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To effectively suppress white noise and preserve more useful components of electrocardiogram (ECG) signal, a novel de-noising method based on morphological component analysis (MCA) is proposed. MCA is a method which allows us to separate features contained in an original signal when these features present different morphological aspects. According to the features of ECG, we used the UWT dictionary to sparsely represent mutated component, and used the DCT dictionary to sparsely represent smooth component. The experimental results of the samples choosing from MIT-BIH databases show that the MCA-based method is effective for white noise removal.
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It is inevitable that noises will be introduced during the acquisition of pulse wave signal, which can result in morphology changes of the original pulse wave, and affect the hemodynamic analysis and diagnosis based on pulse wave signalsIn order to remove these noises, an adaptive de-noising method based on empirical mode decomposition (EMD) and wavelet threshold is proposed in this paperCompared with the wavelet threshold method for denoising pulse wave, the proposed approach is more effective, especially at low signal-to-noise ratio.