中文摘要：

为了提高模糊熵在癫痫EEG信号分析中的抗噪能力,提出一种排列模糊熵新算法,即运用排列符号化时间序列的思想增强模糊熵的抗噪能力。通过在公共癫痫EEG数据上的抗噪实验和分类检测实验,分析了排列模糊熵的抗噪能力和癫痫检测性能。实验结果表明,排列模糊熵具有较好抗噪能力和较高的癫痫检测性能,比模糊熵更适用于癫痫信号的分析。

英文摘要：

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. Experimental 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.