Aiming at the problem of pattern confusion in traditional empirical mode decomposition (EMD) method and the fact that the overall mean empirical mode decomposition (EEMD) does not have completeness and computational complexity, an improved adaptive complementary set empirical mode decomposition - (CEEMD) method is proposed. Based on the analysis of the noise adding criterion, this method introduces peak error (PE) as the noise adding evaluation index to adaptively determine the optimal noise adding amplitude. Then, the original signal amplitude standard deviation and the noise added amplitude standard deviation are used. The ratio coefficient is used to adaptively obtain the overall average number of times for different signals. Finally, the method is applied to the MIT-BIH ECG database established by the Massachusetts Institute of Technology, and the denoising of the target signal is well completed. Experiments show that the average SNR of the proposed method reaches 19. 249 7, the RMSE is only 0. 047 3, and the average smoothness index R is only 0. 030 5. The algorithm effectively removes the original ECG signal noise, improves the signal smoothness and improves the calculation efficiency.