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Article: Identification of detailed time-frequency components in somatosensory evoked potentials

TitleIdentification of detailed time-frequency components in somatosensory evoked potentials
Authors
KeywordsDensity estimation
K-means clustering
Matching pursuit
Somatosensory evoked potentials
Time-frequency analysis
Issue Date2010
PublisherIEEE.
Citation
Ieee Transactions On Neural Systems And Rehabilitation Engineering, 2010, v. 18 n. 3, p. 245-254 How to Cite?
AbstractSomatosensory evoked potential (SEP) usually contains a set of detailed temporal components measured and identified in time domain, providing meaningful information on physiological mechanisms of the nervous system. The purpose of this study is to reveal complex and fine time-frequency features of SEP in time-frequency domain using advanced time-frequency analysis (TFA) and pattern classification methods. A high-resolution TFA algorithm, matching pursuit (MP), was proposed to decompose a SEP signal into a string of elementary waves and to provide a time-frequency feature description of the waves. After a dimension reduction by principle component analysis (PCA), a density-guided K-means clustering was followed to identify typical waves existed in SEP. Experimental results on posterior tibial nerve SEP signals of 50 normal adults showed that a series of typical waves were discovered in SEP using the proposed MP decomposition and clustering methods. The statistical properties of these SEP waves were examined and their representative waveforms were synthesized. The identified SEP waves provided a comprehensive and detailed description of time-frequency features of SEP. © 2006 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/124662
ISSN
2015 Impact Factor: 2.583
2015 SCImago Journal Rankings: 1.385
ISI Accession Number ID
Funding AgencyGrant Number
Research Grants Council of the Hong Kong SAR, ChinaGRF HKU 7130/06E
Biomedical Engineering Centre (BMEC) of the University of Hong Kong
University of Hong Kong
Funding Information:

This work was supported in part by a grant from the Research Grants Council of the Hong Kong SAR, China (GRF HKU 7130/06E), in part by the Biomedical Engineering Centre (BMEC) of the University of Hong Kong, and in part by The University of Hong Kong CRCG Seed Fund.

References

 

DC FieldValueLanguage
dc.contributor.authorZhang, Zen_HK
dc.contributor.authorLuk, KDKen_HK
dc.contributor.authorHu, Yen_HK
dc.date.accessioned2010-10-31T10:47:09Z-
dc.date.available2010-10-31T10:47:09Z-
dc.date.issued2010en_HK
dc.identifier.citationIeee Transactions On Neural Systems And Rehabilitation Engineering, 2010, v. 18 n. 3, p. 245-254en_HK
dc.identifier.issn1534-4320en_HK
dc.identifier.urihttp://hdl.handle.net/10722/124662-
dc.description.abstractSomatosensory evoked potential (SEP) usually contains a set of detailed temporal components measured and identified in time domain, providing meaningful information on physiological mechanisms of the nervous system. The purpose of this study is to reveal complex and fine time-frequency features of SEP in time-frequency domain using advanced time-frequency analysis (TFA) and pattern classification methods. A high-resolution TFA algorithm, matching pursuit (MP), was proposed to decompose a SEP signal into a string of elementary waves and to provide a time-frequency feature description of the waves. After a dimension reduction by principle component analysis (PCA), a density-guided K-means clustering was followed to identify typical waves existed in SEP. Experimental results on posterior tibial nerve SEP signals of 50 normal adults showed that a series of typical waves were discovered in SEP using the proposed MP decomposition and clustering methods. The statistical properties of these SEP waves were examined and their representative waveforms were synthesized. The identified SEP waves provided a comprehensive and detailed description of time-frequency features of SEP. © 2006 IEEE.en_HK
dc.languageengen_HK
dc.publisherIEEE.-
dc.relation.ispartofIEEE Transactions on Neural Systems and Rehabilitation Engineeringen_HK
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rightsIEEE Transactions on Neural Systems and Rehabilitation Engineering. Copyright © IEEE.-
dc.rights©2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.-
dc.subjectDensity estimationen_HK
dc.subjectK-means clusteringen_HK
dc.subjectMatching pursuiten_HK
dc.subjectSomatosensory evoked potentialsen_HK
dc.subjectTime-frequency analysisen_HK
dc.subject.meshAdolescent-
dc.subject.meshAlgorithms-
dc.subject.meshChild-
dc.subject.meshCluster Analysis-
dc.subject.meshEvoked Potentials, Somatosensory - physiology-
dc.titleIdentification of detailed time-frequency components in somatosensory evoked potentialsen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1534-4320&volume=18&issue=3&spage=245&epage=254&date=2010&atitle=Identification+of+detailed+time-frequency+components+in+somatosensory+evoked+potentials-
dc.identifier.emailZhang, Z:zgzhang@eee.hku.hken_HK
dc.identifier.emailLuk, KDK:hcm21000@hku.hken_HK
dc.identifier.emailHu, Y:yhud@hku.hken_HK
dc.identifier.authorityZhang, Z=rp01565en_HK
dc.identifier.authorityLuk, KDK=rp00333en_HK
dc.identifier.authorityHu, Y=rp00432en_HK
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/TNSRE.2010.2043856en_HK
dc.identifier.pmid20215086-
dc.identifier.scopuseid_2-s2.0-77953242758en_HK
dc.identifier.hkuros174334en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-77953242758&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume18en_HK
dc.identifier.issue3en_HK
dc.identifier.spage245en_HK
dc.identifier.epage254en_HK
dc.identifier.isiWOS:000281782000004-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridZhang, Z=8597618700en_HK
dc.identifier.scopusauthoridLuk, KDK=7201921573en_HK
dc.identifier.scopusauthoridHu, Y=7407116091en_HK

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