An Improved ECG Denoising Algorithm Based on Wavelet-scale Correlation Coefficients

Abstract

The ECG signal is a weak low-frequency signal from the human body. It is highly susceptible to noise interference from inside and outside the body during acquisition, affecting the clinician's diagnosis of heart disease. The ECG signal in an ideal state was first used as the raw data. By adding Gaussian white noise as the noise during routine ECG acquisition, each scale's estimated noise standard deviation was used as a natural condition to determine whether it was noisy or not. Experiments were conducted on ECG signals from the MIT-BIH database, and the results showed that the improved denoising algorithm method resulted in a 6.67% increase in the mean signal to noise ratio (SNR), a 0.01% reduction in the mean root mean square (RMS) error and a smooth ECG image signal. Compared with the traditional wavelet coefficient correlation denoising method, the improved wavelet coefficient correlation denoising method proposed in this paper has a better denoising effect.