Gaussian kernel approximate entropy algorithm for analyzing irregularity of time-series

Approximate entropy (ApEn) has been widely used to analyze the complexity of time series. However, the inconsistency that ApEn exhibits not only limits its applications but also raises questions about its validity. Addressing this issue, this paper presents a novel Gaussian kernel approximate entropy (GApEn) algorithm. The experimental results demonstrate that GApEn performs better than ApEn in terms of relative consistency, stability and statistical accuracy.