Fractional fuzzy entropy algorithm and the complexity analysis for nonlinear time series

被引：28
|
作者：
He, Shaobo [1 ,2 ]
Sun, Kehui [2 ]
Wang, Rixing [3 ]
机构：
[1] Hunan Univ Arts & Sci, Sch Comp Sci & Technol, Changde 415000, Peoples R China
[2] Cent S Univ, Sch Phys & Elect, Changsha 410083, Hunan, Peoples R China
[3] Hunan Univ Arts & Sci, Normal Coll, Changde 415000, Peoples R China
来源：
EUROPEAN PHYSICAL JOURNAL-SPECIAL TOPICS | 2018年 / 227卷 / 7-9期
基金：
中国国家自然科学基金;
关键词：
MULTISCALE PERMUTATION ENTROPY; APPROXIMATE ENTROPY; SAMPLE ENTROPY; EEG; INFORMATION; DYNAMICS; DECREASE; SIGNALS; SYSTEM;
D O I：
10.1140/epjst/e2018-700098-x
中图分类号：
O4 [物理学];
学科分类号：
0702 ;
摘要：
In this paper, fractional fuzzy entropy (FFuzzyEn) algorithm is designed by combing the concept of fractional information and fuzzy entropy (FuzzyEn) algorithm. Complexity of chaotic systems is analyzed and parameter choice of FFuzzyEn is investigated. It also shows that FFuzzyEn is effective for measuring dynamics of nonlinear time series and has better comparing results for different time series. Moreover, changes in the complexity of EEG signals from normal health persons and epileptic patients are observed. The results show that, compared with normal health persons, epileptic patients have the lowest complexity during seizure activity and relative lower complexity during seizure free intervals. The proposed method may be useful for EEG signal based physiological and biomedical analysis.
引用
收藏
页码：943 / 957
页数：15
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