Key result
Discrete Wavelet Transform using Symlet 8 or Coiflet 4 wavelet functions with rigorous SURE thresholding at decomposition levels greater than 4 provided optimal denoising performance for ECG signals.
Specific wavelet functions and thresholding methods optimize the denoising of ECG signals depending on the type of artifact (EMG, baseline wander, or power line interference).
Optimal DWT parameter selection may enhance ECG denoising metrics; leaves open standardized validation in clinical datasets.
The denoising of electrocardiogram (ECG) represents the entry point for the processing of this signal. The widely algorithms for ECG denoising are based on discrete wavelet transform (DWT). In the other side the performances of denoising process considerably influence the operations that follow. These performances are quantified by some ratios such as the output signal on noise (SNR) and the mean square error (MSE) ratio. This is why the optimal selection of denoising parameters is strongly recommended. The aim of this work is to define the optimal wavelet function to use in DWT decomposition for a specific case of ECG denoising. The choice of the appropriate threshold method giving the best performances is also presented in this work. Finally the criterion of selection of levels in which the DWT decomposition must be performed is carried on this paper. This study is applied on the electromyography (EMG), baseline drift and power line interference (PLI) noises.
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Aqil et al. (2017) studied ECG signal noise (EMG, baseline drift, PLI). Discrete Wavelet Transform (DWT) denoising vs. Alternative wavelet functions and thresholding methods was evaluated on Output Signal to Noise Ratio (SNR) and Mean Square Error (MSE). Discrete Wavelet Transform using Symlet 8 or Coiflet 4 wavelet functions with rigorous SURE thresholding at decomposition levels greater than 4 provided optimal denoising performance for ECG signals.
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