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Electrocardiogram (ECG) is an essential method for the clinical diagnosis of cardiovascular diseases. The weak biomedical signal buried in all kinds of noise will result in the useful information lost or false information on the wavelet decomposition. According to the characteristics of ECG signal and wavelet transform, a discrete wavelet soft threshold denoise processing method is used to remove...
Non-invasive recordings of intestinal myoelectrical activity (Electroenterogram, EEnG) are affected by very-low-frequency (VLF) interferences, respiration, ECG and movement artifacts. In order to identify the intestinal pacemaker activity (slow wave), VLF interferences and respiration should be removed from abdominal surface recordings. In this paper a method based on empirical mode decomposition...
In this study, EMG noise interfering ECG signal is removed using discrete wavelet and packet wavelet transform. Methods are compared to each other and an optimum denoising ECG signal is obtained. Overall performance is asessed on the basis of the signal-to- noise ratio (SNR) and visually.
This study uses the signal averaging and filtering method for ECG signal de-noising and R-wave detection with moving minimum slot and maximum point selecting method. Signal averaging and filtering method reduces random noise (major component of EMG noise) in ECG signal and also gives the comparatively good result for baseline wander noise cancellation. Signal to noise ratio (SNR) improves in filtered...
The electrical activity recorded on the abdomen during pregnancy and labor (abdominal signals, ADS) contains vital information about the health state of both mother and fetus. The most important signal related with the health of fetus, extensively studied by now, is the fetal ECG (fECG) which allows physicians to examine the evolution of the fetus and to identify possible heart diseases of fetus....
In clinical experiments, the surface electromyogram (sEMG) recorded from low back muscle exhibit a strong presence of electrocardiogram (ECG). This study applied independent component analysis (ICA) to effectively suppress the interference of ECG in sEMG recorded from low back muscle. In contrast to existing ICA denoise techniques, which totally remove the artifactual ICA components, high-pass filter...
We analysed noise interference on the PQRST waveform of the electro- and magnetocardiogram as recovered by an average technique. Three signals (adult A, neonatal N, and fetal F) were corrupted with breathing, electromyographic and power-line noise at different levels of the QRS amplitude. Using the correlation coefficient r, averaged PQRST patterns from corrupted signals were compared with corresponding...
The aim of this study is suppression of parasite electromyographic (EMG) signals (myopotentials) included in ECG signals with use of the Wiener filtering in shift-invariant wavelet domain with pilot estimation of the signal. The wavelet filtering with hybrid thresholding was used for pilot estimation. The four-levels shift-invariant dyadic discrete-time wavelet transform decomposition was used for...
Surface electromyography (sEMG) recorded from the trunk area may reflect underlying muscular function, and is the current standard for in vivo functional examination. However, sEMG of this area, including the low back musculature, usually encounters substantial interference from strong cardiac signals. It is therefore imperative to remove electrocardiogram (ECG) interference from sEMG data. This paper...
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...
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