An automatic SSA-based de-noising and smoothing technique for surface electromyography signals. (April 2015)

• Record Type: Journal Article
• Authors: Romero, F.; Alonso, F.J.; Cubero, J.; Galán-Marín, G.
• Is Part Of: Biomedical signal processing and control. Volume 18(2015)
• Page Start: 317
• Page End: 324
• Publication Date: 2015-04
• Subjects: Singular spectrum analysis -- Electromyography -- Automatic smoothing -- Signal processing -- Cluster analysis; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2015.02.005
• Languages: English
• ISSNs: 1746-8094

Abstract

Abstract : Highlights: An automatic SSA-based de-noising and smoothing technique is presented. The method allows the automatic elimination of the noise present in sEMG signals. The methodology is robust to variations in the selected window length. Results are comparable to traditional MOVAG, RMS and low-pass Butterworth filtering. The method can improve the accuracy of the processing and analysis of sEMG signals. Abstract: The surface electromyography (sEMG) signal is a low amplitude signal that emanates from contracting muscles. It can be used directly to measure muscle activity (once noise has been removed) or it can be smoothed for some other application, e.g., orthoses or prostheses control. Here, an automatic heuristic procedure is presented which applies singular spectrum analysis (SSA) and cluster analysis to de-noise and smooth sEMG signals. SSA is a non-parametric technique that decomposes the original time series into a set of additive time series in which the noise present in the acquired signal can be easily identified. The proposed approach constitutes an alternative to the traditional smoothing procedures, such as moving average (MOVAG), root mean square (RMS), or low-pass Butterworth filtering that are used to extract the trend of the signal. To assess the quality of the method, the results of its application to a non-stationary sEMG signal are compared with those of other step-wise filtering and smoothing techniques.