Manis George
University of Ioannina, Department of Computer Science, Ioannina 45110, Greece.
Comput Methods Programs Biomed. 2008 Jul;91(1):48-54. doi: 10.1016/j.cmpb.2008.02.008.
The approximate entropy (ApEn) is a measure of systems complexity. The implementation of the method is computationally expensive and requires execution time analogous to the square of the size of the input signal. We propose here a fast algorithm which speeds up the computation of approximate entropy by detecting early some vectors that are not similar and by excluding them from the similarity test. Experimental analysis with various biomedical signals revealed a significant improvement in execution times.
近似熵（ApEn）是一种衡量系统复杂性的指标。该方法的实现计算成本高昂，并且所需的执行时间与输入信号大小的平方成正比。我们在此提出一种快速算法，通过早期检测一些不相似的向量并将它们排除在相似性测试之外，从而加速近似熵的计算。对各种生物医学信号的实验分析表明，执行时间有显著改善。
