Abstract
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.
Highlights
The electrocardiogram (ECG) signal represents the electrical activity of the heart
This selection is based on the output SNR and mean square error (MSE)
For this purpose we compute output SNR corresponding to different values of input SNR for different types of wavelet function (Haar, Daubechie 6, Symlet 8, BiorSpline 3.5, Coiflet 4).The Fig 8 and Fig 9 show the comparison of output SNR for different wavelet
Summary
The electrocardiogram (ECG) signal represents the electrical activity of the heart. This signal is useful for the diagnosis and discovery of cardiac diseases. This structure is devided into a preprocessing stage including filtering process and a decision stage including features detection such as R peak, QRS complex. Features of ECG signal Feature Description Duration RR interval. Normal resting heart rate is between 60 to 100 bpm
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