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dc.contributor.authorMarateb, Hamid Reza
dc.contributor.authorRojas Martínez, Mónica
dc.contributor.authorMansourian, Marjan
dc.contributor.authorMerletti, R.
dc.contributor.authorMañanas Villanueva, Miguel Ángel
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial
dc.date.accessioned2013-02-14T11:02:50Z
dc.date.created2012-01
dc.date.issued2012-01
dc.identifier.citationMarateb, H. [et al.]. Outlier detection in high-density surface electromyographic signals. "Medical and biological engineering and computing", Gener 2012, vol. 50, núm. 1, p. 79-89.
dc.identifier.issn0140-0118
dc.identifier.urihttp://hdl.handle.net/2117/17757
dc.description.abstractRecently developed techniques allow the analysis of surface EMG in multiple locations over the skin surface (high-density surface electromyography, HDsEMG). The detected signal includes information from a greater proportion of the muscle of interest than conventional clinical EMG. However, recording with many electrodes simultaneously often implies bad-contacts, which introduce large power-line interference in the corresponding channels, and short-circuits that cause nearzero single differential signals when using gel. Such signals are called ‘outliers’ in data mining. In this work, outlier detection (focusing on bad contacts) is discussed for monopolar HDsEMG signals and a new method is proposed to identify ‘bad’ channels. The overall performance of this method was tested using the agreement rate against three experts’ opinions. Three other outlier detection methods were used for comparison. The training and test sets for such methods were selected from HDsEMG signals recorded in Triceps and Biceps Brachii in the upper arm and Brachioradialis, Anconeus, and Pronator Teres in the forearm. The sensitivity and specificity of this algorithm were, respectively, 96.9 ± 6.2 and 96.4 ± 2.5 in percent in the test set (signals registered with twenty 2D electrode arrays corresponding to a total of 2322 channels), showing that this method is promising.
dc.format.extent11 p.
dc.language.isoeng
dc.subjectÀrees temàtiques de la UPC::Enginyeria biomèdica::Electrònica biomèdica
dc.subject.lcshElectromyography
dc.titleOutlier detection in high-density surface electromyographic signals
dc.typeArticle
dc.subject.lemacElectromiografia
dc.contributor.groupUniversitat Politècnica de Catalunya. SISBIO - Senyals i Sistemes Biomèdics
dc.identifier.doi10.1007/s11517-011-0790-7
dc.relation.publisherversionhttp://link.springer.com/article/10.1007%2Fs11517-011-0790-7
dc.rights.accessRestricted access - publisher's policy
local.identifier.drac11425097
dc.description.versionPostprint (published version)
dc.date.lift10000-01-01
local.citation.authorMarateb, H.; Rojas, M.; Mansourian, M.; Merletti, R.; Mañanas, M.
local.citation.publicationNameMedical and biological engineering and computing
local.citation.volume50
local.citation.number1
local.citation.startingPage79
local.citation.endingPage89


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