Research on ECG signal denoising method based on sparse component analysis

Abstract

ECG signal is used as a primary diagnostic tool for heart disease. A purity ECG signal can provide necessary information for diagnosis, but in most time, noises are often mixed with ECG signal. Therefore, this paper proposed a denoising method for EEG signal based on the sparse representation component analysis. It can help doctor to improve the accuracy of diagnosis. In this paper, the base pursuit algorithm is used L0-norm regularization to transform into the L1-norm regularization. The residual is taken as the noise, and the product of the dictionary and the sparse coefficient is taken as the denoising signal. The experiment results can verify the effectiveness of the algorithm and extract information from noisy ECG signals. This ECG signal denoising method can be used for separation and recognition of ECG signals, which is very important for clinical research and pathological diagnosis.