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Article: Time-frequency component analysis of somatosensory evoked potentials in rats

TitleTime-frequency component analysis of somatosensory evoked potentials in rats
Authors
Issue Date2009
PublisherBioMed Central Ltd. The Journal's web site is located at http://www.biomedical-engineering-online.com
Citation
Biomedical Engineering Online, 2009, v. 8 How to Cite?
AbstractBackground: Somatosensory evoked potential (SEP) signal usually contains a set of detailed temporal components measured and identified in a time domain, giving meaningful information on physiological mechanisms of the nervous system. The purpose of this study is to measure and identify detailed time-frequency components in normal SEP using time-frequency analysis (TFA) methods and to obtain their distribution pattern in the time-frequency domain. Methods: This paper proposes to apply a high-resolution time-frequency analysis algorithm, the matching pursuit (MP), to extract detailed time-frequency components of SEP signals. The MP algorithm decomposes a SEP signal into a number of elementary time-frequency components and provides a time-frequency parameter description of the components. A clustering by estimation of the probability density function in parameter space is followed to identify stable SEP time-frequency components. Results: Experimental results on cortical SEP signals of 28 mature rats show that a series of stable SEP time-frequency components can be identified using the MP decomposition algorithm. Based on the statistical properties of the component parameters, an approximated distribution of these components in time-frequency domain is suggested to describe the complex SEP response. Conclusion: This study shows that there is a set of stable and minute time-frequency components in SEP signals, which are revealed by the MP decomposition and clustering. These stable SEP components have specific localizations in the time-frequency domain. © 2009 Zhang et al; licensee BioMed Central Ltd.
Persistent Identifierhttp://hdl.handle.net/10722/58734
ISSN
2015 Impact Factor: 1.382
2015 SCImago Journal Rankings: 0.531
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
The University of Hong Kong CRCG Seed Fund
Funding Information:

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

References

 

DC FieldValueLanguage
dc.contributor.authorZhang, ZGen_HK
dc.contributor.authorYang, JLen_HK
dc.contributor.authorChan, SCen_HK
dc.contributor.authorLuk, KDKen_HK
dc.contributor.authorHu, Yen_HK
dc.date.accessioned2010-05-31T03:35:58Z-
dc.date.available2010-05-31T03:35:58Z-
dc.date.issued2009en_HK
dc.identifier.citationBiomedical Engineering Online, 2009, v. 8en_HK
dc.identifier.issn1475-925Xen_HK
dc.identifier.urihttp://hdl.handle.net/10722/58734-
dc.description.abstractBackground: Somatosensory evoked potential (SEP) signal usually contains a set of detailed temporal components measured and identified in a time domain, giving meaningful information on physiological mechanisms of the nervous system. The purpose of this study is to measure and identify detailed time-frequency components in normal SEP using time-frequency analysis (TFA) methods and to obtain their distribution pattern in the time-frequency domain. Methods: This paper proposes to apply a high-resolution time-frequency analysis algorithm, the matching pursuit (MP), to extract detailed time-frequency components of SEP signals. The MP algorithm decomposes a SEP signal into a number of elementary time-frequency components and provides a time-frequency parameter description of the components. A clustering by estimation of the probability density function in parameter space is followed to identify stable SEP time-frequency components. Results: Experimental results on cortical SEP signals of 28 mature rats show that a series of stable SEP time-frequency components can be identified using the MP decomposition algorithm. Based on the statistical properties of the component parameters, an approximated distribution of these components in time-frequency domain is suggested to describe the complex SEP response. Conclusion: This study shows that there is a set of stable and minute time-frequency components in SEP signals, which are revealed by the MP decomposition and clustering. These stable SEP components have specific localizations in the time-frequency domain. © 2009 Zhang et al; licensee BioMed Central Ltd.en_HK
dc.languageengen_HK
dc.publisherBioMed Central Ltd. The Journal's web site is located at http://www.biomedical-engineering-online.comen_HK
dc.relation.ispartofBioMedical Engineering Onlineen_HK
dc.rightsBioMedical Engineering OnLine. Copyright © BioMed Central Ltd.en_HK
dc.titleTime-frequency component analysis of somatosensory evoked potentials in ratsen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1475-925X&volume=&spage=&epage=&date=2009&atitle=Time-frequency+component+analysis+of+somatosensory+evoked+potentials+in+ratsen_HK
dc.identifier.emailZhang, ZG:zgzhang@eee.hku.hken_HK
dc.identifier.emailChan, SC:scchan@eee.hku.hken_HK
dc.identifier.emailLuk, KDK:hcm21000@hku.hken_HK
dc.identifier.emailHu, Y:yhud@hku.hken_HK
dc.identifier.authorityZhang, ZG=rp01565en_HK
dc.identifier.authorityChan, SC=rp00094en_HK
dc.identifier.authorityLuk, KDK=rp00333en_HK
dc.identifier.authorityHu, Y=rp00432en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1186/1475-925X-8-4en_HK
dc.identifier.scopuseid_2-s2.0-65349171929en_HK
dc.identifier.hkuros159377en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-65349171929&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume8en_HK
dc.identifier.isiWOS:000265536000001-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridZhang, ZG=8597618700en_HK
dc.identifier.scopusauthoridYang, JL=35799149900en_HK
dc.identifier.scopusauthoridChan, SC=13310287100en_HK
dc.identifier.scopusauthoridLuk, KDK=7201921573en_HK
dc.identifier.scopusauthoridHu, Y=7407116091en_HK
dc.identifier.citeulike4031286-

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