Biomedical Signal Processing and Artificial Intelligence in Healthcare

Biomedical Signal Processing and Artificial Intelligence in Healthcare
Author: Walid A. Zgallai
Publsiher: Academic Press
Total Pages: 268
Release: 2020-08-19
ISBN 10: 0128189460
ISBN 13: 9780128189467
Language: EN, FR, DE, ES & NL

Biomedical Signal Processing and Artificial Intelligence in Healthcare Book Review:

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

Biomedical Signal Processing and Artificial Intelligence in Healthcare
Author: Walid A. Zgallai
Publsiher: Academic Press
Total Pages: 268
Release: 2020-07-29
ISBN 10: 0128189479
ISBN 13: 9780128189474
Language: EN, FR, DE, ES & NL

Biomedical Signal Processing and Artificial Intelligence in Healthcare Book Review:

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 presentations, tutorials, application and algorithm examples.

Classification and Clustering in Biomedical Signal Processing

Classification and Clustering in Biomedical Signal Processing
Author: Dey, Nilanjan
Publsiher: IGI Global
Total Pages: 463
Release: 2016-04-07
ISBN 10: 152250141X
ISBN 13: 9781522501411
Language: EN, FR, DE, ES & NL

Classification and Clustering in Biomedical Signal Processing Book Review:

Advanced techniques in image processing have led to many innovations supporting the medical field, especially in the area of disease diagnosis. Biomedical imaging is an essential part of early disease detection and often considered a first step in the proper management of medical pathological conditions. Classification and Clustering in Biomedical Signal Processing focuses on existing and proposed methods for medical imaging, signal processing, and analysis for the purposes of diagnosing and monitoring patient conditions. Featuring the most recent empirical research findings in the areas of signal processing for biomedical applications with an emphasis on classification and clustering techniques, this essential publication is designed for use by medical professionals, IT developers, and advanced-level graduate students.

AI Enabled Smart Healthcare Using Biomedical Signals

AI Enabled Smart Healthcare Using Biomedical Signals
Author: Chaurasiya, Rahul Kumar,Agrawal, Dheeraj,Pachori, Ram Bilas
Publsiher: IGI Global
Total Pages: 322
Release: 2022-05-27
ISBN 10: 1668439484
ISBN 13: 9781668439487
Language: EN, FR, DE, ES & NL

AI Enabled Smart Healthcare Using Biomedical Signals Book Review:

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.

Signal Processing Techniques for Computational Health Informatics

Signal Processing Techniques for Computational Health Informatics
Author: Md Atiqur Rahman Ahad,Mosabber Uddin Ahmed
Publsiher: Springer Nature
Total Pages: 334
Release: 2020-10-07
ISBN 10: 3030549321
ISBN 13: 9783030549329
Language: EN, FR, DE, ES & NL

Signal Processing Techniques for Computational Health Informatics Book Review:

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 and the Internet of Medical Things in Healthcare

Machine Learning and the Internet of Medical Things in Healthcare
Author: Krishna Kant Singh,Mohamed Elhoseny,Akansha Singh,Ahmed A. Elngar
Publsiher: Academic Press
Total Pages: 290
Release: 2021-04-26
ISBN 10: 012823217X
ISBN 13: 9780128232170
Language: EN, FR, DE, ES & NL

Machine Learning and the Internet of Medical Things in Healthcare Book Review:

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

Handbook of Research on Advancements of Artificial Intelligence in Healthcare Engineering

Handbook of Research on Advancements of Artificial Intelligence in Healthcare Engineering
Author: Sisodia, Dilip Singh,Pachori, Ram Bilas,Garg, Lalit
Publsiher: IGI Global
Total Pages: 420
Release: 2020-02-28
ISBN 10: 1799821226
ISBN 13: 9781799821229
Language: EN, FR, DE, ES & NL

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

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.

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

Signal and Image Processing Techniques for the Development of Intelligent Healthcare Systems
Author: E. Priya,V. Rajinikanth
Publsiher: Springer Nature
Total Pages: 283
Release: 2020-09-21
ISBN 10: 9811561419
ISBN 13: 9789811561412
Language: EN, FR, DE, ES & NL

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

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.

Biomedical Signal and Image Processing in Patient Care

Biomedical Signal and Image Processing in Patient Care
Author: Kolekar, Maheshkumar H.,Kumar, Vinod
Publsiher: IGI Global
Total Pages: 312
Release: 2017-08-11
ISBN 10: 152252830X
ISBN 13: 9781522528302
Language: EN, FR, DE, ES & NL

Biomedical Signal and Image Processing in Patient Care Book Review:

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.

Applications of Artificial Intelligence in Medical Imaging

Applications of Artificial Intelligence in Medical Imaging
Author: Abdulhamit Subasi
Publsiher: Academic Press
Total Pages: 0
Release: 2022-11-01
ISBN 10: 0443184518
ISBN 13: 9780443184512
Language: EN, FR, DE, ES & NL

Applications of Artificial Intelligence in Medical Imaging Book Review:

Applications of Artificial Intelligence in Medical Imaging provides the description of various biomedical image analysis in disease detection using AI that can be used to incorporate knowledge obtained from different medical imaging devices such as CT, X-ray, PET and ultrasound. The book discusses the use of AI for detection of several cancer types, including brain tumor, breast, pancreatic, rectal, lung colon, and skin. In addition, it explains how AI and deep learning techniques can be used to diagnose Alzheimer's, Parkinson's, COVID-19 and mental conditions. This is a valuable resource for clinicians, researchers and healthcare professionals who are interested in learning more about AI and its impact in medical/biomedical image analysis. Discusses new deep learning algorithms for image analysis and how they are used for medical images Provides several examples for each imaging technique, along with their application areas so that readers can rely on them as a clinical decision support system Describes how new AI tools may contribute significantly to the successful enhancement of a single patient's clinical knowledge to improve treatment outcomes

Artificial Intelligence in Healthcare

Artificial Intelligence in Healthcare
Author: Adam Bohr,Kaveh Memarzadeh
Publsiher: Academic Press
Total Pages: 378
Release: 2020-06-21
ISBN 10: 0128184396
ISBN 13: 9780128184394
Language: EN, FR, DE, ES & NL

Artificial Intelligence in Healthcare Book Review:

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

Artificial Intelligence in Healthcare and Medicine
Author: Kayvan Najarian,Delaram Kahrobaei,Enrique Dominguez,Reza Soroushmehr
Publsiher: CRC Press
Total Pages: 300
Release: 2022-04-06
ISBN 10: 1000565815
ISBN 13: 9781000565812
Language: EN, FR, DE, ES & NL

Artificial Intelligence in Healthcare and Medicine Book Review:

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 Security, University of York. Enrique Domínguez is a professor in the Department of Computer Science at the University of Malaga and a member of the Biomedical Research Institute of Malaga. Reza Soroushmehr is a Research Assistant Professor in the Department of Computational Medicine and Bioinformatics and a member of the Michigan Center for Integrative Research in Critical Care, University of Michigan, Ann Arbor.

Advancing the Investigation and Treatment of Sleep Disorders Using AI

Advancing the Investigation and Treatment of Sleep Disorders Using AI
Author: Kumar, M. Rajesh,Kumar, Ranjeet,Vaithiyanathan, D.
Publsiher: IGI Global
Total Pages: 290
Release: 2021-06-25
ISBN 10: 1799880192
ISBN 13: 9781799880196
Language: EN, FR, DE, ES & NL

Advancing the Investigation and Treatment of Sleep Disorders Using AI Book Review:

There are more than 80 different sleep disorders including insomnia, sleep apnea, restless leg syndrome, hypersomnia, circadian rhythm disorders, and parasomnia. Good sleep is necessary for optimal health and can affect hormone levels and weight. The use of artificial intelligence (AI) and biomedical signals and images can help in healthcare diagnostics that are related to these and other sleep disorders. Advancing the Investigation and Treatment of Sleep Disorders Using AI presents an overview of sleep disorders based on machine intelligence methods in order to learn and explore the latest advancements, developments, methods, systems, futuristic approaches, and algorithms towards sleep disorders and to address their challenges. This book also discusses recent and future advancements in various feature extraction techniques and machine learning methods. Covering topics such as biomedical signal processing, augmented reality for clinical investigation, and sleep disorder detection, this book is essential for sleep medicine practitioners, clinical psychologists, psychiatrists, medical technologists, doctors, IT specialists, biomedical engineers, researchers, graduate students, and academicians.

Biomedical Signal Processing for Healthcare Applications

Biomedical Signal Processing for Healthcare Applications
Author: Varun Bajaj,G. R. Sinha,Chinmay Chakraborty
Publsiher: CRC Press
Total Pages: 336
Release: 2021-07-21
ISBN 10: 1000413306
ISBN 13: 9781000413304
Language: EN, FR, DE, ES & NL

Biomedical Signal Processing for Healthcare Applications Book Review:

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 in Bio Signal Analysis and Diagnostic Imaging

Machine Learning in Bio Signal Analysis and Diagnostic Imaging
Author: Nilanjan Dey,Surekha Borra,Amira Ashour,Fuqian Shi
Publsiher: Academic Press
Total Pages: 220
Release: 2019-01-15
ISBN 10: 9780128160862
ISBN 13: 0128160861
Language: EN, FR, DE, ES & NL

Machine Learning in Bio Signal Analysis and Diagnostic Imaging Book Review:

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

Advanced Methods in Biomedical Signal Processing and Analysis
Author: Kunal Pal,Samit Ari,Arindam Bit,Saugat Bhattacharyya
Publsiher: Academic Press
Total Pages: 434
Release: 2022-09-15
ISBN 10: 0323859542
ISBN 13: 9780323859547
Language: EN, FR, DE, ES & NL

Advanced Methods in Biomedical Signal Processing and Analysis Book Review:

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

Artificial Intelligence Based Brain Computer Interface

Artificial Intelligence Based Brain Computer Interface
Author: Varun Bajaj,G. R. Sinha
Publsiher: Academic Press
Total Pages: 392
Release: 2022-02-08
ISBN 10: 0323914128
ISBN 13: 9780323914123
Language: EN, FR, DE, ES & NL

Artificial Intelligence Based Brain Computer Interface Book Review:

Artificial Intelligence-Based Brain Computer Interface provides concepts of AI for modelling of non-invasive modalities of medical signals such as EEG, MRI, and FMRI. These modalities and their AI-based analysis are employed in BCI and related applications. This can help to improve the healthcare system through detection, identification, predication, analysis and classification of disease, management of chronic conditions, and delivery of health services. Artificial Intelligence-Based Brain Computer Interface emphasizes the real challenges in non-invasive input due to the complex nature of the human brain and for a variety of applications for analysis, classification and identification of different mental states. Each chapter starts with a description of a non-invasive input example and the need and motivation of the associated AI methods, along with discussions to connect the technology through BCI. Major topics include different AI methods/techniques such as Deep Neural Networks and Machine Learning algorithms for different non-invasive modalities such as EEG, MRI, FMRI for improving the diagnosis and prognosis of numerous disorders of the nervous system, cardiovascular system, musculoskeletal system, respiratory system and various organs of the body. The book also covers applications of AI in management of chronic condition, databases and delivery of health services. Various brain image modalities are analyzed and capabilities of the human brain will be exploited in BCI applications and case studies. The book presents AI methods for solving real-world problems and challenges in BCI and healthcare systems with the help of appropriate case studies and research results. Provides readers with an understanding of the key applications of Artificial Intelligence to Brain-Computer Interface for acquisition and modelling of non-invasive biomedical signal and image modalities for various conditions and disorders Integrates recent advancements of Artificial Intelligence to the evaluation of large amounts of clinical data for early detection of disorders such as Epilepsy, Alcoholism, Sleep Apnea, motor-imagery tasks classification, and others Provides readers with illustrative examples of how Artificial Intelligence can be applied to Brain-Computer Interface, including a wide range of case studies in predicting and classification of neurological disorders

Signal Processing and Machine Learning for Biomedical Big Data

Signal Processing and Machine Learning for Biomedical Big Data
Author: Ervin Sejdic,Tiago H. Falk
Publsiher: CRC Press
Total Pages: 624
Release: 2018-07-04
ISBN 10: 149877346X
ISBN 13: 9781498773461
Language: EN, FR, DE, ES & NL

Signal Processing and Machine Learning for Biomedical Big Data Book Review:

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, covering topics such as data quality, data compression, statistical and graph signal processing techniques, and deep learning and their applications within the biomedical sphere. This book’s material covers how expert domain knowledge can be used to advance signal processing and machine learning for biomedical big data applications.

Computational Intelligence in Healthcare Applications

Computational Intelligence in Healthcare Applications
Author: Rajeev Agrawal,M. A. Ansari,R. S. Anand,Sweta Sneha,Rajat Mehrotra
Publsiher: Academic Press
Total Pages: 376
Release: 2022-07-22
ISBN 10: 0323993745
ISBN 13: 9780323993746
Language: EN, FR, DE, ES & NL

Computational Intelligence in Healthcare Applications Book Review:

Computational Intelligence in Healthcare Applications discusses a variety of techniques designed to represent, enhance and empower inter-domain research based on computational intelligence in healthcare. The book serves as a reference for the pervasive healthcare domain which takes into consideration new convergent computing and other applications. The book discusses topics such as mathematical modeling in medical imaging, predictive modeling based on artificial intelligence and deep learning, smart healthcare and wearable devices, and evidence-based predictive modeling. In addition, it discusses computer-aided diagnostic for clinical inferences and pervasive and ubiquitous techniques in healthcare. This book is a valuable resource for graduate students and researchers in medical informatics, however, it is also ideal for members of the biomedical field and healthcare industry who are interested in learning more about novel technologies and their applications in the field. Presents advanced procedures to address and enhance available diagnostic methods Focuses on identifying challenges and solutions through an integrated approach that shapes a path for new research dimensions Discusses the implementation of deep learning techniques for the detection and classification of diseases

Assistive Technology Intervention in Healthcare

Assistive Technology Intervention in Healthcare
Author: Shruti Jain,Sudip Paul
Publsiher: CRC Press
Total Pages: 294
Release: 2021-12-31
ISBN 10: 1000514048
ISBN 13: 9781000514049
Language: EN, FR, DE, ES & NL

Assistive Technology Intervention in Healthcare Book Review:

Assistive Technology Intervention in Healthcare focuses on various applications of intelligent techniques in biomedical engineering and health informatics. It aims to create awareness about disability reduction and recovery of accidental disability with the help of various rehabilitative systems. Novel technologies in disability treatment, management and assistance, including healthcare devices and their utility from home to hospital, are described. The book deals with simulation, modeling, measurement, control, analysis, information extraction and monitoring of physiological data in clinical medicine and biology. Features Covers the latest evolutionary approaches to solve optimization problems in the biomedical engineering field Explains machine learning–based approaches to improvement in health engineering areas Reviews the IoT, cloud computing and data analytics in healthcare informatics Discusses modeling and simulations in the design of biomedical equipment Explores monitoring of physiological data This book is aimed at researchers and graduate students in biomedical engineering, clinical engineering and bioinformatics.