
Compressive Sensing in Healthcare
- 1st Edition - May 20, 2020
- Editors: Mahdi Khosravy, Nilanjan Dey, Carlos A. Duque
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 8 2 1 2 4 7 - 9
- eBook ISBN:9 7 8 - 0 - 1 2 - 8 2 1 2 4 8 - 6
Compressive Sensing in Healthcare, part of the Advances in Ubiquitous Sensing Applications for Healthcare series gives a review on compressive sensing techniques in a practical w… Read more
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Compressive Sensing in Healthcare, part of the Advances in Ubiquitous Sensing Applications for Healthcare series gives a review on compressive sensing techniques in a practical way, also presenting deterministic compressive sensing techniques that can be used in the field. The focus of the book is on healthcare applications for this technology. It is intended for both the creators of this technology and the end users of these products. The content includes the use of EEG and ECG, plus hardware and software requirements for building projects. Body area networks and body sensor networks are explored.
- Provides a toolbox for compressive sensing in health, presenting both mathematical and coding information
- Presents an intuitive introduction to compressive sensing, including MATLAB tutorials
- Covers applications of compressive sensing in health care
1. Compressive sensing theoretical foundations in a nutshell
2. Recovery in compressive sensing: a review
3. A descriptive review to sparsity measures
4. Compressive sensing in practice and potential advancements
5. A review of deterministic sensing matrices
6. Deterministic compressive sensing by chirp codes: A descriptive tutorial
7. Deterministic compressive sensing by chirp codes: A MATLAB® tutorial
8. Cyber physical systems for healthcare applications using compressive sensing
9. Compressive sensing of electrocardiogram
10. Multichannel ECG reconstruction based on joint compressed sensing for healthcare applications
11. Neural signal compressive sensing
12. Level-crossing sampling: principles, circuits, and processing for healthcare applications
13. Compressive sensing of electroencephalogram: a review
14. Calibrationless parallel compressed sensing reconstruction for rapid magnetic resonance imaging
- Edition: 1
- Published: May 20, 2020
- Language: English
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Mahdi Khosravy
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Nilanjan Dey
Nilanjan Dey (Senior Member, IEEE) received the B.Tech., M.Tech. in information technology from West Bengal Board of Technical University and Ph.D. degrees in electronics and telecommunication engineering from Jadavpur University, Kolkata, India, in 2005, 2011, and 2015, respectively. Currently, he is Associate Professor with the Techno International New Town, Kolkata and a visiting fellow of the University of Reading, UK. He has authored over 300 research articles in peer-reviewed journals and international conferences and 40 authored books. His research interests include medical imaging and machine learning. Moreover, he actively participates in program and organizing committees for prestigious international conferences, including World Conference on Smart Trends in Systems Security and Sustainability (WorldS4), International Congress on Information and Communication Technology (ICICT), International Conference on Information and Communications Technology for Sustainable Development (ICT4SD) etc.
He is also the Editor-in-Chief of International Journal of Ambient Computing and Intelligence, Associate Editor of IEEE Transactions on Technology and Society and series Co-Editor of Springer Tracts in Nature-Inspired Computing and Data-Intensive Research from Springer Nature and Advances in Ubiquitous Sensing Applications for Healthcare from Elsevier etc. Furthermore, he was an Editorial Board Member Complex & Intelligence Systems, Springer, Applied Soft Computing, Elsevier and he is an International Journal of Information Technology, Springer, International Journal of Information and Decision Sciences etc. He is a Fellow of IETE and member of IE, ISOC etc.
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