About the Book
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.
Table of Contents:
1. Introduction to Computer-Aided Medical Diagnosis Systems
2. Advanced Signal Processing and Machine Learning Techniques
3. EEG-Based Imagined Speech Recognition for Brain-Computer Applications
4. Automated Emotion Detection Using Multi-Modal Data
5. Visual Cognitive Systems Using EEG and MEG for BCI Applications
6. ECG Sensor-Based Devices for Cardiac Disease Diagnosis
7. Automated Detection of Neurological Disorders via Voiced Speech Patterns
8. EEG-Based Diagnosis Systems for Sleep Disorders
9. Automated Brain Cancer Diagnosis Using MRI
10. Automated Eye Disease Diagnosis Using Ophthalmoscopic Images
11. PPG-Based Diagnosis System for Cardiovascular Disorders
12. EMG Signal-Based Devices for Neuromuscular Diseases
13. Wearable Systems for Real-Time Disease Diagnosis and Predictive Analytics
14. Computer-Aided Detection of Thoracic Diseases Using X-Ray Images
15. Computer-Aided Detection of Kidney Diseases Using Ultrasound Images
16. IoT-Enabled Diagnosis System for Telemedicine Applications
About the Author :
Dr. Rajesh Kumar Tripathy received a Ph.D. degree in the area of Machine Learning for Signal Processing from IIT Guwahati in 2017. He has also received BTech and Mtech degrees in Electronics & Telecommunication and Biomedical Engineering from BPUT, Odisha, and NIT Rourkela. He is currently working as an assistant professor at BITS Pilani Hyderabad, India. He has over five years of experience as an assistant professor in reputed institutions. He has published 65 papers in reputed international journals. He has also published 10 conference papers and 4 book chapters. He has filed one Indian patent in the area of ECG signal processing. Dr. Tripathy has supervised 2 Ph.D. students in machine learning and biomedical signal processing. He has also supervised 5 Mtech projects and 12 Btech projects. Currently, he supervises one Ph.D. student and 8 Btech students as supervisor. Dr. Tripathy is extensively working in the research areas such as Biomedical Signal Processing, Machine Learning and Deep Learning for Healthcare, Natural Language Processing, Time-frequency analysis, graph signal processing, vertex frequency analysis, Medical Image Processing, and Biomedical Embedded system. He received the outstanding potential for excellence in Research award (OPERA) from BITS Pilani in 2018. He has received 22.80 lacs funding from BITS Pilani through an OPERA grant to conduct high-quality research on signal processing and machine learning for healthcare data analysis. He has completed one sponsored project as a co-principal investigator from the CARS project, DRDO, India. His research papers are cited more than 1807 times on Google scholar (accessed on 19/11/2022). He has been listed as one of the top 2% of scientists based on the Elsevier and Stanford University data. Dr. Tripathy has been awarded as a certified senior data scientist from the United States Data Science institute in 2021. He is also working as the associate editor for reputed journals like IEEE Access, Frontiers in Physiology, and IET Electronics Letters. Dr. Tripathy is also working as Academic Editor for Biomed Research International Journal. He has also worked as a session chair at national and international conferences. 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).
