A comparative study of wavelet denoising of surface electromyographic signals.
This study intends to explore the wavelet denoising for optimal MUAP detection through the wavelet analysis of surface electromyographic (SEMG) signals. We first derive an estimator for signal to noise ratio and show that this estimator correlates to the quality of the reconstructed simulated signal. When applying this estimator to evaluate the SEMG signal, we find that the reconstructed signal is insensitive to the selection of denoising methods. This finding is further confirmed by the identical plots of those reconstructed SEMG data. In addition, the close correspondence of MUAP occurrences in the reconstructed signal and those in the original signal suggests that the denoising procedure can preserve the features of MUAP in the original SEMG signals.
Jiang
Ching-Fen
CF
Department of Biomedical Engineering, I-Shou University, Kaohsiung, Taiwan. cfjiang@isu.edu.tw
Kuo
Shou-Long
SL
D000465:Algorithms; D016477:Artifacts; D003198:Computer Simulation; D003201:Computers; D003627:Data Interpretation, Statistical; D004576:Electromyography / Q000295:instrumentation* / Q000379:methods; D004594:Electrophysiology / Q000295:instrumentation* / Q000379:methods; D004867:Equipment Design; D006801:Humans; D015233:Models, Statistical; D012107:Research Design; D012815:Signal Processing, Computer-Assisted / Q000295:instrumentation*