Time-frequency component analysis of somatosensory evoked potentials in rats.
Background: 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...
| Published in: | BioMedical Engineering OnLine Vol. 8; pp. 4 - 5 |
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| Main Authors: | , , , , , , , , , |
| Format: | research Journal Article |
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BioMed Central
2009
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=105506171&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 105506171 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 1475925X 1CGX jtl: BioMedical Engineering OnLine issn: 1475925X maglogo: N pubinfo: dt: 2009 vid: 8 pid: 24147 pub: BioMed Central artinfo: ui: 105506171 NLM19203394 2010254271 10.1186/1475-925X-8-4 NLM19203394 105506171 ppf: 4 ppct: 1 formats: tig: atl: Time-frequency component analysis of somatosensory evoked potentials in rats. aug: au: Zhang ZG Yang JL Chan SC Luk KD Hu Y Zhang, Zhi-Guo Yang, Jun-Lin Chan, Shing-Chow Luk, Keith Dip-Kei Hu, Yong affil: Department of Orthopaedics and Traumatology, The University of Hong Kong, Pokfulam, Hong Kong, PR China sug: subj: Algorithms Brain Mapping Methods Electroencephalography Methods Evoked Potentials, Somatosensory Physiology Information Science Methods Parietal Lobe Physiology Signal Processing, Computer Assisted Animal Studies Rats Reproducibility of Results Sensitivity and Specificity ab: Background: 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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