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

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 a...

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Main Authors:	Walther C., Wenzel A., Schneider M., Trommer M., Sturm K.-P., Jaeger U.
Format:	Article
Language:	English
Published:	De Gruyter 2015-09-01
Series:	Current Directions in Biomedical Engineering
Online Access:	http://www.degruyter.com/view/j/cdbme.2015.1.issue-1/cdbme-2015-0020/cdbme-2015-0020.xml?format=INT

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spelling	doaj-a13040f3c1c046aaaf07db395f6499032020-11-24T22:43:55ZengDe GruyterCurrent Directions in Biomedical Engineering2364-55042015-09-0111777910.1515/cdbme-2015-0020cdbme-2015-0020Automated classification of stages of anaesthesia by populations of evolutionary optimized fuzzy rulesWalther C.0Wenzel A.1Schneider M.2Trommer M.3Sturm K.-P.4Jaeger U.5University of Applied Sciences Schmalkalden, Faculty of Electrical Engineering, Embedded Diagnostic Systems, Schmalkalden, Germany and Fraunhofer IOSB, Advanced System Technology, Ilmenau, GermanyUniversity of Applied Sciences Schmalkalden, Faculty of Electrical Engineering, Embedded Diagnostic Systems, Schmalkalden, Germany and Fraunhofer IOSB, Advanced System Technology, Ilmenau, GermanyUniversity of Applied Sciences Schmalkalden, Faculty of Electrical Engineering, Embedded Diagnostic Systems, Schmalkalden, GermanyUniversity of Applied Sciences Schmalkalden, Faculty of Electrical Engineering, Embedded Diagnostic Systems, Schmalkalden, GermanyHospital of Schmalkalden, Department of Anaesthesia, Schmalkalden, GermanyMedical Practice of Steinbach-Hallenberg, SteinbachHallenberg, GermanyThe 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.http://www.degruyter.com/view/j/cdbme.2015.1.issue-1/cdbme-2015-0020/cdbme-2015-0020.xml?format=INT
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Walther C. Wenzel A. Schneider M. Trommer M. Sturm K.-P. Jaeger U.
spellingShingle	Walther C. Wenzel A. Schneider M. Trommer M. Sturm K.-P. Jaeger U. Automated classification of stages of anaesthesia by populations of evolutionary optimized fuzzy rules Current Directions in Biomedical Engineering
author_facet	Walther C. Wenzel A. Schneider M. Trommer M. Sturm K.-P. Jaeger U.
author_sort	Walther C.
title	Automated classification of stages of anaesthesia by populations of evolutionary optimized fuzzy rules
title_short	Automated classification of stages of anaesthesia by populations of evolutionary optimized fuzzy rules
title_full	Automated classification of stages of anaesthesia by populations of evolutionary optimized fuzzy rules
title_fullStr	Automated classification of stages of anaesthesia by populations of evolutionary optimized fuzzy rules
title_full_unstemmed	Automated classification of stages of anaesthesia by populations of evolutionary optimized fuzzy rules
title_sort	automated classification of stages of anaesthesia by populations of evolutionary optimized fuzzy rules
publisher	De Gruyter
series	Current Directions in Biomedical Engineering
issn	2364-5504
publishDate	2015-09-01
description	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.
url	http://www.degruyter.com/view/j/cdbme.2015.1.issue-1/cdbme-2015-0020/cdbme-2015-0020.xml?format=INT
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Automated classification of stages of anaesthesia by populations of evolutionary optimized fuzzy rules

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