Measuring System Regularity Using Fuzzy Similarity-based Approximate Entropy

作     者：陈伟婷 王志中 王刚 CHEN Wei-ting, WANG Zhi-zhong, WANG Gang(Dept. of Biomedical Eng., Shanghai Jiaotong Univ., Shanghai 200240, China)

作者机构：Dept.of Biomedical Eng.Shanghai Jiaotong Univ. 

出 版 物：《Journal of Shanghai Jiaotong university(Science)》 (上海交通大学学报（英文版）)

年 卷 期：2007年第12卷第5期

页      面：623-627页

核心收录：

学科分类：07[理学] 081203[工学-计算机应用技术] 08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：The National Basic Research Program (973)of China (No 2005CB724303) 

主　　题：regularity approximate entropy (ApEn) fuzzy similarity physiological signal 

摘      要：Approximate entropy (ApEn), a measure quantifying regularity and complexity, is believed to be an effective analyzing method of diverse settings that include both deterministic chaotic and stochastic processes, particularly operative in the analysis of physiological signals that involve relatively small amount of data. However, the similarity definition of vectors based on Heaviside function, of which the boundary is discontinuous and hard, may cause some problems in the validity and accuracy of ApEn. To overcome these problems, a modified ApEn based on fuzzy similarity (mApEn) was proposed. The performance on the MIX stochastic model, as well as those on the Logistic map and the Hennon map with noise, shows that the fuzzy similarity-based ApEn gets more satisfying results than the standard ApEn when characterizing systems with different regularities.