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Surface EMG (electromyography) signal is a complex nonlinear signal with low signal to noise ratio (SNR). This paper is aimed at identifying different patterns of surface EMG signals according to fractal dimension. Two patterns of surface EMG signals are respectively acquired from the right forearm flexor of 30 healthy volunteers during right forearm(More)
We have developed an effective technique for extracting and classifying motor unit action potentials (MUAPs) for electromyography (EMG) signal decomposition. This technique is based on single-channel and short perioda9s real recordings from normal subjects and artificially generated recordings. This EMG signal decomposition technique has several distinctive(More)
Features can be classified into interferential features and discriminable features according to their contribution to pattern recognition. In this paper, a novel and simple method based on wavelet packet transform is proposed to extract the features from surface EMG signal. In this method, the features are relative wavelet packet energy (RWPE), which is(More)
An improved approximate entropy (ApEn) is presented and applied to characterize surface electromyography (sEMG) signals. In most previous experiments using nonlinear dynamic analysis, this certain processing was often confronted with the problem of insufficient data points and noisy circumstances, which led to unsatisfactory results. Compared with fractal(More)
BACKGROUND Arterial media calcification (AMC) is highly prevalent and is a major cause of morbidity, mortality, stroke and amputation in patients with diabetes mellitus (DM). Previous research suggests that advanced glycation end products (AGEs) are responsible for vascular calcification in diabetic patients. The potential link between oxidative stress and(More)
We have studied methods for noise reduction of myoelectric signals and for extraction of motor unit action potentials from these signals. Effective MUAP peak detection is the first important step in EMG decomposition. We first combined independent component analysis and wavelet filtering to remove power line interference, and then applied a wavelet(More)
This paper proposes the use of independent component analysis (ICA) and thresholding estimation calculated in wavelet transform for noise reduction in electromyographic (EMG) signals. In contrast to existing amplitude threshold detection scheme which either need to be participated by the operator or is time consuming, this method is more fast and completely(More)
BACKGROUND Thioglycolic acid (TGA) is widely used in the hairdressing industry, which mostly caters to women. Recently, TGA has been reported to impair several organs, especially reproductive ones such as testes and ovaries. The reproductive toxicity of TGA on females has become an issue that cannot be neglected. METHODOLOGY/PRINCIPAL FINDINGS In the(More)