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Conference Paper: Identifying the location of spinal cord injury by support vector machines using time-frequency features of somatosensory evoked potentials

TitleIdentifying the location of spinal cord injury by support vector machines using time-frequency features of somatosensory evoked potentials
Authors
KeywordsSomatosensory evoked potentials
Spinal cord injury
Support vector machine
Time-frequency analysis
Issue Date2016
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6598376
Citation
The 2016 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA 2016), Budapest, Hungary, 27-28 July 2016. In Conference Proceedings, 2016, p. 1-5 How to Cite?
AbstractSomatosensory evoked potentials (SEP) have been found to contain a series of time-frequency components that conveys information about the location of neurological deficits within the spinal cord. This study aims to develop a classification system for identifying the location of neurological deficit in cervical spinal cord based on the time-frequency patterns of SEPs. Waveforms of SEPs after compressive injuries at various locations (C4, C5, and C6) of rats' spinal cord were decomposed into a series of time-frequency components (TFCs) by a high resolution time-frequency analysis method, matching pursuit (MP). A classification system was build according to the distributional distinction of these TFCs among different levels using support vector machine (SVM). This distinction manifests itself in different categories of SEP TFCs. High-energy TFCs of normal state SEP have significantly higher power and frequency compared with those of injury state SEP. The level of C5 is characterized by a unique distribution pattern of middle-energy TFCs. And the difference between C4 and C6 level is evidenced by the distribution pattern of low-energy TFCs. The proposed classification system was proved to be able to distinguish the four functional status (normal, injury at C4, C5, and C6) with an accuracy of 80.17%. © 2016 IEEE.
DescriptionTechnical Papers - Session 4: Computational Intelligence for Medical and Bioengineering Applications
Persistent Identifierhttp://hdl.handle.net/10722/232513
ISBN

 

DC FieldValueLanguage
dc.contributor.authorWang, Y-
dc.contributor.authorHu, Y-
dc.date.accessioned2016-09-20T05:30:33Z-
dc.date.available2016-09-20T05:30:33Z-
dc.date.issued2016-
dc.identifier.citationThe 2016 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA 2016), Budapest, Hungary, 27-28 July 2016. In Conference Proceedings, 2016, p. 1-5-
dc.identifier.isbn978-146739759-9-
dc.identifier.urihttp://hdl.handle.net/10722/232513-
dc.descriptionTechnical Papers - Session 4: Computational Intelligence for Medical and Bioengineering Applications-
dc.description.abstractSomatosensory evoked potentials (SEP) have been found to contain a series of time-frequency components that conveys information about the location of neurological deficits within the spinal cord. This study aims to develop a classification system for identifying the location of neurological deficit in cervical spinal cord based on the time-frequency patterns of SEPs. Waveforms of SEPs after compressive injuries at various locations (C4, C5, and C6) of rats' spinal cord were decomposed into a series of time-frequency components (TFCs) by a high resolution time-frequency analysis method, matching pursuit (MP). A classification system was build according to the distributional distinction of these TFCs among different levels using support vector machine (SVM). This distinction manifests itself in different categories of SEP TFCs. High-energy TFCs of normal state SEP have significantly higher power and frequency compared with those of injury state SEP. The level of C5 is characterized by a unique distribution pattern of middle-energy TFCs. And the difference between C4 and C6 level is evidenced by the distribution pattern of low-energy TFCs. The proposed classification system was proved to be able to distinguish the four functional status (normal, injury at C4, C5, and C6) with an accuracy of 80.17%. © 2016 IEEE.-
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 Proceedings-
dc.rightsIEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications Proceedings. Copyright © IEEE.-
dc.rights©2016 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.subjectSomatosensory evoked potentials-
dc.subjectSpinal cord injury-
dc.subjectSupport vector machine-
dc.subjectTime-frequency analysis-
dc.titleIdentifying the location of spinal cord injury by support vector machines using time-frequency features of somatosensory evoked potentials-
dc.typeConference_Paper-
dc.identifier.emailHu, Y: yhud@hku.hk-
dc.identifier.authorityHu, Y=rp00432-
dc.description.natureLink_to_subscribed_fulltext-
dc.identifier.doi10.1109/CIVEMSA.2016.7524315-
dc.identifier.scopuseid_2-s2.0-84984643836-
dc.identifier.hkuros263970-
dc.identifier.spage1-
dc.identifier.epage5-
dc.publisher.placeUnited States-
dc.customcontrol.immutablesml 160923-

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