Non-Stationary and Nonlinear Data Processing for Automated Computer-Aided Medical Diagnosis
Language: English
Published by Elsevier Science Jun 2026, 2026
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
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Condition: New
US$ 314.19
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Neuware - Non-Stationary and Nonlinear Data Processing for Automated Computer-Aided Medical Diagnosis demonstrates the applications of machine learning and deep learning combined with signal processing techniques for human-machine interface applications using EMG signals. The book includes the analysis and classification of various heart diseases based on bio-signals like electrocardiogram (ECG), photoplethysmography (PPG), and phonocardiogram (PCG) signals. Various machine learning approaches, including advanced algorithms like multivariate signal processing, time-frequency analysis, and nonlinear signal processing are covered for CAD of neural, muscular, and cardiovascular diseases. The methods for CAD of various brain disorders are also included.Presented techniques utilize advanced non-stationary and nonlinear signal processing, along with machine learning and deep learning-based classification processes. CAD methods for diagnosing various neurological diseases are based on bio-signals such as electroencephalogram (EEG) and magnetoencephalogram (MEG), as well as medical images like magnetic resonance imaging (MRI) and computerized tomography (CT). Finally, the book addresses various types of medical signals and images, integrating nonlinear and non-stationary signal processing, machine learning, and deep learning within the CAD framework for diagnosing various diseases.…
Seller Inventory # 9780443314261
- Title
- Non-Stationary and Nonlinear Data Processing for Automated Computer-Aided Medical Diagnosis
- Author
- Maarten de Vos
- Publisher
- Elsevier Science Jun 2026
- Publication year
- 2026
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 0443314268
- ISBN 13
- 9780443314261
- Item weight
- 980 grams
- Dimensions
- 236x191x23 mm
Presented techniques utilize advanced non-stationary and nonlinear signal processing, along with machine learning and deep learning-based classification processes. CAD methods for diagnosing various neurological diseases are based on bio-signals such as electroencephalogram (EEG) and magnetoencephalogram (MEG), as well as medical images like magnetic resonance imaging (MRI) and computerized tomography (CT). Finally, the book addresses various types of medical signals and images, integrating nonlinear and non-stationary signal processing, machine learning, and deep learning within the CAD framework for diagnosing various diseases.
- Focuses on various signal analysis techniques
- Addresses a wide range of applications, including the analysis and classification of signals related to neural, muscular, and cardiovascular diseases
- Covers CAD methods for diagnosing various brain disorders using bio-signals like EEG and medical images like MRI and CT scans
- Explores advanced algorithms and methodologies, such as multivariate signal processing, time-frequency analysis, and nonlinear signal processing
"Synopsis" may belong to another edition of this title.
About the Author
Ram Bilas Pachori received the B.E. degree with honours in Electronics and Communication Engineering from Rajiv Gandhi Technological University, Bhopal, India in 2001, the M.Tech. and Ph.D. degrees in Electrical Engineering from IIT Kanpur, India in 2003 and 2008, respectively. He worked as a Post-Doctoral Fellow at Charles Delaunay Institute, University of Technology of Troyes, France during 2007-2008. He served as an Assistant Professor at Communication Research Centre, International Institute of Information Technology, Hyderabad, India during 2008-2009. He served as an Assistant Professor at Department of Electrical Engineering, IIT Indore, India during 2009-2013. He worked as an Associate Professor at Department of Electrical Engineering, IIT Indore during 2013-2017 where presently he has been working as a Professor since 2017. Currently, he is also associated with the Centre for Advanced Electronics at IIT Indore. He was a Visiting Professor at Neural Dynamics of Visual Cognition Lab, Free University of Berlin, Germany during July-September, 2022. He has served as a Visiting Professor at School of Medicine, Faculty of Health and Medical Sciences, Taylor’s University, Malaysia during 2018-2019. Previously, he worked as a Visiting Scholar at Intelligent Systems Research Centre, Ulster University, Londonderry, UK during December 2014. His research interests are in the areas of Signal and Image Processing, Biomedical Signal Processing, Nonstationary Signal Processing, Speech Signal Processing, Brain-Computer Interfacing, Machine Learning, and Artificial Intelligence & Internet of Things in Healthcare. He is an Associate Editor of Electronics Letters, IEEE Transactions on Neural Systems and Rehabilitation Engineering, Biomedical Signal Processing and Control and an Editor of IETE Technical Review journal. He is a senior member of IEEE and a Fellow of IETE, IEI, and IET. He has supervised 15 Ph.D., 23 M.Tech., and 42 B.Tech. students for their theses and projects (14 Ph.D., 03 M.Tech., 01 M.S. (by Research), and 07 B.Tech. under progress). He has 270 publications which include journal papers (166), conference papers (74), books (08), and book chapters (22). He has also three patents: 01 Australian patent (granted) and 02 Indian patents (filed). He has worked on various research projects with funding support from SERB, DST, DBT, CSIR, and ICMR.
Sibasankar Padhy received the Ph.D. degree in Electronics and Electrical Engineering with a Biomedical Signal Processing specialization from the Indian Institute of Technology Guwahati in March 2017. Earlier, he received the B. Tech degree and M.Tech degree with SILVER Medal in Electronics & Telecommunication Engineering from BPUT Odisha and VSSUT Burla. After obtaining Ph.D., he worked as a postdoctoral researcher from June 2017 to Nov 2018 in ESAT (Dept. of Electrical Engineering), KU Leuven, Belgium, in the BIOTENSORS project. Later, he worked for two years as an Assistant Professor (Senior) in the School of Electronics, Vellore Institute of Technology, Vellore, Tamilnadu, India. He is currently working as an Assistant Professor at IIIT Dharwad, India. His research interests include Signal and Image Processing, Tensor Decompositions, Transform Domain Analysis, and Machine Learning. He is supervising one PhD thesis in the area of medical image processing. He is also part of two sponsored projects related to healthcare and agriculture.
Maarten De Vos received the M.Sc. degree and the Ph.D. degree in electrical engineering from KU Leuven, Leuven, Belgium, in 2005 and 2009, respectively. He has a joint appointment as a Professor with the Departments of Engineering and Medicine, KU Leuven, after being an Associate Professor with the University of Oxford, Oxford, U.K., and a Junior Professor with the University of Oldenburg, Oldenburg, Germany. His academic work focuses on AI for health, innovative biomedical monitoring, and signal analysis for daily life applications, particularly the derivation of personalized biosignatures of patient health from data acquired via wearable sensors and the incorporation of smart analytics into unobtrusive systems. In 2019, he was awarded the Martin Black Prize for the best paper in Physiological Measurements. His pioneering research in the field of mobile real-life brain monitoring has won several innovation prizes, among which was the prestigious Mobile Brain-Body Monitoring Prize in 2017. In 2023, he was also elected as a Laureate of the Flemish Academy of Sciences, discipline of Technical Sciences. He is an Associate Editor of the Journal of Biomedical and Health Informatics and on the editorial board of the Journal of Neural Engineering and Digital Medicine (Nature).
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