Computational Intelligence and Sensor Networks for Biomedical Systems

Computational Intelligence and Sensor Networks for Biomedical Systems

Daniel T.H. Lai (Melbourne University, Australia), Jussi Pakkanen (Helsinki University of Technology, Finland), Rezaul Begg (Victoria University, Canada) and Marimuthu Palaniswami (Melbourne University, Australia)
Copyright: © 2008 |Pages: 13
DOI: 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.

Key Terms in this Chapter

Sensor Networks: A network of sensors which are each equipped with radio transceivers.

Intelligent Monitoring Systems: Systems designed to monitor patient health signs and are capable to act on them.

Detection: Becoming aware of a present condition.

Rehabilitation: The recovery process from a disorder after medical treatment.

Prognosis: The severity and future outlook of a disease and its progression.

Diagnosis: Reaching a conclusion on a certain disorder based on observation and physical measurements.

Computational Intelligence: Mathematical formulations which simulate human learning and are implemented on computers.

Classif ica t ion: Assigning a class designation to a set of subject attributes.

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