Outlier detection in high-density surface electromyographic signals.
Recently 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...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 50; no. 1; pp. 79 - 90 |
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| Autores principales: | , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Jan2012
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104623637&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104623637 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Jan2012 vid: 50 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104623637 NLM21698432 2011421939 10.1007/s11517-011-0790-7 NLM21698432 104623637 ppf: 79 ppct: 11 formats: fmt: @attributes: type: P tig: atl: Outlier detection in high-density surface electromyographic signals. aug: au: Marateb HR Rojas-Martínez M Mansourian M Merletti R Mañanas Villanueva MA Marateb, Hamid R Rojas-Martínez, Monica Mansourian, Marjan Merletti, Roberto Villanueva, Miguel A Mañanas affil: Laboratory for Engineering of the Neuromuscular Systems, Department of Electronics, Politecnico di Torino, Turin, Italy sug: subj: Electromyography Methods Signal Processing, Computer Assisted Adult Algorithms Human Male Muscle, Skeletal Physiology Sensitivity and Specificity Young Adult Adult: 19-44 years Male ab: Recently 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 near-zero 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. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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