Results 271 to 280 of about 500,852 (334)

Editorial: Deep learning for high-dimensional sense, non-linear signal processing and intelligent diagnosis, vol II. [PDF]

Front Psychiatry
Ke H, Cai C, Yao L, Chen D.
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Combined Use of Microwave Sensing Technologies and Artificial Intelligence for Biomedical Monitoring and Imaging. [PDF]

Biosensors (Basel)
Martínez-Lozano A, Buitrago-Bernal A, Roy L, Vicente-Samper JM, Juan CG.   +4 more
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Effects of Acute Ebselen Add-on Treatment on Resting State Function Connectivity in Depressed Patients with Inadequate Response to Antidepressants

Ramli FF, Villa LM, Martens MA, Singh N, Harmer CJ, Cowen PJ, Godlewska BR.   +6 more
europepmc   +1 more source

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Biomedical Signal and Image Processing

IEEE Pulse, 2011
Generally, physiological modeling and biomedical signal processing constitute two important paradigms of biomedical engineering (BME): their fundamental concepts are taught starting from undergraduate studies and are more completely dealt with in the last years of graduate curricula, as well as in Ph.D. courses.
CERUTTI, SERGIO, BASELLI, GIUSEPPE, BIANCHI, ANNA MARIA, CAIANI, ENRICO GIANLUCA, CONTINI, DAVIDE, CUBEDDU, RINALDO, DERCOLE, FABIO, Rienzo L, LIBERATI, DIEGO, MAINARDI, LUCA, RAVAZZANI, PAOLO GIUSEPPE, RINALDI, SERGIO, SIGNORINI, MARIA GABRIELLA, TORRICELLI, ALESSANDRO   +13 more
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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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