Denoising of HD-sEMG signals using canonical correlation analysis.
High-density surface electromyography (HD-sEMG) is a recent technique that overcomes the limitations of monopolar and bipolar sEMG recordings and enables the collection of physiological and topographical informations concerning muscle activation. However, HD-sEMG channels are usually contaminated by...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 55; no. 3; pp. 375 - 389 |
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| Autores principales: | , , , , , , , , |
| Formato: | equations & formulas pictorial research tables/charts tracings Journal Article |
| Publicado: |
Springer Nature
Mar2017
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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=121412398&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 121412398 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Mar2017 vid: 55 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 121412398 121412398 NLM27221811 121412398 10.1007/s11517-016-1521-x NLM27221811 121412398 ppf: 375 ppct: 14 formats: fmt: @attributes: type: P tig: atl: Denoising of HD-sEMG signals using canonical correlation analysis. aug: au: Al Harrach, M. Boudaoud, S. Hassan, M. Ayachi, F. Gamet, D. Grosset, J. Marin, F. Ayachi, F S Grosset, J F affil: CNRS UMR 7338 , Sorbonne Universités, Université de Technologie de Compiègne , Compiègne France sug: subj: Algorithms Signal Processing, Computer Assisted Electromyography Computer Simulation Isometric Contraction Physiology Sensitivity and Specificity Male Young Adult Human Male ab: High-density surface electromyography (HD-sEMG) is a recent technique that overcomes the limitations of monopolar and bipolar sEMG recordings and enables the collection of physiological and topographical informations concerning muscle activation. However, HD-sEMG channels are usually contaminated by noise in an heterogeneous manner. The sources of noise are mainly power line interference (PLI), white Gaussian noise (WGN) and motion artifacts (MA). The spectral components of these disruptive signals overlap with the sEMG spectrum which makes classical filtering techniques non effective, especially during low contraction level recordings. In this study, we propose to denoise HD-sEMG recordings at 20 % of the maximum voluntary contraction by using a second-order blind source separation technique, named canonical component analysis (CCA). For this purpose, a specific and automatic canonical component selection, using noise ratio thresholding, and a channel selection procedure for the selective version (sCCA) are proposed. Results obtained from the application of the proposed methods (CCA and sCCA) on realistic simulated data demonstrated the ability of the proposed approach to retrieve the original HD-sEMG signals, by suppressing the PLI and WGN components, with high accuracy (for five different simulated noise dispersions using the same anatomy). Afterward, the proposed algorithms are employed to denoise experimental HD-sEMG signals from five healthy subjects during biceps brachii contractions following an isometric protocol. Obtained results showed that PLI and WGN components could be successfully removed, which enhances considerably the SNR of the channels with low SNR and thereby increases the mean SNR value among the grid. Moreover, the MA component is often isolated on specific estimated sources but requires additional signal processing for a total removal. In addition, comparative study with independent component analysis, CCA-wavelet and CCA-empirical mode decomposition (EMD) proved a higher efficiency of the presented method over existing denoising techniques and demonstrated pointless a second filtering stage for denoising HD-sEMG recordings at this contraction level. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts tracings Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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