Publication de 6 numéros par an
ISSN Imprimer: 0278-940X
ISSN En ligne: 1943-619X
Indexed in
Signal Processing and Physiological Modeling-Part I: Surface Analysis
RÉSUMÉ
Signal processing offers a wide spectrum of theories, methods, and algorithms for addressing a variety of problems ranging from noise reduction, restoration, detection (of events or changes), spatiotemporal dynamics estimation, source localization, and pattern recognition. However, the classical assumptions (stationarity, linearity, etc.) usually do not apply in real situations. Recent advances, such as time-scale and time-frequency transforms, data fusion, long-range dependence, and higher order moments, do not always provide sufficiently robust solutions. In this article, the basic properties and generic features of biomedical signals are examined using a wide range of examples. Algorithmic results are presented to show not only the potential performance but also the limitations of the processing resources at our disposal. The last section describes and discusses signal matching, scenario recognition, and data fusion.
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Porta A., Baselli G., Cerutti S., Implicit and Explicit Model-Based Signal Processing for the Analysis of Short-Term Cardiovascular Interactions, Proceedings of the IEEE, 94, 4, 2006. Crossref
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Tré Frank Du, Jacobs Reinhilde, Styven Sean, van Steenberghe Daniel, Development of a novel digital subtraction technique for detecting subtle changes in jawbone density, Clinical Oral Investigations, 10, 3, 2006. Crossref
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Cerutti Sergio, In the Spotlight: Biomedical Signal Processing, IEEE Reviews in Biomedical Engineering, 6, 2013. Crossref
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Coatrieux J.L., Integrative science: a modeling challenge, IEEE Engineering in Medicine and Biology Magazine, 23, 1, 2004. Crossref