for reducing dimensionality and extracting discriminatory information hidden in biomedical signals. Given its scope, the book is intended for academics, researchers and practitioners interested in the latest real-world technological innovations.

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

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

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

Handbook of Research on Advancements of Artificial Intelligence in Healthcare Engineering Book
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Handbook of Research on Advancements of Artificial Intelligence in Healthcare Engineering


  • Author : Sisodia, Dilip Singh
  • Publisher : IGI Global
  • Release Date : 2020-02-28
  • Genre: Medical
  • Pages : 420
  • ISBN 10 : 9781799821229

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Handbook of Research on Advancements of Artificial Intelligence in Healthcare Engineering Excerpt :

Artificial intelligence (AI) is revolutionizing every aspect of human life including human healthcare and wellbeing management. Various types of intelligent healthcare engineering applications have been created that help to address patient healthcare and outcomes such as identifying diseases and gathering patient information. Advancements in AI applications in healthcare continue to be sought to aid rapid disease detection, health monitoring, and prescription drug tracking. TheHandbook of Research on Advancements of Artificial Intelligence in Healthcare Engineering is an essential scholarly publication that provides comprehensive research on the possible applications of machine learning, deep learning, soft computing, and evolutionary computing techniques in the design, implementation, and optimization of healthcare engineering solutions. Featuring a wide range of topics such as genetic algorithms, mobile robotics, and neuroinformatics, this book is ideal for engineers, technology developers, IT consultants, hospital administrators, academicians, healthcare professionals, practitioners, researchers, and students.

Biomedical Signal and Image Processing in Patient Care Book
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Biomedical Signal and Image Processing in Patient Care


  • Author : Kolekar, Maheshkumar H.
  • Publisher : IGI Global
  • Release Date : 2017-08-11
  • Genre: Technology & Engineering
  • Pages : 312
  • ISBN 10 : 9781522528302

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Biomedical Signal and Image Processing in Patient Care Excerpt :

In healthcare systems, medical devices help physicians and specialists in diagnosis, prognosis, and therapeutics. As research shows, validation of medical devices is significantly optimized by accurate signal processing. Biomedical Signal and Image Processing in Patient Care is a pivotal reference source for progressive research on the latest development of applications and tools for healthcare systems. Featuring extensive coverage on a broad range of topics and perspectives such as telemedicine, human machine interfaces, and multimodal data fusion, this publication is ideally designed for academicians, researchers, students, and practitioners seeking current scholarly research on real-life technological inventions.

Artificial Intelligence in Healthcare Book

Artificial Intelligence in Healthcare


  • Author : Adam Bohr
  • Publisher : Academic Press
  • Release Date : 2020-06-21
  • Genre: Computers
  • Pages : 378
  • ISBN 10 : 9780128184394

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Artificial Intelligence in Healthcare Excerpt :

Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. Highlights different data techniques in healthcare data analysis, including machine learning and data mining Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks Includes applications and case studies across all areas of AI in healthcare data

Artificial Intelligence in Healthcare and Medicine Book

Artificial Intelligence in Healthcare and Medicine


  • Author : Kayvan Najarian
  • Publisher : CRC Press
  • Release Date : 2022-04-06
  • Genre: Computers
  • Pages : 300
  • ISBN 10 : 9781000565812

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Artificial Intelligence in Healthcare and Medicine Excerpt :

This book provides a comprehensive overview of the recent developments in clinical decision support systems, precision health, and data science in medicine. The book targets clinical researchers and computational scientists seeking to understand the recent advances of artificial intelligence (AI) in health and medicine. Since AI and its applications are believed to have the potential to revolutionize healthcare and medicine, there is a clear need to explore and investigate the state-of-the-art advancements in the field. This book provides a detailed description of the advancements, challenges, and opportunities of using AI in medical and health applications. Over 10 case studies are included in the book that cover topics related to biomedical image processing, machine learning for healthcare, clinical decision support systems, visualization of high dimensional data, data security and privacy, bioinformatics, and biometrics. The book is intended for clinical researchers and computational scientists seeking to understand the recent advances of AI in health and medicine. Many universities may use the book as a secondary training text. Companies in the healthcare sector can greatly benefit from the case studies covered in the book. Moreover, this book also: Provides an overview of the recent developments in clinical decision support systems, precision health, and data science in medicine Examines the advancements, challenges, and opportunities of using AI in medical and health applications Includes 10 cases for practical application and reference Kayvan Najarian is a Professor in the Department of Computational Medicine and Bioinformatics, Department of Electrical Engineering and Computer Science, and Department of Emergency Medicine at the University of Michigan, Ann Arbor. Delaram Kahrobaei is the University Dean for Research at City University of New York (CUNY), a Professor of Computer Science and Mathematics, Queens College CUNY, and the former Chair of Cyber Secur

Ai Enabled Smart Healthcare Using Biomedical Signals Book

Ai Enabled Smart Healthcare Using Biomedical Signals


  • Author : Rahul Kumar Chaurasiya
  • Publisher : Medical Information Science Reference
  • Release Date : 2022-05-27
  • Genre: Uncategoriezed
  • Pages : null
  • ISBN 10 : 1668439476

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Ai Enabled Smart Healthcare Using Biomedical Signals Excerpt :

Technological advancements have enhanced all functions of society and revolutionized the healthcare field. Smart healthcare applications and practices have grown within the past decade, strengthening overall care. Biomedical signals observe physiological activities, which provide essential information to healthcare professionals. Biomedical signal processing can be optimized through artificial intelligence (AI) and machine learning (ML), presenting the next step towards smart healthcare. AI-Enabled Smart Healthcare Using Biomedical Signals will not only cover the mathematical description of the AI- and ML-based methods, but also analyze and demonstrate the usability of different AI methods for a range of biomedical signals. The book covers all types of biomedical signals helpful for smart healthcare applications. Covering topics such as automated diagnosis, emotion identification, and frequency discrimination techniques, this premier reference source is an excellent resource for healthcare administration, biomedical engineers, medical laboratory technicians, medical technology assistants, computer scientists, libraries, students and faculty of higher education, researchers, and academicians.