A Novel Approach for Denoising ECG Signals Corrupted with White Gaussian Noise Using Wavelet Packet Transform and Soft-Thresholding

Abstract

The electrocardiogram (ECG) is a vital tool for detecting heart abnormalities, However, noise frequently disrupts the signals during recording, reducing diagnostic precision. During wireless recording and portable heart monitoring, one major source of noise is called additive white Gaussian noise (AWGN). Therefore, clean ECG signals are really important to diagnose cardic disorders. To address this concern , a novel approach is introduced that employs the Wavelet Packet Transform (WPT) for effective ECG signal denoising. WPT provides a comprehensive signal analysis, using the Symlets 8 mother wavelet function, decomposing ECG data into high and low frequency components over two levels. Subsequent to this, a soft thresholding (ST) technique is implemented to attenuate noise. Moreover, the universal threshold technique is incorporated, dynamically determining threshold values. Proposed method efficiently reduces noise through thresholding, addressing both low and high frequency noise components at each level. The retained coefficients are then utilized in the inverse WPT to reconstruct the denoised ECG signal. Comprehensive analysis highlights the robustness of our approach, demonstrating better performance compared to established denoising techniques on the MIT-BIH database. Performance metrics including Signal-to-Noise Ratio (SNR), SNR Improvement (SNRimp), correlation coefficient (CC) , Percentage Root Mean Square Difference (PRD) and Mean Squared Error (MSE) are employed. Proposed WPT approach, tailored through suitable decomposition levels and mother wavelet selection, represents a substantial improvement in ECG signal denoising beyond conventional techniques. The proposed method showcases substantial improvements over EMD-DWT, with 28.32% lower RMSE, 34.99% higher SNR, and 0.25% enhanced CC.