About the Book
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.
Table of Contents:
1. Feature engineering
2. Heart rate variability
3. Understanding the suitabillity of parametric modeling techniques in detecting the changes in the HRV signals acquired from cannabis consuming and nonconsuming Indian paddy-field workers
4. Patient-specific ECG beat classification using EMD and deep learning-based technique
5. Empirical wavelet transform and deep learning-based technique for ECG beat classification
6. Development of an Internet-of-Things (IoT)-based pill monitoring device for geriatric patients
7. Biomedical robotics
8. Combating COVID-19 by implying machine learning predictions and projections
9. Deep learning methods for analysis of neural signals: From conventional neural network to graph neural network
10. Improved extraction of the extreme thermal regions of breast IR images
11. New metrics to asses the subtle changes of the heart's electromagnetic field
12. The role of optimal and modified lead systems in electrocardiogram
13. Adaptive rate EEG processing and machine learning-based efficient recognition of epilepsy
14. Multimodal microscopy: A novel low-cost microscope designed for food and biological applications
About the Author :
Dr. Kunal Pal is a Professor in the Department of Biotechnology and Medical Engineering, National Institute of Technology Rourkela, India. His major research interests revolve around biomedical signal processing, biomedical equipment design, soft materials, and controlled drug delivery. He has published more than 100 publications in SCI-cited journals of high repute. Dr. Samit Ari received B. Tech in Electronics and Tele-communication Engineering degree from University of Kalyani and M. Tech degree in Instrumentation Engineering from University of Calcutta. He completed Ph.D in Electronics and Electrical Communication Engineering from Indian Institute of Technology (IIT), Kharagpur. He joined National Institute of Technology (NIT) Rourkela as a faculty member in 2009, where he presently holds the position of Associate Professor in the department of Electronics and Communication Engineering. He is an accomplished researcher with strong research background in Signal Processing, Image processing, Data Modeling, Biomedical Signal and Image Processing, Pattern Recognition and Artificial Intelligence. Currently, he is Professor-in-charge of Pattern Recognition and Machine Intelligence Lab, Dept. of Electronics and Communication Enginnering, NIT Rourkela. Dr. Ari is a member of IEEE and IEEE signal processing society. Currently, he is also Associate Editor of IET Image Processing Journal. He has published more than sixty research articles in reputed journals (like IEEE, IET, Elsevier, Springer etc.) and conferences. He has guided four PhD, and supervised more than fifty M. Tech and more than forty B. Tech projects. He handles number of projects funded by DRDO, SERB etc. in the area of Signal and Image processing, Artificial Intelligence. He is a reviewer of IEEE, IET, Elsevier and other international journal papers. He received his PhD in Biofluidic Engineering at Jadavpur University, India in 2016. Currently, he is working as an Assistant Professor in the Department of Biomedical Engineering, National Institute of Technology, Raipur, India. He has more than 20 research publications in peer-reviewed SCI/Scopus journal. He has also edited one book in IGI Global and published 4 book chapters. He served as guest editor to Journal of Healthcare Engineering. He is a reviewer to many journals like Journal of Bionanoscience, Journal of Healthcare Engineering, Journal of Psycholinguistic Research, Journal of Cognitive Neurodynamics, Journal of Scientific Report-Nature, Journal of Medical & Biological Engineering & Computing, Progress in Computational Fluid Dynamics: An Internal Journal, Acta Biomechanics and Bioengineering, and Journal of Computer Methods in Medicine. He is a visiting scientist to Kazan Federal University, Russia (2017-2020). His primary research area includes Tissue Engineering, Microfluidic system for tissue engineering and point-of-care device development, Organoid development, biomechanics, and Cognitive Neuroscience. Dr. Saugat Bhattacharyya pursued his graduation in Biomedical Engineering from West Bengal University of Technology, India in the year 2009 followed by post-graduation in Biomedical Engineering from Jadavpur University, Kolkata, India in the year 2011. He did a Ph.D. internship in the DEMAR project team, INRIA, Montpellier, France in 2014-2015 as part of the Erasmus Mundus-Svaagata Project Fellowship in 2014. Later, he was awarded his Ph.D. in Biomedical Engineering from Jadavpur University, Kolkata, India in the year 2015. Subsequently, he joined as a post-doctoral researcher with the BCI-LIFT project, CAMIN project team, INRIA, Montpellier, France at the end of 2015. Later, he worked in the Decision-Making Lab, BCI-NE Group, School of Computer Science & Electronics Engineering, University of Essex as a Senior Research Officer from July 2017 to April 2020. Currently he is a Lecturer in Computer Science in the School of Computing, Engineering & Intelligent Systems. His research interests are in the area of Cognitive Neuroscience, Artificial Intelligence and Machine Learning and its application in Human-Machine Interaction and Neuro-Rehabilitation. He has experience in developing intelligent neuro-technologies to improve rehabilitation and decision making. His research is primarily focussed on developing brain-computer interfacing systems based on robust signal processing, quantitative and machine learning algorithms to draw inference into an users' state of mind through their neural and other physiological signals. He has more than 40 publications in SCI cited journals of high repute, book chapters and peer-reviewed conferences with citations more than 600.
