Results 231 to 240 of about 502,307 (287)

Biomedical signal processing using SVM

IET-UK International Conference on Information and Communication Technology in Electrical Sciences (ICTES 2007), 2007
This paper presents a novel approach for biomedical signal processing using support vector machine (SVM). Bioelectrical signals express the electrical functionality of different organs in the human body. The electrocardiogram, also called ECG signal, is one important signal among all bioelectrical signals.
S.S. Mehta, N.S. Lingayat
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In the Spotlight: Biomedical Signal Processing

IEEE Reviews in Biomedical Engineering, 2008
This article presents a review on biomedical signal processing. Discussions on traditional approaches, nonstationary and nonlinear systems, signal fusion, physiological modeling, and the MMM (multivariate, multiorgan and multiscale) paradigm are included.
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Wavelets for biomedical signal processing

Proceedings of the 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 'Magnificent Milestones and Emerging Opportunities in Medical Engineering' (Cat. No.97CH36136), 2002
In this paper, we will discuss the general idea of the wavelet representation, in its continuous and discrete versions, as well as in terms of a multiresolution approximation. In addition, the general expression for the affine class, and the relationship between the affine and Cohen's classes are presented.
M. Akay, C. Mello
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Parallel processing for biomedical signal processing

Computer, 1991
The authors describe how systematic mapping methodologies can be used to derive special-purpose processor arrays for the estimation of the bispectrum via the third-order moments. A novel design that is optimal in terms of total execution time for multiple pipelined data blocks is proposed, and it is shown how formal verification of the design can be ...
E.S. Manolakos, H.M. Stellakis, D.H. Brooks   +2 more
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Biomedical Signal Processing

The primary objective of this monograph is to provide a comprehensive exploration of biomedical signal processing, structured to introduce fundamental concepts, explore advanced techniques, and discuss clinical applications. It aims to equip readers with a solid understanding of core signal processing principles, offering practical methods for ...
Prasant P, Pradeep Devendra Gaikwad, Praveena S, Satyanarayana Naga V.K   +3 more
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Watermarking in Biomedical Signal Processing

2016
Recently, by means of technological innovation in communication networks and information, it has assisted healthcare experts across the world to seek high-quality diagnosis as well as to communicate each other as second opinions via enabling extensive and faster access to the patients’ electronic medical records, such as medical images.
Nilanjan Dey, Amira S. Ashour, Sayan Chakraborty, Sukanya Banerjee, Evgeniya Gospodinova, Mitko Gospodinov, Aboul Ella Hassanien   +6 more
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Hands-on learning in biomedical signal processing

IEEE Engineering in Medicine and Biology Magazine, 2003
For 20 years, the Harvard-MIT Division of Health Sciences and Technology (HST) has offered a graduate-level course on Biomedical Signal and Image Processing (HST582J). This course takes a practical, hands-on approach to learning about signal processing and physiological signals through the application of digital signal processing methods to biomedical ...
Julie E, Greenberg, Bertrand, Delgutte, Martha L, Gray   +2 more
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SoC-based architecture for biomedical signal processing

2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2015
Over the last decades, many algorithms have been proposed for processing biomedical signals. Most of these algorithms have been focused on the elimination of noise and artifacts existing in these signals, so they can be used for automatic monitoring and/or diagnosis applications.
R, Gutiérrez-Rivas, A, Hernández, J J, García, W, Marnane   +3 more
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Biomedical Signal Processing

2023
Ganesh R. Naik, Wellington Pinheiro dos Santos   +1 more
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Mathematics in Biomedical Signal Processing

2002
Abstract Biomedical data are typically affected by large measurement errors, largely due to the non invasive nature of the measurement process or the severe constraints to keep the input signal as low as possible for safety and bio-ethical reasons.
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