New algorithm based on group sparsity for correcting wideband noise in ECG recordings

Abstract

A new algorithm for denoising ECG data contaminated by wideband noise signals (e.g., muscle artifacts and instrumentation noise) is proposed in this paper. In contrast to the wideband noise, a clean ECG signal s(t) has the property that its pth order difference sp(t) (with p≥3) and the nonlinear energy operator h(t) associated with it are both group-sparse signals that have common support, which coincides with that of the QRS-complex. Accordingly, the main idea of the proposed algorithm is to estimate the clean ECG signal s(t) by encouraging the group sparsity of both sp(t) and h(t), and also encouraging their supports to coincide with that of the QRS-complex. A modified partial whitening technique is also proposed in this paper to handle the practical situations of having colored noise. The proposed algorithm depends on two regularization parameters, and their values are selected automatically in the proposed algorithm. Simulation results on real and simulated ECG data show that the proposed algorithm can be successfully utilized to denoise ECG data contaminated by wideband noise signals. In addition, the proposed algorithm is also shown to produce significantly improved results compared to existing techniques used for performing similar tasks.