Key result
Sparse optimization improves ECG denoising and baseline estimation over TV, NLM, and CEEMDAN.
Why the study?
Baseline wander and noise reduction play an important role in ECG data analysis and disease diagnosis.
A novel sparse optimization method for ECG denoising and baseline estimation outperforms existing methods like NLM and CEEMDAN in reducing error and improving signal-to-noise ratio.
Baseline wander (BW) and electrocardiogram (ECG) noise reduction play an important role in ECG data analysis and disease diagnosis. This article introduces a sparse optimization method, which takes into account the group sparse characteristics of the signal, and combines low-pass filter to denoise the ECG signal and estimate the baseline. Derived from the classic total variation (TV) denoising method, a denoising method considering the structural characteristics of ECG signals is proposed. This method uses a band matrix to represent the sparse optimization problem, and adopts majorization-minimization (MM) algorithm to optimize the solution of the convergence problem. Through data comparison and detailed analysis, we first compares the method with two TV denoising methods. Then, the proposed method is validated in the MIT-BIH arrhythmia database of ECG signals, and compared with nonlocal means (NLM) and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) methods. The simulation experiment results show that the proposed algorithm has lower root mean square error (RMSE) and higher signal-to-noise ratio improvement (SNR_imp).
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Shi et al. (2021) studied ECG signal processing. Sparse optimization method with group sparse regularization and low-pass filter vs. Total variation (TV) denoising, nonlocal means (NLM), and CEEMDAN methods was evaluated on Root mean square error (RMSE) and signal-to-noise ratio improvement (SNR_imp). A proposed sparse optimization method for ECG denoising and baseline estimation demonstrated lower root mean square error and higher signal-to-noise ratio improvement compared to TV, NLM, and CEEMDAN.
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