Surface electromyogram denoising using adaptive wavelet thresholding.
- Author:
Zhi LOU
;
Deng HAO
;
Xiang CHEN
;
Bo YAO
;
Jihai YANG
- Publication Type:Journal Article
- MeSH:
Algorithms;
Electromyography;
Humans;
Signal Processing, Computer-Assisted;
Wavelet Analysis
- From:
Journal of Biomedical Engineering
2014;31(4):723-728
- CountryChina
- Language:Chinese
-
Abstract:
Surface electromyogram (sEMG) may have low signal to noise ratios. An adaptive wavelet thresholding technique was developed in this study to remove noise contamination from sEMG signals. Compared with convention- al wavelet thresholding methods, the adaptive approach can adjust thresholds based on different signal to noise ratios of the processed signal, thus effectively removing noise contamination and reducing distortion of the EMG signal. The advantage of the developed adaptive thresholding method was demonstrated using simulated and experimental sEMG recordings.
