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Compressive Sensing in Healthcare. Advances in ubiquitous sensing applications for healthcare

  • Book

  • May 2020
  • Elsevier Science and Technology
  • ID: 4858551

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.

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Table of Contents

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

Authors

Mahdi Khosravy Visiting Associate Professor, Electrical Engineering Department, Federal Universit of Juiz de Fora, Sao Pedro, Brazil. Mahdi Khosravy received BSc. in Electrical Engineering (bio-electric) from Sahand University of Technology, Tabriz, Iran, and MSc. in Biomedical Engineering (bio-electric) from Beheshti University of Medical Studies, Tehran, Iran. Mahdi received his Ph.D. in the field of Information Technology from University of Ryukyus, Okinawa, Japan. He was awarded by the head of University for his excellence in research. In September 2010, he joined University of Information Science and Technology (UIST), Ohrid, Macedonia, in the capacity of assistant professor. In 2016, he established a journal in information technology (ejist.uist.edu.mk) in UIST as currently hold its executive editorship. In July 2017, he became an associate professor. From August 2018 he joined the Energy lab in University of the Ryukyus as a Visiting Researcher. Since April 2018, he is a visiting associate professor in Electrical Engineering Department, Federal Universit of Juiz de For a in Brazil. Dr. Khosravy is a member of IEEE. Nilanjan Dey Associate Professor, Department of Computer Science and Engineering, Techno International New Town, Kolkata, India..

Nilanjan Dey is an Associate Professor in the Department of Computer Science and Engineering, Techno International New Town, Kolkata, India. He is a visiting fellow of the University of Reading, UK. He also holds a position of Adjunct Professor at Ton Duc Thang University, Ho Chi Minh City, Vietnam. Previously, he held an honorary position of Visiting Scientist at Global Biomedical Technologies Inc., CA, USA (2012-2015). He was awarded his PhD from Jadavpur University in 2015. He is the Editor-in-Chief of the International Journal of Ambient Computing and Intelligence , IGI Global, USA. He is the Series Co-Editor of Springer Tracts in Nature-Inspired Computing (SpringerNature), Data-Intensive Research(SpringerNature), Advances in Ubiquitous Sensing Applications for Healthcare (Elsevier). He was an associate editor of IET Image Processing and editorial board member of Complex & Intelligent Systems, Springer Nature. He is an editorial board member of Applied Soft Computing, Elsevier. He is having 35 authored books and over 300 publications in the area of medical imaging, machine learning, computer aided diagnosis, data mining, etc. He is the Fellow of IETE and Senior member of IEEE.

Carlos A. Duque Full Professor, Electrical Engineering Faculty, Federal University of Juiz de Fora, Sao Pedro, Brazil. Carlos A. Duque received the B.S. degree in Electrical Engineering from the Federal University of Juiz de Fora, Brazil, in 1986, and the M.Sc. and Ph.D. degree from the Catholic University of Rio de Janeiro, in 1990 and 1997, in Electrical Engineering. Since 1989 he is a Full Professor in the Electrical Engineering Faculty at Federal University of Juiz de Fora (UFJF), Brazil. During 2007-2008 he joined to the Centre for Advanced Power Systems (CAPS) at Florida State University as visiting researcher. His research areas are signal processing for power systems. He is currently the head of the Research Group of Signal Processing Applied to Power Systems- UFJF and associated researcher of the Brazil National Institute of Energy. Dr. Duque has written over 120 peer-reviewed papers and chapters of technical books. Professor Duque is the author of "Power System Signal Processing for Smart Grids " book, published in 2014 by Wiley.