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Conference Paper: Automatic ECG artifact removal in the real-time SEMG recording system

TitleAutomatic ECG artifact removal in the real-time SEMG recording system
Authors
KeywordsSurface electromyography
Electrocardiography
Low back muscle
Independent component analysis
Issue Date2013
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6598376
Citation
The 2013 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA), Milan, Italy, 15-17 July 2013. In Conference Proceedings, 2013, p. 72-77 How to Cite?
AbstractThe contaminated electrocardiography (ECG) is a big problem in the surface electromyography (SEMG) signal detection and analysis. The objective of the current study is to propose and validate an algorithm for the automated feature cognition and identification for eliminating ECG artifact from the raw SEMG signals. The utilization of Independent Component Analysis (ICA) method is to decompose the raw SEMG signals into individual independent source components. After that, some of the independent source components with the characteristics of ECG artifact were detected by the automated identification algorithm and thereafter eliminated. The sensitivity and specificity of the algorithm for distinguishing ECG source components from independent source components are 100% and 99% respectively. The automated identification algorithm exhibits the prominent performance of recognition for ECG artifact and can be considered reliable and effective.
Persistent Identifierhttp://hdl.handle.net/10722/186876
ISBN

 

DC FieldValueLanguage
dc.contributor.authorHu, Yen_US
dc.contributor.authorKwok, Jen_US
dc.contributor.authorTse, Jen_US
dc.date.accessioned2013-08-20T12:22:54Z-
dc.date.available2013-08-20T12:22:54Z-
dc.date.issued2013en_US
dc.identifier.citationThe 2013 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA), Milan, Italy, 15-17 July 2013. In Conference Proceedings, 2013, p. 72-77en_US
dc.identifier.isbn978-1-4673-4703-7-
dc.identifier.urihttp://hdl.handle.net/10722/186876-
dc.description.abstractThe contaminated electrocardiography (ECG) is a big problem in the surface electromyography (SEMG) signal detection and analysis. The objective of the current study is to propose and validate an algorithm for the automated feature cognition and identification for eliminating ECG artifact from the raw SEMG signals. The utilization of Independent Component Analysis (ICA) method is to decompose the raw SEMG signals into individual independent source components. After that, some of the independent source components with the characteristics of ECG artifact were detected by the automated identification algorithm and thereafter eliminated. The sensitivity and specificity of the algorithm for distinguishing ECG source components from independent source components are 100% and 99% respectively. The automated identification algorithm exhibits the prominent performance of recognition for ECG artifact and can be considered reliable and effective.-
dc.languageengen_US
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=6598376-
dc.relation.ispartofIEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications Proceedingsen_US
dc.rightsIEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications Proceedings. Copyright © IEEE.-
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.subjectSurface electromyography-
dc.subjectElectrocardiography-
dc.subjectLow back muscle-
dc.subjectIndependent component analysis-
dc.titleAutomatic ECG artifact removal in the real-time SEMG recording systemen_US
dc.typeConference_Paperen_US
dc.identifier.emailHu, Y: yhud@hku.hken_US
dc.identifier.emailKwok, J: jerrykwl@hku.hken_US
dc.identifier.authorityHu, Y=rp00432en_US
dc.description.naturepublished_or_final_version-
dc.identifier.hkuros220336en_US
dc.identifier.spage72-
dc.identifier.epage77-
dc.publisher.placeUnited Statesen_US
dc.customcontrol.immutablesml 131016-

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