太原理工大学学报2017，Vol.48Issue(1)：91-96,6.DOI:10.16355/j.cnki.issn1007-9432tyut.2017.01.015
用于癫痫EEG分析的排列模糊熵新算法
A Permutation Fuzzy Entropy for Epilepsy EEG Analysis
摘要
Abstract
This paper put forward a permutation fuzzy entropy algorithm (PFEN ) ,which used the idea of the permutational symbolization in time series to enhance the antinoise ability of FuzzyEn .Through the antinoise experiment and epilepsy detection experiment on public epileptic EEG data ,we analysed the antinoise ability and epilepsy detection performance of PFEN .Experi‐mental results show that the PFEN has better ability to resist noise and better epilepsy detection performance .It’s more suitable for Epilepsy EEG signal analysis than FuzzyEn .关键词
符号化/时间序列/排列模糊熵/癫痫Key words
symbolic/time series/permutation fuzzy entropy/epilepsy分类
信息技术与安全科学引用本文复制引用
王慧云,窦大庆,曹锐,王彬,刘桂青,相洁..用于癫痫EEG分析的排列模糊熵新算法[J].太原理工大学学报,2017,48(1):91-96,6.基金项目
国家自然科学基金资助项目抑郁症 f M R 数据分析方法及辅助诊断治疗模型研究（61373101）；山西省工业攻关项目（20140321002-01）；虚拟现实技术与系统国家重点实验室开放课题 （）
