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Electrocardiogram (ECG) signal gets corrupted with artifacts, such as 50/60 Hz power line interference (PLI), electromyogram (EMG) and baseline wander, making it difficult to diagnose the cardiac abnormalities. This paper presents autoregressive (AR) modelling of cumulants for enhancement of ECG signals. Higher order spectral (HOS) cumulants possess many properties that make it an effective tool for...
In biomedical signal processing, it is often the case that many sources are mixed into the measured signal. The goal is usually to analyze one or several of them separately. In the case of multichannel measurements, several blind source separation techniques are available for decomposing the signal into its components [e.g., independent component analysis (ICA)]. However, only a few techniques have...
In this paper, an artificial intelligence technique called Back Propagation Network (BPN) is proposed to cancel the electrocardiogram (ECG) interference in electromyogram (EMG) signal. Conventional filtering techniques are not suitable due to an overlap in spectral content of the EMG and the ECG. The performance evaluation of the proposed technique is done in terms of signal to noise ratio, mean square...
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...
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