Abstract
We consider the problem of electrocardiogram (ECG) denoising using source separation. In this study, a hybrid technique using empirical mode decomposition (EMD) and source separation, is proposed. This technique consists of two steps, the first step consists in applying the EMD to two different mixtures. These mixtures are obtained by corrupting in additive manner, the same ECG signal by a white Gaussian noise with two different values of the signal-to-noise ratio (SNR). The second step consists in computing the entropy of each obtained intrinsic mode function (IMF) and finds the two IMFs having the minimal entropy. These two IMFs are used to estimate the separation matrix of the ECG signal from noise by using the source separation. The proposed technique is evaluated by comparing it to the denoising technique based on source separation in time domain using runica and the technique based on Bionic wavelet transform (BWT) and also using source separation. The obtained results from SNR and the mean square error (MSE) computations, show that the proposed technique outperforms the other two techniques used in this evaluation.
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