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dc.contributor.authorLádrová, Martina
dc.contributor.authorMartinek, Radek
dc.contributor.authorNedoma, Jan
dc.contributor.authorFajkus, Marcel
dc.date.accessioned2019-03-12T09:36:09Z
dc.date.available2019-03-12T09:36:09Z
dc.date.issued2019
dc.identifier.citationJournal of Biomimetics Biomaterials and Biomedical Engineering. 2019, vol. 40, p. 64-70.cs
dc.identifier.issn2296-9837
dc.identifier.issn2296-9845
dc.identifier.urihttp://hdl.handle.net/10084/134190
dc.description.abstractElectromyogram (EMG) recordings are often corrupted by the wide range of artifacts, which one of them is power line interference (PLI). The study focuses on some of the well-known signal processing approaches used to eliminate or attenuate PLI from EMG signal. The results are compared using signal-to-noise ratio (SNR), correlation coefficients and Bland-Altman analysis for each tested method: notch filter, adaptive noise canceller (ANC) and wavelet transform (WT). Thus, the power of the remaining noise and shape of the output signal are analysed. The results show that the ANC method gives the best output SNR and lowest shape distortion compared to the other methods.cs
dc.language.isoencs
dc.publisherTrans Tech Publicationscs
dc.relation.ispartofseriesJournal of Biomimetics Biomaterials and Biomedical Engineeringcs
dc.relation.urihttp://doi.org/10.4028/www.scientific.net/JBBBE.40.64cs
dc.rights© 2019 Trans Tech Publications, Switzerlandcs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectelectromyographycs
dc.subjectbiological signal processingcs
dc.subjectpower linecs
dc.subjectnotch filtercs
dc.subjectadaptive noise cancellercs
dc.subjectwavelet transformcs
dc.titleMethods of power line interference elimination in EMG signalscs
dc.typearticlecs
dc.identifier.doi10.4028/www.scientific.net/JBBBE.40.64
dc.rights.accessopenAccess
dc.type.versionpublishedVersion
dc.type.statusPeer-reviewedcs
dc.description.sourceWeb of Sciencecs
dc.description.volume40cs
dc.description.lastpage70cs
dc.description.firstpage64cs
dc.identifier.wos000459402200006


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© 2019 Trans Tech Publications, Switzerland
Except where otherwise noted, this item's license is described as © 2019 Trans Tech Publications, Switzerland