Abstract

Info

Title:: ECG signal denoising using improved variable step size least mean square algorithm

Author(s):: ZHANG Wei; GU Xuan; LIANG Fue; L?Shanshan; LIU Donghua; College of Information Engineering, Gansu University of Chinese Medicine, Lanzhou 730100, China

Keywords:: Keywords: electrocardiogram signal noise least mean square algorithm convergence speed steady-state error

Abstract:: An improved variable step size least mean square (LMS) algorithm based on fractional function is proposed to solve the problem of the poor denoising performances of the fixed step size and the existing variable step size LMS adaptive filtering algorithms on electrocardiogram (ECG) signals. The fractional function is used to construct the step function of the improved variable step size LMS algorithm, and the optimal parameter values of the algorithm are obtained through theoretical and simulation analyses. The performance comparison with the fixed step size and other variable step size LMS algorithms under the same conditions verifies that the proposed algorithm has faster convergence speed, lower steady-state error and less computational complexity. Moreover, the proposed algorithm is compared with the fixed step size and other variable step size LMS algorithms on ECG signals containing multiple real noises under the same conditions. The experimental results show that compared with the other algorithms, the proposed algorithm can better remove the noise in ECG signals, and that the denoised ECG signals have the largest signal-to-noise ratio and the minimum mean square error, with a Pearson correlation coefficient closest to 1.