Abstract :
The electrocardiogram (ECG) is widely used for diagnosis of heart diseases. Good quality ECG are utilized by physicians for interpretation and identification of physiological and pathological phenomena. However, in real situations, ECG recordings are often corrupted by artifacts. Two dominant artifacts present in ECG recordings are: (1) high-frequency noise caused by electromyogram induced noise, power line interferences, or mechanical forces acting on the electrodes; (2) baseline wander (BW) that may be due to respiration or the motion of the patients or the instruments. These artifacts severely limit the utility of recorded ECGs and thus need to be removed for better clinical evaluation. Several methods have been developed for ECG enhancement. In this paper, we propose a new ECG enhancement method based on the recently
developed empirical mode decomposition (EMD). The proposed EMD-based method is able to remove both high-frequency noise and BW with minimum signal distortion. The method is validated through experiments on the MIT–BIH databases. Both quantitative and qualitative
results are given. The simulations show that the proposed EMD-based method provides very good results for denoising and BW removal
ECG signal is one of the biomedical signals, which are widely studies and applied in clinic. A normal ECG waveform is usually composed of P wave, QRS complexes, and T wave, and the accurate detection of them is important to analyze ECG signal. However, because ECG signal is very faint, it is extremely easy to interfere by the different noises while gathering and recording. How to suppress noises effectively is always an important problem in the detection of ECG signal. Recently, wavelet transform has been widely used in signal and image processing due to the time-frequency localization characteristics. There are mainly two kinds of wavelet denoising methods used in denoising of ECG signal: one is wavelet transform modulus maxima method. This method can eliminate noises and remain the information of the original signal in maximum at the same time, but the amount of calculation is great, and the process of calculation may be unstable. The other is wavelet thresholding denoising method. Wavelet thresholding denoising method deals with wavelet coefficients using a suitable threshold chosen in advance. The wavelet coefficients at different scales could be obtained by taking discrete wavelet transform (DWT) of the noisy signal. Normally, those wavelet coefficients with smaller magnitudes than the preset threshold are caused by the noise and replaced by zero, and the others with larger magnitudes than the preset threshold are caused by original signal mainly and kept (hard-thresholding case) or shrunk (the soft-thresholding case). Then the denoised signal could be reconstructed from the resulting wavelet coefficients. This method is simple and easy to be used in denoising of ECG signal. But hard-thresholding denoising method may lead to the
oscillation of the reconstructed ECG signal. The soft-thresholding denoising method may reduce the amplitudes of ECG waveforms, and especially reduce the amplitudes of the R waves. To overcome these disadvantages mentioned above, an improved thresholding denoising method is proposed firstly. It is a compromising method between the hard- and soft-thresholding. Second