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In this study, improved normalized LMS adaptive filters are proposed to reduce the electromyogram (EMG) noise from ECG signals. The proposed technique mainly uses simple addition and shift operations and achieves considerable speed over other methods based on the LMS method. Simulation result gives by the improved versions of adaptive filter (NLMS, PNLMS, IPNLMS and MPNLS) show superior performance...
Surface electromyograms (EMGs) are valuable in the pathophysiological study and clinical treatment. These recordings are critically often contaminated by cardiac artifact. The purpose of this article was to evaluate the performance of an adaptive filter and artificial neural network (ANN) in removing electrocardiogram (ECG) contamination from surface EMGs recorded from the pectoralismajor muscles...
In this paper a new method for removing of Power Line Interference (PLI) and ECG Signal from EMG signal is proposed. This method is designed based on filtering of EMG signal corrupted with interference of power line and ECG (EMG+PLI+ECG), by using Matching Pursuit (MP) that is a time-frequency transform. For this reason, according to the cosine nature of PLI and alternative mode of ECG signal, Cosine...
Electromyogram (EMG) is used in various circumstances such as diagnostic and prosthesis control. This paper deals with the diaphragmatic electromyogram (EMGdi) as a controller of mechanical ventilation. However, recorded EMGdi signals are always contaminated by electrocardiogram (ECG). Cancellation of the ECG contamination, especially in real-time, is not a simple operation because the spectra of...
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...
Extracting the diaphragmatic electromyogram (EMG) signal is the key to manufacture the breathing machine in synchrony with patient. However, the diaphragmatic EMG signal is immerged in the electrocardiogram (ECG) signal. The mixed signals composed of EMG signal and ECG signal are obtained from the platform of EMG signal acquisition. Hence, extracting the diaphragmatic EMG signal perfectly is a difficult...
In this paper we show how independent component analysis (ICA) algorithms can be used to perform spatio-temporal filtration of electromyographic (EMG) and electrocardiographic (ECG) signals. The technique was used to decompose the EMG signals into motor unit action potential (MUAP) trains. From the 88 outputs of the adaptive spatio-temporal filtration, three groups of different MUAP train patterns...
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