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.

Biomedical Signal Processing

Biomedical Signal Processing
Author: Iyad Obeid,Ivan Selesnick,Joseph Picone
Publsiher: Springer
Total Pages: 261
Release: 2021-04-13
ISBN 10: 9783030674939
ISBN 13: 3030674932
Language: EN, FR, DE, ES & NL

Biomedical Signal Processing Book Review:

This book provides an interdisciplinary look at emerging trends in signal processing and biomedicine found at the intersection of healthcare, engineering, and computer science. It examines the vital role signal processing plays in enabling a new generation of technology based on big data, and looks at applications ranging from medical electronics to data mining of electronic medical records. Topics covered include analysis of medical images, machine learning, biomedical nanosensors, wireless technologies, and instrumentation and electrical stimulation. Biomedical Signal Processing: Innovation and Applications presents tutorials and examples of successful applications, and will appeal to a wide range of professionals, researchers, and students interested in applications of signal processing, medicine, and biology.

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: 606
Release: 2018-07-04
ISBN 10: 1351061216
ISBN 13: 9781351061216
Language: EN, FR, DE, ES & NL

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

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.

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 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

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.

Biomedical Signal Analysis for Connected Healthcare

Biomedical Signal Analysis for Connected Healthcare
Author: Sridhar Krishnan
Publsiher: Academic Press
Total Pages: 334
Release: 2021-06-23
ISBN 10: 012813173X
ISBN 13: 9780128131732
Language: EN, FR, DE, ES & NL

Biomedical Signal Analysis for Connected Healthcare Book Review:

Biomedical Signal Analysis for Connected Healthcare provides rigorous coverage on several generations of techniques, including time domain approaches for event detection, spectral analysis for interpretation of clinical events of interest, time-varying signal processing for understanding dynamical aspects of complex biomedical systems, the application of machine learning principles in enhanced clinical decision-making, the application of sparse techniques and compressive sensing in providing low-power applications that are essential for wearable designs, the emerging paradigms of the Internet of Things, and connected healthcare. Provides comprehensive coverage of biomedical engineering, technologies, and healthcare applications of various physiological signals Covers vital signals, including ECG, EEG, EMG and body sounds Includes case studies and MATLAB code for selected applications

Machine Learning in Bio Signal Analysis and Diagnostic Imaging

Machine Learning in Bio Signal Analysis and Diagnostic Imaging
Author: Nilanjan Dey,Surekha Borra,Amira S. Ashour,Fuqian Shi
Publsiher: Academic Press
Total Pages: 345
Release: 2018-11-30
ISBN 10: 012816087X
ISBN 13: 9780128160879
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

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: 606
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.

Biomedical Signal Processing for Healthcare Applications

Biomedical Signal Processing for Healthcare Applications
Author: Anonim
Publsiher: Unknown
Total Pages: 135
Release: 2021-07
ISBN 10: 9780367707545
ISBN 13: 0367707543
Language: EN, FR, DE, ES & NL

Biomedical Signal Processing for Healthcare Applications Book Review:

"This book examines biomedical signal processing in which biomedical signals such as EEG, EMG, ECG are used to analyze and diagnosis various medical conditions. In combination with machine learning tools and other optimization methods, the analysis of biomedical signals will subsequently benefit the healthcare sector"--

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.

Intelligent Decision Support Systems

Intelligent Decision Support Systems
Author: Surekha Borra,Nilanjan Dey,Siddhartha Bhattacharyya,Mohamed Salim Bouhlel
Publsiher: Walter de Gruyter GmbH & Co KG
Total Pages: 193
Release: 2019-10-21
ISBN 10: 311062110X
ISBN 13: 9783110621105
Language: EN, FR, DE, ES & NL

Intelligent Decision Support Systems Book Review:

Intelligent prediction and decision support systems are based on signal processing, computer vision (CV), machine learning (ML), software engineering (SE), knowledge based systems (KBS), data mining, artificial intelligence (AI) and include several systems developed from the study of expert systems (ES), genetic algorithms (GA), artificial neural networks (ANN) and fuzzy-logic systems The use of automatic decision support systems in design and manufacturing industry, healthcare and commercial software development systems has the following benifits: Cost savings in companies, due to employment of expert system technology. Fast decision making, completion of projects in time and development of new products. Improvement in decision making capability and quality. Usage of Knowledge database and Preservation of expertise of individuals Eases complex decision problems. Ex: Diagnosis in Healthcare To address the issues and challenges related to development, implementation and application of automatic and intelligent prediction and decision support systems in domains such as manufacturing, healthcare and software product design, development and optimization, this book aims to collect and publish wide ranges of quality articles such as original research contributions, methodological reviews, survey papers, case studies and/or reports covering intelligent systems, expert prediction systems, evaluation models, decision support systems and Computer Aided Diagnosis (CAD).

Practical Machine Learning for Data Analysis Using Python

Practical Machine Learning for Data Analysis Using Python
Author: Abdulhamit Subasi
Publsiher: Academic Press
Total Pages: 534
Release: 2020-06-05
ISBN 10: 0128213809
ISBN 13: 9780128213803
Language: EN, FR, DE, ES & NL

Practical Machine Learning for Data Analysis Using Python Book Review:

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

Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques

Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques
Author: Abdulhamit Subasi
Publsiher: Academic Press
Total Pages: 456
Release: 2019-03-16
ISBN 10: 0128176733
ISBN 13: 9780128176733
Language: EN, FR, DE, ES & NL

Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques Book Review:

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

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.

Computational Tools and Techniques for Biomedical Signal Processing

Computational Tools and Techniques for Biomedical Signal Processing
Author: Singh, Butta
Publsiher: IGI Global
Total Pages: 415
Release: 2016-08-12
ISBN 10: 1522506616
ISBN 13: 9781522506614
Language: EN, FR, DE, ES & NL

Computational Tools and Techniques for Biomedical Signal Processing Book Review:

Biomedical signal processing in the medical field has helped optimize patient care and diagnosis within medical facilities. As technology in this area continues to advance, it has become imperative to evaluate other ways these computation techniques could be implemented. Computational Tools and Techniques for Biomedical Signal Processing investigates high-performance computing techniques being utilized in hospital information systems. Featuring comprehensive coverage on various theoretical perspectives, best practices, and emergent research in the field, this book is ideally suited for computer scientists, information technologists, biomedical engineers, data-processing specialists, and medical physicists interested in signal processing within medical systems and facilities.

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.

CMBEBIH 2019

CMBEBIH 2019
Author: Almir Badnjevic,Ranko Škrbić,Lejla Gurbeta Pokvić
Publsiher: Springer
Total Pages: 782
Release: 2019-05-10
ISBN 10: 3030179710
ISBN 13: 9783030179717
Language: EN, FR, DE, ES & NL

CMBEBIH 2019 Book Review:

This volume gathers the proceedings of the International Conference on Medical and Biological Engineering, which was held from 16 to 18 May 2019 in Banja Luka, Bosnia and Herzegovina. Focusing on the goal to ‘Share the Vision’, it highlights the latest findings, innovative solutions and emerging challenges in the field of Biomedical Engineering. The book covers a wide range of topics, including: biomedical signal processing, medical physics, biomedical imaging and radiation protection, biosensors and bioinstrumentation, bio-micro/nano technologies, biomaterials, biomechanics, robotics and minimally invasive surgery, and cardiovascular, respiratory and endocrine systems engineering. Further topics include bioinformatics and computational biology, clinical engineering and health technology assessment, health informatics, e-health and telemedicine, artificial intelligence and machine learning in healthcare, as well as pharmaceutical and genetic engineering. Given its scope, the book provides academic researchers, clinical researchers and professionals alike with a timely reference guide to measures for improving the quality of life and healthcare.

Internet of Things for Healthcare Technologies

Internet of Things for Healthcare Technologies
Author: Chinmay Chakraborty,Amit Banerjee,Maheshkumar H. Kolekar,Lalit Garg,Basabi Chakraborty
Publsiher: Springer Nature
Total Pages: 324
Release: 2020-06-08
ISBN 10: 9811541124
ISBN 13: 9789811541124
Language: EN, FR, DE, ES & NL

Internet of Things for Healthcare Technologies Book Review:

This book focuses on recent advances in the Internet of Things (IoT) in biomedical and healthcare technologies, presenting theoretical, methodological, well-established, and validated empirical work in these fields. Artificial intelligence and IoT are set to revolutionize all industries, but perhaps none so much as health care. Both biomedicine and machine learning applications are capable of analyzing data stored in national health databases in order to identify potential health problems, complications and effective protocols, and a range of wearable devices for biomedical and healthcare applications far beyond tracking individuals’ steps each day has emerged. These prosthetic technologies have made significant strides in recent decades with the advances in materials and development. As a result, more flexible, more mobile chip-enabled prosthetics or other robotic devices are on the horizon. For example, IoT-enabled wireless ECG sensors that reduce healthcare cost, and lead to better quality of life for cardiac patients. This book focuses on three current trends that are likely to have a significant impact on future healthcare: Advanced Medical Imaging and Signal Processing; Biomedical Sensors; and Biotechnological and Healthcare Advances. It also presents new methods of evaluating medical data, and diagnosing diseases in order to improve general quality of life.