Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6951189 | Biomedical Signal Processing and Control | 2016 | 10 Pages |
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Signal Processing
Authors
Jorge Barrios-Muriel, Francisco Romero, Francisco Javier Alonso, Kostas Gianikellis,