automated classification of stages of anaesthesia by populations of evolutionary optimized fuzzy rules

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ID: 203571
2015
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Ranked #92 of 96 articles by views in materials science and engineering c

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Abstract
The detection of stages of anaesthesia is mainly performed on evaluating the vital signs of the patient. In addition the frontal one-channel electroencephalogram can be evaluated to increase the correct detection of stages of anaesthesia. As a classification model fuzzy rules are used. These rules are able to classify the stages of anaesthesia automatically and were optimized by multiobjective evolutionary algorithms. As a result the performance of the generated population of fuzzy rule sets is presented. A concept of the construction of an autonomic embedded system is introduced. This system should use the generated rules to classify the stages of anaesthesia using the frontal one-channel electroencephalogram only.
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c.2015currentautomated Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Walther C.;Wenzel A.;Schneider M.;Trommer M.;Sturm K.-P.;Jaeger U.
Journal materials science and engineering c
Year 2015
DOI
10.1515/cdbme-2015-0020
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