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- Publisher Website: 10.1109/EMBC.2013.6610473
- Scopus: eid_2-s2.0-84886448730
- PMID: 24110660
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Conference Paper: Single-trial laser-evoked potentials feature extraction for prediction of pain perception
Title | Single-trial laser-evoked potentials feature extraction for prediction of pain perception |
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Authors | |
Keywords | Medical sciences Computer applications |
Issue Date | 2013 |
Publisher | Institute 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? |
Abstract | Pain 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 Identifier | http://hdl.handle.net/10722/189878 |
ISBN | |
ISSN | 2020 SCImago Journal Rankings: 0.282 |
DC Field | Value | Language |
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dc.contributor.author | Huang, G | en_US |
dc.contributor.author | Xiao, P | en_US |
dc.contributor.author | Hu, L | en_US |
dc.contributor.author | Hung, YS | en_US |
dc.contributor.author | Zhang, Z | en_US |
dc.date.accessioned | 2013-09-17T15:01:04Z | - |
dc.date.available | 2013-09-17T15:01:04Z | - |
dc.date.issued | 2013 | en_US |
dc.identifier.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 | en_US |
dc.identifier.isbn | 978-1-4577-0216-7 | - |
dc.identifier.issn | 1557-170X | - |
dc.identifier.uri | http://hdl.handle.net/10722/189878 | - |
dc.description.abstract | Pain 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.language | eng | en_US |
dc.publisher | Institute 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.ispartof | IEEE Engineering in Medicine and Biology Society Conference Proceedings | en_US |
dc.subject | Medical sciences | - |
dc.subject | Computer applications | - |
dc.title | Single-trial laser-evoked potentials feature extraction for prediction of pain perception | en_US |
dc.type | Conference_Paper | en_US |
dc.identifier.email | Huang, G: huanggan@hku.hk | en_US |
dc.identifier.email | Hung, YS: yshung@eee.hku.hk | en_US |
dc.identifier.email | Zhang, Z: zgzhang@eee.hku.hk | en_US |
dc.identifier.authority | Hung, YS=rp00220 | en_US |
dc.identifier.authority | Zhang, Z=rp01565 | en_US |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/EMBC.2013.6610473 | - |
dc.identifier.pmid | 24110660 | - |
dc.identifier.scopus | eid_2-s2.0-84886448730 | - |
dc.identifier.hkuros | 223282 | en_US |
dc.identifier.spage | 4207 | - |
dc.identifier.epage | 4210 | - |
dc.publisher.place | United States | - |
dc.customcontrol.immutable | sml 131024 | - |
dc.identifier.issnl | 1557-170X | - |