automated classification of stages of anaesthesia by populations of evolutionary optimized fuzzy rules
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2015
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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.
| Reference Key |
c.2015currentautomated
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| 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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