Abstract
A new algorithm for denoising electrocardiogram (ECG) signals contaminated by additive white Gaussian noise is proposed in this study. In the proposed algorithm, a clean ECG signal is modelled as a combination of a smooth signal representing the P-wave and the T-wave, and a group-sparse (GS) signal representing the QRS-complex, where a GS signal is a sparse signal in which its non-zero entries tend to concentrate in groups. Accordingly, the proposed approach consists of two stages. In the first stage, an algorithm previously developed by the author is adapted to extract the GS signal representing the QRS-complex, while in the second stage a new algorithm is developed to smooth the remaining signal. Each one of these two algorithms depends on a regularisation parameter, which is selected automatically in the proposed algorithms. Simulation results on real and simulated ECG data show that the proposed algorithm can be successfully utilised to denoise ECG data. In addition, the proposed algorithm is also shown to produce significantly improved results compared to existing techniques used for performing similar tasks.