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Conference Paper: Single-trial laser-evoked potentials feature extraction for prediction of pain perception

TitleSingle-trial laser-evoked potentials feature extraction for prediction of pain perception
Authors
KeywordsMedical sciences
Computer applications
Issue Date2013
PublisherInstitute of Electrical and Electronics Engineers. The Journal's web site is located at http://www.ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000269
Citation
The 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2013), Osaka, Japan, 3-7 July 2013. In IEEE Engineering in Medicine and Biology Society Conference Proceedings, 2013, p. 4207-4210 How to Cite?
AbstractPain is a highly subjective experience, and the availability of an objective assessment of pain perception would be of great importance for both basic and clinical applications. The objective of the present study is to develop a novel approach to extract pain-related features from single-trial laser-evoked potentials (LEPs) for classification of pain perception. The single-trial LEP feature extraction approach combines a spatial filtering using common spatial pattern (CSP) and a multiple linear regression (MLR). The CSP method is effective in separating laser-evoked EEG response from ongoing EEG activity, while MLR is capable of automatically estimating the amplitudes and latencies of N2 and P2 from single-trial LEP waveforms. The extracted single-trial LEP features are used in a Naïve Bayes classifier to classify different levels of pain perceived by the subjects. The experimental results show that the proposed single-trial LEP feature extraction approach can effectively extract pain-related LEP features for achieving high classification accuracy.
Persistent Identifierhttp://hdl.handle.net/10722/189878
ISBN
ISSN

 

DC FieldValueLanguage
dc.contributor.authorHuang, Gen_US
dc.contributor.authorXiao, Pen_US
dc.contributor.authorHu, Len_US
dc.contributor.authorHung, YSen_US
dc.contributor.authorZhang, Zen_US
dc.date.accessioned2013-09-17T15:01:04Z-
dc.date.available2013-09-17T15:01:04Z-
dc.date.issued2013en_US
dc.identifier.citationThe 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2013), Osaka, Japan, 3-7 July 2013. In IEEE Engineering in Medicine and Biology Society Conference Proceedings, 2013, p. 4207-4210en_US
dc.identifier.isbn978-1-4577-0216-7-
dc.identifier.issn1557-170X-
dc.identifier.urihttp://hdl.handle.net/10722/189878-
dc.description.abstractPain is a highly subjective experience, and the availability of an objective assessment of pain perception would be of great importance for both basic and clinical applications. The objective of the present study is to develop a novel approach to extract pain-related features from single-trial laser-evoked potentials (LEPs) for classification of pain perception. The single-trial LEP feature extraction approach combines a spatial filtering using common spatial pattern (CSP) and a multiple linear regression (MLR). The CSP method is effective in separating laser-evoked EEG response from ongoing EEG activity, while MLR is capable of automatically estimating the amplitudes and latencies of N2 and P2 from single-trial LEP waveforms. The extracted single-trial LEP features are used in a Naïve Bayes classifier to classify different levels of pain perceived by the subjects. The experimental results show that the proposed single-trial LEP feature extraction approach can effectively extract pain-related LEP features for achieving high classification accuracy.-
dc.languageengen_US
dc.publisherInstitute of Electrical and Electronics Engineers. The Journal's web site is located at http://www.ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1000269-
dc.relation.ispartofIEEE Engineering in Medicine and Biology Society Conference Proceedingsen_US
dc.rightsIEEE Engineering in Medicine and Biology Society. Conference Proceedings. Copyright © Institute of Electrical and Electronics Engineers.-
dc.rights©2013 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.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectMedical sciences-
dc.subjectComputer applications-
dc.titleSingle-trial laser-evoked potentials feature extraction for prediction of pain perceptionen_US
dc.typeConference_Paperen_US
dc.identifier.emailHuang, G: huanggan@hku.hken_US
dc.identifier.emailHung, YS: yshung@eee.hku.hken_US
dc.identifier.emailZhang, Z: zgzhang@eee.hku.hken_US
dc.identifier.authorityHung, YS=rp00220en_US
dc.identifier.authorityZhang, Z=rp01565en_US
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/EMBC.2013.6610473-
dc.identifier.pmid24110660-
dc.identifier.hkuros223282en_US
dc.identifier.spage4207-
dc.identifier.epage4210-
dc.publisher.placeUnited States-
dc.customcontrol.immutablesml 131024-

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