title: [Surface electromyogram denoising using adaptive wavelet thresholding].
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.
authors.forename: Zhi
authors.initials: Z
authors.lastname: Lou
authors.lastname: Deng Hao
authors.forename: Xiang
authors.initials: X
authors.lastname: Chen
authors.forename: Bo
authors.initials: B
authors.lastname: Yao
authors.forename: Jihai
authors.initials: J
authors.lastname: Yang
mesh_terms: D000465:Algorithms; D004576:Electromyography; D006801:Humans; D012815:Signal Processing, Computer-Assisted; D058067:Wavelet Analysis