A simple SSA-based de-noising technique to remove ECG interference in EMG signals. (September 2016)

• Record Type: Journal Article
• Authors: Barrios-Muriel, Jorge; Romero, Francisco; Alonso, Francisco Javier; Gianikellis, Kostas
• Is Part Of: Biomedical signal processing and control. Volume 30(2016)
• Page Start: 117
• Page End: 126
• Publication Date: 2016-09
• Subjects: Electromyography -- Biomedical signal processing -- Singular Spectrum Analysis -- ECG cancellation; 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.2016.06.001
• Languages: English
• ISSNs: 1746-8094

Abstract

Highlights: An approach to remove ECG from contaminated EMG signals is presented. A new algorithm based in SSA is used to identify ECG and EMG components. The method considers frequency content to separate ECG from EMG components. This approach is validated for isometric and dynamic contractions. The new method demonstrated high performance compared against other methods. Abstract: Electromyography (EMG) signals provide significant information of muscle activity that may be used, among others, to estimate the activation stages during a certain activity or to predict fatigue. Heart activity or electrocardiogram (ECG) is one of the main contamination sources, especially in trunk muscles. This paper proposes a novel method based on Singular Spectrum Analysis (SSA) and frequency analysis to separate both signals present in the raw data. The performance of the method has been compared in time and frequency domains with traditional high-pass filtering or novel techniques such as Complete Ensemble Empirical Mode Decomposition or Wavelets analysis. The results show that for both time and frequency domains the proposed approach outperforms the other methods. Thus, the proposed SSA approach is a valid method to remove the ECG artifact from the contaminated EMG signals without using an ECG reference signal.