Effect of noises on moving cut data-approximate entropy
噪声对滑动移除近似熵的影响

Affected by some factors such as external forcing and the measurement errors of instrument itself, observational data often contain noises, disturbances and some other false information. To solve this problem, the effects of different noises on moving cut data-approximate entropy (MC-ApEn) are investigated in this paper. The results indicate that MC-ApEn is little affected by random spikes and Gaussian white noise, which means that the MC-ApEn method has strong anti-noise ability. The results provide an essential experimental basis for the wide applications of the present method to observational data.