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- W2892825009 abstract "During the acquisition process Electrocardiogram (ECG) signal gets corrupted by various noises. Denoising of ECG signal is the key requirement for arrhythmia classification, cardiac diagnosis, etc. Discrete wavelet transform (DWT) has been widely used for the denoising of Electrocardiogram (ECG) signal. In the traditional DWT based denoising approaches, the ECG signal is decomposed into detail and approximation coefficients up to a certain level. Then the detail coefficients at each level is thresholded and the final reconstruction is performed. On contrary, the approximation coefficient has not been properly denoised and it carries significant amount of noise components. In this paper, we present the significance of non-local means estimation for denoising the approximation coefficients of DWT. In this work, only a two level decomposition is performed using DWT. Apart from the detail coefficient thresholding, the approximation coefficient at level-2 has also been denoised using the non-local means (NLM) estimation. The resultant denoising performance is found to be enhanced compared to the existing DWT based denoising." @default.
- W2892825009 created "2018-10-05" @default.
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- W2892825009 date "2018-02-01" @default.
- W2892825009 modified "2023-10-04" @default.
- W2892825009 title "Significance of non-local means estimation in DWT based ECG signal denoising" @default.
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- W2892825009 doi "https://doi.org/10.1109/spin.2018.8474133" @default.
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