Overview
- Advances and applications in biomedical signal & image processing, machine and deep learning are presented
- The cross disciplinary research in the fields of signal acquisition and sensing, measurement, analysis and biological sciences is described
- The role of machine & deep learning in relation to processing biomedical signals and the applications in medicine and healthcare is evaluated
- Several types of biomedical signals are introduced and their role in the prediction, diagnosis and prognosis of healthy and unhealthy conditions are explored
- Alternative therapy technique based on the biofeedback for health and wellbeing is investigated. The implications of classification of neural signals using machine & deep learning methods are covered
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About this book
This book presents the modern technological advancements and revolutions in the biomedical sector. Progress in the contemporary sensing, Internet of Things (IoT) and machine learning algorithms and architectures have introduced new approaches in the mobile healthcare. A continuous observation of patients with critical health situation is required. It allows monitoring of their health status during daily life activities such as during sports, walking and slee**. It is realizable by intelligently hybridizing the modern IoT framework, wireless biomedical implants and cloud computing. Such solutions are currently under development and in testing phases by healthcare and governmental institutions, research laboratories and biomedical companies. The biomedical signals such as electrocardiogram (ECG), electroencephalogram (EEG), Electromyography (EMG), phonocardiogram (PCG), Chronic Obstructive Pulmonary (COP), Electrooculography (EoG), photoplethysmography (PPG), and image modalitiessuch as positron emission tomography (PET), magnetic resonance imaging (MRI) and computerized tomography (CT) are non-invasively acquired, measured, and processed via the biomedical sensors and gadgets. These signals and images represent the activities and conditions of human cardiovascular, neural, vision and cerebral systems. Multi-channel sensing of these signals and images with an appropriate granularity is required for an effective monitoring and diagnosis. It renders a big volume of data and its analysis is not feasible manually. Therefore, automated healthcare systems are in the process of evolution. These systems are mainly based on biomedical signal and image acquisition and sensing, preconditioning, features extraction and classification stages. The contemporary biomedical signal sensing, preconditioning, features extraction and intelligent machine and deep learning-based classification algorithms are described.
Each chapter starts with the importance, problemstatement and motivation. A self-sufficient description is provided. Therefore, each chapter can be read independently. To the best of the editors’ knowledge, this book is a comprehensive compilation on advances in non-invasive biomedical signal sensing and processing with machine and deep learning. We believe that theories, algorithms, realizations, applications, approaches, and challenges, which are presented in this book will have their impact and contribution in the design and development of modern and effective healthcare systems.
Each chapter starts with the importance, problemstatement and motivation. A self-sufficient description is provided. Therefore, each chapter can be read independently. To the best of the editors’ knowledge, this book is a comprehensive compilation on advances in non-invasive biomedical signal sensing and processing with machine and deep learning. We believe that theories, algorithms, realizations, applications, approaches, and challenges, which are presented in this book will have their impact and contribution in the design and development of modern and effective healthcare systems.
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Keywords
- Artificial intelligence
- Biomedical signal processing
- Classification
- Chronic obstructive pulmonary (COP)
- Compression
- Dimension reduction
- Deep Learning
- Electrocardiogram (ECG)
- Electroencephalogram (EEG)
- Electromyography (EMG)
- Electrooculography (EOG)
- Empirical mode decomposition
- Fourier analysis
- Features extraction
- Machine learning
- Non-invasive sensing
- Optimization algorithms
- Pre-processing, phonocardiogram (PCG)
- Time-frequency analysis
- Wavelet transform
Table of contents (14 chapters)
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Front Matter
Editors and Affiliations
About the editors
Saeed Mian Qaisar obtained his M.S and Ph.D. degrees in Electrical and Computer Engineering from the Grenoble Alpes University, France in 2005 and 2009 respectively. Subsequently, he did a postdoctoral stay at the University of Bordeaux, France. Afterward, he worked at different R&D positions in France. Currently, he is working as an Associate Professor & Researcher of the Electrical and Computer Engineering Department, Effat University, Jeddah, Saudi Arabia. He has been awarded with the Queen Effat Award for Excellence in Teaching, May 2016. He has two patents to his credit and has more than 200 published journal articles, book chapters and conference papers. He is serving as an editor of several international journals and is also on the technical and review committees of several international journals and conferences. His current area of research interest includes Signal processing, Event-driven systems, Circuits and systems, Machine learning, Deep Learning, Sampling theory, and Embedded Systems.
Humaira Nisar has a B.S (Honours) in Electrical Engineering from the University of Engineering and Technology, Lahore, Pakistan, M.S in Nuclear Engineering from Quaid-i-Azam University, Islamabad, Pakistan, another M.S in Mechatronics, and Ph.D. in Information and Mechatronics from Gwangju Institute of Science and Technology, Gwangju, South Korea.
She has more than twenty years of research experience. Currently, she is working as a Full Professor in the Department of Electronic Engineering, Universiti Tunku Abdul Rahman, Kampar, Malaysia. Her research interests include signal and image processing, biomedical imaging, neuro-signal processing and analysis, Brain-Computer Interface, and Neurofeedback. She has published more than 200 international journal and conference papers. She is a senior member of IEEE.Abdulhamit Subasi is specialized in Artificial Intelligence,Machine Learning, Biomedical Signal and Image Analysis. Concerning application of machine learning to different fields, he wrote more than 30 book chapters and more than 200 published journal and conference papers. He is also author of the books, “Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques” and “Practical Machine Learning for Data Analysis Using Python”. Moreover, he is the Editor of the book “Applications of Artificial Intelligence in Medical Imaging”. He worked at many institutions as an academician and Georgia Institute of Technology, Georgia, USA, as a researcher. He has been awarded with the Queen Effat Award for Excellence in Research, May 2018. He worked as a professor of computer science at Effat University, Jeddah, Saudi Arabia between 2015 and 2020. Since 2020, he has been working as a Professor at Faculty of Medicine, University of Turku, Turku, Finland.
Bibliographic Information
Book Title: Advances in Non-Invasive Biomedical Signal Sensing and Processing with Machine Learning
Editors: Saeed Mian Qaisar, Humaira Nisar, Abdulhamit Subasi
DOI: https://doi.org/10.1007/978-3-031-23239-8
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023
Hardcover ISBN: 978-3-031-23238-1Published: 02 March 2023
Softcover ISBN: 978-3-031-23241-1Published: 03 March 2024
eBook ISBN: 978-3-031-23239-8Published: 01 March 2023
Edition Number: 1
Number of Pages: XVII, 373
Number of Illustrations: 41 b/w illustrations, 90 illustrations in colour
Topics: Biometrics, Health Informatics, Machine Learning