Processing of EMG Signals with High Impact of Power Line and Cardiac Interferences

Abstract

This work deals with electromyography (EMG) signal processing for the diagnosis and therapy of different muscles. Because the correct muscle activity measurement of strongly noised EMG signals is the major hurdle in medical applications, a raw measured EMG signal should be cleaned of different factors like power network interference and ECG heartbeat. Unfortunately, there are no completed studies showing full multistage signal processing of EMG recordings. In this article, the authors propose an original algorithm to perform muscle activity measurements based on raw measurements. The effectiveness of the proposed algorithm for EMG signal measurement was validated by a portable EMG system developed as a part of the EU research project and EMG raw measurement sets. Examples of removing the parasitic interferences are presented for each stage of signal processing. Finally, it is shown that the proposed processing of EMG signals enables cleaning of the EMG signal with minimal loss of the diagnostic content.

Use of Advanced Materials and Artificial Intelligence in Electromyography Signal Detection and Interpretation

“…Raw EMG data is collected from subjects by surface or needle electrodes (the example in Figure 10 is from a dataset created by M. Ozdemir et al [ 89 ] ). To remove the DC offset and low‐frequency noise, a high‐pass filter (cutoff frequency at 2 Hz [ 90 ] ) can be applied in the first pre‐processing step. Then, windowing the period in the time‐domain which we are interested in to exclude the confusion impact from other periods.…”

“…[ 104 ] K. Strzecha et al used an infinite impulse response (IIR) filter to eliminate frequency drift, power line, and ECG noise, which highly resemble EMG signals. [ 90 ] Furthermore, wavelet or model decomposition basing algorithms are equally employed in signal filtering for they can adapt to various signals. Wavelet transform could efficiently remove spectral overlapping noise but the main limitation lies in non‐data‐driven principles and the parameters’ choice will largely affect filtering results.…”

Evaluation of Simple Algorithms for Proportional Control of Prosthetic Hands Using Intramuscular Electromyography

“…Namely, the powerline noise was superimposed with the iEMG signal, which was relatively weak compared to the sEMG. To remove the powerline base frequency (f0) and its harmonics (f0 × i, i = 2, 3, 4…), which also exceeded iEMG signal amplitude, a band-stop comb filter comprising third-order Butterworth notch filters (f0 = 50 Hz and Δf = ± 2 Hz) was applied to the recorded signals [27]. Measurement setup.…”

“…. ), which also exceeded iEMG signal amplitude, a band-stop comb filter comprising third-order Butterworth notch filters (f 0 = 50 Hz and ∆f = ± 2 Hz) was applied to the recorded signals [27]. The next pre-processing step was the segmentation of recorded signals based on the automatic data labeling performed during the recordings.…”

A smart approach to EMG envelope extraction and powerful denoising for human–machine interfaces

“…In literature, many studies describe rather sophisticated methods to reduce EMG noises, which are usually based on digital filters 16 , 17 , neural networks (NN) 18 , wavelets 19 , ensemble empirical mode decomposition (EEMD) 20 , canonical correlation analysis (CCA) 21 , among many others. Many of these methods are currently not suitable for real-time operation.…”