Empirical Mode Decomposition, Viterbi and Wavelets Applied to Electrocardiogram Noise Removal

The electrocardiogram (ECG) signal is generally used as a cardiovascular disease diagnostic tool. The accuracy of the diagnosis is directly related to the quality of the ECG signal, which can be corrupted by several sources of noises such as, for example, baseline wanders and power line interference. This paper proposes a new ECG denoising methodology based on wavelets, empirical mode decomposition (EMD), and Viterbi algorithm. The EMD decomposes the signal in intrinsic mode functions (IMFs), then each one of these IMFs is processed by the discrete wavelet transform through a decision process based on the Viterbi algorithm. We apply the proposed method to a synthetic ECG signal and three real ECG signals. The simulations results show that this novel methodology outperforms denoising schemes based on wavelets, empirical mode decomposition, and total variation.