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Removing artifacts from biomedical signals, such as surface electromyography (sEMG), has become a major research topic in biomedical signal processing. In electromyography signals, a source of contamination is the electrophysiological signal of the heart (ECG signals). This contamination influences features extracted from the sEMG, especially during low-activity measurements of the muscles such as...
Electroencephalographic (EEG) recordings are, most of the times, corrupted by spurious artifacts, which should be rejected or cleaned by the practitioner. As human scalp EEG screening is error-prone, automatic artifact detection is an issue of capital importance, to ensure objective and reliable results. In this paper we propose a new approach for discrimination of muscular activity in the human scalp...
After knee or ankle injury, Freeman has proposed a rehabilitation program consisting in a prolonged maintain of monopodal equilibrium on an unstable plateform. The efficacy of such programs, often debated, is evaluated in the present study by a quantification of equilibrium criteria and electromyographical activities along the rehabilitation program. Our aim is to detect all events in the four EMG...
Both independent component analysis (ICA) and principal component analysis (PCA) were used in this study to evaluate their effects in data reduction in the hand motion identification using surface electromyogram (SEMG) and stationary wavelet transformation. The results indicate that both methods increase the number of training epochs of the artificial neural network. The unsupervised fast ICA reduces...
In this study, the methods of wavelet threshold de-noising and independent component analysis (ICA) are introduced. ICA is a novel signal processing technique based on high order statistics, and is used to separate independent components from measurements. The extended ICA algorithm does not need to calculate the higher order statistics, converges fast, and can be used to separate subGaussian and...
The purpose of this study is to classify the uterine contractions in the electromyography (EMG) signal. As the frequency content of the contraction changes from one woman to another and during the pregnancy, wavelet decomposition is used to extract the parameters of each contraction, and an unsupervised statistical classification method based on Fisher test is used to classify events. A principal...
Wavelet transformation (WT) and wavelet packet transformation (WPT) are used in this paper to eliminate the noises of surface EMG sampled from the lower limb during the subjects of study walked normally on the flat. By comparing the denoise result, WPT wins an advantage of WP with the same threshold. And the result shows that if the suitable scale and the threshold are selected, the high-frequency...
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