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Conference Paper: A dynamic prediction model for intraoperative somatosensory evoked potential monitoring

TitleA dynamic prediction model for intraoperative somatosensory evoked potential monitoring
Authors
KeywordsSupport vector machine
Probabilistic support vector regression
Somatosensory evoked potential
Prediction model
Issue Date2015
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6598376
Citation
The 2015 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA), Shenzhen, China, 12-14 June 2015. In Conference Proceedings, 2015, p. 1-5 How to Cite?
AbstractThis study proposed a support vector regression model applied in prediction of intraoperative somatosensory evoked potential changes associated with physiological and anesthetic changes. This model was developed from probability distribution and support vector machines. The predicted results showed that observed and predicted SEP has similar variation trend with different values, with acceptable errors. With this prediction model, changes of SEP in correlation with non-surgical factors were estimated. Not only the prediction accuracy of SEP has been improved, but also provides the reliability of the classification. It will be helpful to develop an intelligent monitor model based expert system that can make a reliable decision for the potential spinal injury.
Persistent Identifierhttp://hdl.handle.net/10722/213566
ISBN

 

DC FieldValueLanguage
dc.contributor.authorCui, HY-
dc.contributor.authorXie, XB-
dc.contributor.authorXu, SP-
dc.contributor.authorHu, Y-
dc.date.accessioned2015-08-05T08:41:32Z-
dc.date.available2015-08-05T08:41:32Z-
dc.date.issued2015-
dc.identifier.citationThe 2015 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA), Shenzhen, China, 12-14 June 2015. In Conference Proceedings, 2015, p. 1-5-
dc.identifier.isbn978-1-4799-6092-7-
dc.identifier.urihttp://hdl.handle.net/10722/213566-
dc.description.abstractThis study proposed a support vector regression model applied in prediction of intraoperative somatosensory evoked potential changes associated with physiological and anesthetic changes. This model was developed from probability distribution and support vector machines. The predicted results showed that observed and predicted SEP has similar variation trend with different values, with acceptable errors. With this prediction model, changes of SEP in correlation with non-surgical factors were estimated. Not only the prediction accuracy of SEP has been improved, but also provides the reliability of the classification. It will be helpful to develop an intelligent monitor model based expert system that can make a reliable decision for the potential spinal injury.-
dc.languageeng-
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6598376-
dc.relation.ispartofIEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications-
dc.rightsIEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications. Copyright © IEEE.-
dc.rights©2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectSupport vector machine-
dc.subjectProbabilistic support vector regression-
dc.subjectSomatosensory evoked potential-
dc.subjectPrediction model-
dc.titleA dynamic prediction model for intraoperative somatosensory evoked potential monitoring-
dc.typeConference_Paper-
dc.identifier.emailHu, Y: yhud@hku.hk-
dc.identifier.authorityHu, Y=rp00432-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/CIVEMSA.2015.7158596-
dc.identifier.hkuros247341-
dc.identifier.spage1-
dc.identifier.epage5-
dc.publisher.placeUnited States-
dc.customcontrol.immutablesml 150805-

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