File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: An automated ECG-artifact removal method for trunk muscle surface EMG recordings

TitleAn automated ECG-artifact removal method for trunk muscle surface EMG recordings
Authors
KeywordsAutomated
ECG artifact
ICA
Surface EMG
Issue Date2010
PublisherElsevier Ltd. The Journal's web site is located at http://www.elsevier.com/locate/medengphy
Citation
Medical Engineering And Physics, 2010, v. 32 n. 8, p. 840-848 How to Cite?
AbstractThis study aimed at developing a method for automated electrocardiography (ECG) artifact detection and removal from trunk electromyography signals. Independent Component Analysis (ICA) method was applied to the simulated data set of ECG-corrupted surface electromyography (SEMG) signals. Independent Components (ICs) correspond to ECG artifact were then identified by an automated detection algorithm and subsequently removed. The detection performance of the algorithm was compared to that by visual inspection, while the artifact elimination performance was compared with Butterworth high pass filter at 30. Hz cutoff (BW HPF 30). The automated ECG-artifact detection algorithm successfully recognized the ECG source components in all data sets with a sensitivity of 100% and specificity of 99%. Better performance indicated by a significantly higher correlation coefficient (p< 0.001) with the original EMG recordings was found in the SEMG data cleaned by the ICA-based method, than that by BW HPF 30. The automated ECG-artifact removal method for trunk SEMG recordings proposed in this study was demonstrated to produce a very good detection rate and preserved essential EMG components while keeping its distortion to minimum. The automatic nature of our method has solved the problem of visual inspection by standard ICA methods and brings great clinical benefits. © 2010 IPEM.
Persistent Identifierhttp://hdl.handle.net/10722/142432
ISSN
2015 Impact Factor: 1.619
2015 SCImago Journal Rankings: 0.784
ISI Accession Number ID
Funding AgencyGrant Number
Research Grants Council of the Hong Kong SAR, ChinaGRF HKU 712408E
S.K. Yee Medical Foundation207210/203210
Funding Information:

This work was partially supported by grants from the Research Grants Council of the Hong Kong SAR, China (GRF HKU 712408E) and S.K. Yee Medical Foundation (207210/203210).

References

 

DC FieldValueLanguage
dc.contributor.authorMak, JNFen_HK
dc.contributor.authorHu, Yen_HK
dc.contributor.authorLuk, KDKen_HK
dc.date.accessioned2011-10-28T02:45:57Z-
dc.date.available2011-10-28T02:45:57Z-
dc.date.issued2010en_HK
dc.identifier.citationMedical Engineering And Physics, 2010, v. 32 n. 8, p. 840-848en_HK
dc.identifier.issn1350-4533en_HK
dc.identifier.urihttp://hdl.handle.net/10722/142432-
dc.description.abstractThis study aimed at developing a method for automated electrocardiography (ECG) artifact detection and removal from trunk electromyography signals. Independent Component Analysis (ICA) method was applied to the simulated data set of ECG-corrupted surface electromyography (SEMG) signals. Independent Components (ICs) correspond to ECG artifact were then identified by an automated detection algorithm and subsequently removed. The detection performance of the algorithm was compared to that by visual inspection, while the artifact elimination performance was compared with Butterworth high pass filter at 30. Hz cutoff (BW HPF 30). The automated ECG-artifact detection algorithm successfully recognized the ECG source components in all data sets with a sensitivity of 100% and specificity of 99%. Better performance indicated by a significantly higher correlation coefficient (p< 0.001) with the original EMG recordings was found in the SEMG data cleaned by the ICA-based method, than that by BW HPF 30. The automated ECG-artifact removal method for trunk SEMG recordings proposed in this study was demonstrated to produce a very good detection rate and preserved essential EMG components while keeping its distortion to minimum. The automatic nature of our method has solved the problem of visual inspection by standard ICA methods and brings great clinical benefits. © 2010 IPEM.en_HK
dc.languageengen_US
dc.publisherElsevier Ltd. The Journal's web site is located at http://www.elsevier.com/locate/medengphyen_HK
dc.relation.ispartofMedical Engineering and Physicsen_HK
dc.subjectAutomateden_HK
dc.subjectECG artifacten_HK
dc.subjectICAen_HK
dc.subjectSurface EMGen_HK
dc.subject.meshArtifacts-
dc.subject.meshElectrocardiography - methods-
dc.subject.meshElectromyography - methods-
dc.subject.meshMuscles-
dc.subject.meshSignal Processing, Computer-Assisted-
dc.titleAn automated ECG-artifact removal method for trunk muscle surface EMG recordingsen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1350-4533&volume=32&issue=8&spage=840&epage=848&date=2010&atitle=An+automated+ECG-artifact+removal+method+for+trunk+muscle+surface+EMG+recordingsen_US
dc.identifier.emailHu, Y:yhud@hku.hken_HK
dc.identifier.emailLuk, KDK:hcm21000@hku.hken_HK
dc.identifier.authorityHu, Y=rp00432en_HK
dc.identifier.authorityLuk, KDK=rp00333en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.medengphy.2010.05.007en_HK
dc.identifier.pmid20561810-
dc.identifier.scopuseid_2-s2.0-77956419221en_HK
dc.identifier.hkuros196985en_US
dc.identifier.hkuros189059-
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-77956419221&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume32en_HK
dc.identifier.issue8en_HK
dc.identifier.spage840en_HK
dc.identifier.epage848en_HK
dc.identifier.isiWOS:000282565800004-
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridMak, JNF=35980187600en_HK
dc.identifier.scopusauthoridHu, Y=7407116091en_HK
dc.identifier.scopusauthoridLuk, KDK=7201921573en_HK
dc.identifier.citeulike7386467-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats