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Computational Intelligence and Sensor Networks for Biomedical Systems

Computational Intelligence and Sensor Networks for Biomedical Systems

Daniel T.H. Lai, Jussi Pakkanen, Rezaul Begg, Marimuthu Palaniswami
Copyright: © 2008 |Pages: 13
ISBN13: 9781599048895|ISBN10: 1599048892|EISBN13: 9781599048901
DOI: 10.4018/978-1-59904-889-5.ch036
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MLA

Lai, Daniel T.H., et al. "Computational Intelligence and Sensor Networks for Biomedical Systems." Encyclopedia of Healthcare Information Systems, edited by Nilmini Wickramasinghe and Eliezer Geisler, IGI Global, 2008, pp. 261-273. https://doi.org/10.4018/978-1-59904-889-5.ch036

APA

Lai, D. T., Pakkanen, J., Begg, R., & Palaniswami, M. (2008). Computational Intelligence and Sensor Networks for Biomedical Systems. In N. Wickramasinghe & E. Geisler (Eds.), Encyclopedia of Healthcare Information Systems (pp. 261-273). IGI Global. https://doi.org/10.4018/978-1-59904-889-5.ch036

Chicago

Lai, Daniel T.H., et al. "Computational Intelligence and Sensor Networks for Biomedical Systems." In Encyclopedia of Healthcare Information Systems, edited by Nilmini Wickramasinghe and Eliezer Geisler, 261-273. Hershey, PA: IGI Global, 2008. https://doi.org/10.4018/978-1-59904-889-5.ch036

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Abstract

Sensor networks (SN) is an emergent technology which combines small sensors outfitted with wireless transmitters to form a network with more powerful sensing capabilities (Akyildiz, Su, Sankarasubramaniam, & Cayirci, 2002; Chong & Kumar, 2003). The primary application for SN technology is monitoring environmental changes making it ideal for deployment in patient monitoring systems. In contrast to other monitoring technologies such as video, SN offers a potentially cheaper solution consisting of cost effective interconnected sensors which cooperatively sense the surroundings. Individual sensor information is then fused to derive an instantaneous description of the environment. In this article, we review briefly the recent applications of CI and SN technologies in health care, mentioning some of the challenges in deploying these technologies. This is followed by an example of a biomedical system incorporating both technologies in a single paradigm. The state of current systems and their advantages over existing methods are highlighted with examples focusing primarily on intelligent automated diagnostic systems to augment clinician diagnoses and health care monitoring systems for continuous patient observation.

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