Advanced Methods in Biomedical Signal Processing and Analysis

1st Edition - September 7, 2022
Editors: Kunal Pal, Samit Ari, Arindam Bit, Saugat Bhattacharyya
Language: English
Paperback ISBN:
9 7 8 - 0 - 3 2 3 - 8 5 9 5 5 - 4
eBook ISBN:
9 7 8 - 0 - 3 2 3 - 8 5 9 5 4 - 7

Advanced Methods in Biomedical Signal Processing and Analysis presents state-of-the-art methods in biosignal processing, including recurrence quantification analysis, heart rate v… Read more

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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.

Cover image
Title page
Table of Contents
Copyright
Contributors
1: Feature engineering methods
Abstract
1: Machine learning projects development standards and feature engineering
2: Exploratory data analysis
3: Data vs features
4: Feature reduction
5: Feature selection
6: Feature dimensionality reduction
7: Concluding remarks
References
2: Heart rate variability
Abstract
Acknowledgments
1: Introduction
2: Literature review
3: Results
4: Discussion
5: Conclusion
References
3: Understanding the suitability of parametric modeling techniques in detecting the changes in the HRV signals acquired from cannabis consuming and nonconsuming Indian paddy-field workers
Abstract
1: Introduction
2: Literature review on cannabis and its legal status
3: Methods
4: Results
5: Discussion
6: Conclusion
References
4: Patient-specific ECG beat classification using EMD and deep learning-based technique
Abstract
1: Introduction
2: Database
3: Proposed methodology
4: Experimental results
5: Conclusions
References
5: Empirical wavelet transform and deep learning-based technique for ECG beat classification
Abstract
1: Introduction
2: Database
3: Proposed methodology
4: Experimental results
5: Conclusions
References
6: Development of an internet of things (IoT)-based pill monitoring device for geriatric patients
Abstract
1: Introduction
2: Literature review
3: Materials and methods
4: Results and discussions
5: Conclusion
Appendix
References
7: Biomedical robotics
Abstract
1: Introduction
2: Challenges and opportunities
References
8: Combating COVID-19 by employing machine learning predictions and projections
Abstract
1: Introduction
2: COVID-19: The 2020 pandemic
3: What is machine learning (ML)?
4: Key application of machine learning with illustrative examples: Fighting COVID-19
5: Concerns
6: Final thoughts
7: Takeaway points
References
9: Deep learning methods for analysis of neural signals: From conventional neural network to graph neural network
Abstract
1: Introduction
2: Deep learning methods
3: Graph neural network
4: Applications of GNNs on neural data
5: Discussion
References
10: Improved extraction of the extreme thermal regions of breast IR images
Abstract
Acknowledgments
1: Introduction
2: Methodology
3: Experimental results and discussion
4: Conclusion
References
11: New metrics to assess the subtle changes of the heart's electromagnetic field
Abstract
Acknowledgments
1: Introduction
2: Information technology of magnetocardiography: Basis, technical means, diagnostic metrics
3: Metrics and information technologies for the analysis of magnetocardiographic data based on two-dimensional visualization of the solution of the inverse problem of magnetostatics
4: New metrics and information technologies based on computerized electrocardiography
5: New metrics and information technologies based on heart rate variability analysis
6: Conclusions
References
Further reading
12: The role of optimal and modified lead systems in electrocardiogram
Abstract
Acknowledgments
1: Introduction
2: Modified and optimal lead systems
3: ECG signal processing
4: Advantages of optimal and modified leads in ECG signal processing
5: Conclusion
References
13: Adaptive rate EEG processing and machine learning-based efficient recognition of epilepsy
Abstract
Acknowledgments
1: Introduction
2: The electroencephalogram (EEG) for healthcare
3: Signal acquisition, preprocessing, and features extraction
4: Results
5: Discussion and conclusion
References
14: Development of a novel low-cost multimodal microscope for food and biological applications
Abstract
Acknowledgments
1: Introduction
2: Literature review
3: Materials and methods
4: Results and discussion
5: Conclusion and future scope
References
Index

Kunal Pal

Dr. Kunal Pal is currently the professor-in-charge of Medical Electronics and Instrumentation Laboratory in the Department of Biotechnology and Medical Engineering, National Institute of Technology, 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.

Affiliations and expertise

Professor in the Department of Biotechnology and Medical Engineering, National Institute of Technology Rourkela, Odisha, India.

Samit Ari

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.

Affiliations and expertise

Professor-in-charge of Pattern Recognition and Machine Intelligence Lab, Dept. of Electronics and Communication Enginnering, NIT Rourkela, India

Arindam Bit

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.

Affiliations and expertise

Assistant Professor, Department of Biomedical Engineering, National Institute of Technology, Raipur, India

Saugat Bhattacharyya

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.

Affiliations and expertise

Lecturer in Computer Science, School of Computing, Engineering and Intelligent Systems, UK