ECG noise reduction based on stationary wavelet transform and zero-crossings interval thresholding

In this paper, a novel method for removing white gaussian noise from raw ECG signals based on Stationary Wavelet Transform and Interval Thresholding is proposed. Unlike the classical existing denoising methods in the wavelet domains, that apply a simple thresholding to details coefficients, we considers the extremums of each zero-crossings interval in the wavelet domain a whole to perform a thresholding function, which can highlight preserve the most clinical information about the region of the QRS complex in the reconstructed ECG signal. The performance of the proposed SWT-IT (Stationary wavelet transform-Interval Thresholding) method is evaluated in terms of Signal-to-Noise Ratio (SNR), Root Mean Square Error (RMSE), and Percent Root Mean Square Difference (PRD) using real ECG signals acquired from the MIT-BIH Arrhythmia Database. The simulations results show that the proposed method demonstrate superior performance compared with conventional ECG denoising approaches based on the wavelet filtering.