TELKOMNIKA Telecommunication Computing Electronics and Control
Combination time-frequency and empirical wavelet transform methods for removal of composite noise in EMG signals

TELKOMNIKA Telecommunication Computing Electronics and Control
Combination time-frequency and empirical wavelet transform methods for removal of composite noise in EMG signals

CEEMDAN
Choi-Williams
Electromyography
Empirical wavelet transform
ICEEMDAN
Periodogram
Smoothed pseudo Wigner-Ville

Electromyography (EMG) is a technique used to measure the electrical
activity in muscles during movement. Doctors commonly employ this method to quickly evaluate and identify conditions such as muscle problems, dystrophy, and neuropathies. The main objective of this study was to provide a reliable and effective method for preprocessing EMG signals. EMG signals are often affected by natural noise, which can make analysis challenging. To address this issue, a new method called the empirical wavelet transform (EWT) was used in this study to remove and clean the noise. Conventional methods like time domain and frequency domain analysis are limited when dealing with the non-stationary nature of EMG signals, as they do not provide sufficient information about the signals. Therefore, a time-frequency analysis tool was necessary. This research employed three time-frequency analysis techniques: periodogram, Choi-Williams, and smoothed pseudo-Wigner-Ville. The denoising and time-frequency applications demonstrated that the
periodogram and EWT methods outperformed other previously published techniques regarding performance and accuracy. The results indicate the effectiveness of the periodogram and EWT methods in achieving this objective.

Samir Elouaham, Boujemaa Nassiri, Azzedine Dliou, Hicham Zougagh, Najib El Kamoun, Khalid El Khadiri, Sara Said

http://telkomnika.uad.ac.id

Aug 15, 2023

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