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通过奇异值分解评估表面肌电图信号的重复性。

Assessment of repeatability of surface electromyography signals by singular value decomposition.

作者信息

Soylu Abdullah Ruhi

机构信息

Hacettepe University, School of Medicine, Biophysics Department, Sihhiye-Altindag, 06100 Ankara, Turkey.

出版信息

J Electromyogr Kinesiol. 2008 Aug;18(4):690-4. doi: 10.1016/j.jelekin.2007.02.014. Epub 2007 Apr 16.

Abstract

Singular value decomposition (SVD) of full-wave rectified surface electromyography (sEMG) signals was investigated for repeatability indices of sEMG linear envelopes (LE) during biceps curl. The SVD based repeatability indices were compared with a well-known method, the variance ratio (VR). The usefulness of the offered indices was examined by a simulation and it was applied to the sEMG LEs. The results have shown that the VRs were correlated with the SVD based indices significantly. The usefulness of the offered indices on real world EMG signals practically comes from decreasing amplitudes of the first few singular values of EMG LE matrix. If repeatability is high, singular values decay fast and vice versa. If justified by further researches, the offered indices may be used practically for repeatability measurement of sEMG LEs.

摘要

研究了全波整流表面肌电图(sEMG)信号的奇异值分解(SVD),以获取二头肌卷曲期间sEMG线性包络(LE)的重复性指标。将基于SVD的重复性指标与一种著名的方法——方差比(VR)进行了比较。通过模拟检验了所提供指标的有效性,并将其应用于sEMG LE。结果表明,VR与基于SVD的指标显著相关。所提供指标在实际肌电信号中的有效性实际上源于肌电LE矩阵前几个奇异值幅度的降低。如果重复性高,奇异值衰减快,反之亦然。如果进一步的研究证明合理,所提供的指标可实际用于sEMG LE的重复性测量。

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