ECG De-Noising using improved thresholding based on Wavelet transforms

The electrocardiogram (ECG) is widely used for diagnosis of heart diseases. Good quality of ECG is utilized by physicians for interpretation and identification of physiological and pathological phenomena. However, in real situations, ECG recordings are often corrupted by artifacts. Noise severely limits the utility of the recorded ECG and thus need to be removed, for better clinical evaluation. Donoho and Johnstone [4, 5, 10] proposed wavelet thresholding de-noising method based on discrete wavelet transform (DWT) with universal threshold is suitable for non-stationary signals such as ECG signal. In the present paper a new thresholding technique is proposed for denoising of ECG signal. This new de-noising method is called as improved thresholding de-noising method could be regarded as a compromising between hardand soft-thresholding de-noising methods. The proposed method selects the best suitable wavelet function based on DWT at the decomposition level of 5, using mean square error (MSE) and output SNR. The advantage of the improved thresholding de-noising method is that it retains both the geometrical characteristics of the original ECG signal and variations in the amplitudes of various ECG waveforms effectively. The experimental results indicate that the proposed method is better than traditional wavelet thresholding de-noising methods in the aspects of remaining geometrical characteristics of ECG signal and in improvement of signal-to-noise ratio (SNR).