Denoising of ECG and Estimation of the Baseline Using Group Sparse Regularization and Some Advanced Filtering Techniques

Abstract

Abstract: In this context, the sparse features of the particular signal are taken into consideration to exhibit the technique of sparse optimization. This type of approach is derived from the traditional approach known as total variation. This traditional approach is the old one used to reduce the noise in a particular signal in order to rescue the sharp edges of the signal. In this context, majorization and minimization are the major keys to getting the desired noise-free signal. Along with that, most advanced filters like median filters and moving average filters are considered in order to gain a noise-free signal, and the signal will be more vulnerable to identification. Using the filters discussed above, the signal can be determined in the high SNR ranges as well.