An Iterative Filtering Based ECG Denoising Using Lifting Wavelet Transform Technique

This research article explores a hybrid strategy that combines an adaptive iterative filtering (IF) method and the fast discrete lifting-based wavelet transform (LWT) to eliminate power-line noise (PLI) and baseline wander from an electrocardiogram (ECG) signal. Due to its correct mathematical basis and its guaranteed a priori convergence, the iterative filtering approach was preferred over empirical mode decomposition (EMD). The noisy modes generated from the IF are fed to an LWT system so as to be disintegrated into the detail and the approximation coefficients. These coefficients are then scaled using a threshold method to generate a noise-free signal. The proposed strategy improves the quality and allows us to precisely preserve the vital components of the signal. The method's potency has been established empirically by calculating the improvement in signal-to-noise ratio, cross-correlation coefficient and percent root-mean-square difference for different recordings available on the MIT-BIH arrhythmia database and then compared to numerous existing methods.

Automated summary

This research article presents a hybrid strategy that combines an adaptive iterative filtering method and a fast discrete lifting-based wavelet transform to remove power-line noise and baseline wander from an ECG signal. The proposed strategy improves the signal quality and preserves the vital components accurately. Its efficacy is established through empirical calculations and comparisons with existing methods using the MIT-BIH arrhythmia database.