Background:　Entropy　is　a　nonlinear　index　that　can　reflect　the　degree　of　chaos　within　a　system.　It　is　often　used　to　analyze　epileptic　electroencephalograms　(EEG)　to　detect　whether　there　is　an　epileptic　attack.　Much　research　into　the　state　inspection　of　epileptic　seizures　has　been　conducted　based　on　sample　entropy　(SampEn).　However,　the　study　of　epileptic　seizures　based　on　fuzzy　entropy　(FuzzyEn)　has　lagged　behind.　New　methods:　We　propose　a　method　of　state　inspection　of　epileptic　seizures　based　on　FuzzyEn.　The　method　first　calculates　the　FuzzyEn　of　EEG　signals　from　different　epileptic　states,　and　then　feature　selection　is　conducted　to　obtain　classification　features.　Finally,　we　use　the　acquired　classification　features　and　a　grid　optimization　method　to　train　support　vector　machines　(SVM).　Results:　The　results　of　two　open-EEG　datasets　in　epileptics　show　that　there　are　major　differences　between　seizure　attacks　and　non-seizure　attacks,　such　that　FuzzyEn　can　be　used　to　detect　epilepsy,　and　our　method　obtains　better　classification　performance　(accuracy,　sensitivity　and　specificity　of　classification　of　the　CHB-MIT　are　98.31%,　98.27%　and　9836%,　and　of　the　Bonn　are　100%,　100%,　100%,　respectively).　Comparisons　with　existing　method(s):　To　verify　the　performance　of　the　proposed　method,　a　comparison　of　the　classification　performance　for　epileptic　seizures　using　FuzzyEn　and　SampEn　is　conducted.　Our　method　obtains　better　classification　performance,　which　is　superior　to　the　SampEn-based　methods　currently　in　use.　Conclusions:　The　results　indicate　that　FuzzyEn　is　a　better　index　for　detecting　epileptic　seizures　effectively.　The　FuzzyEn-based　method　is　preferable,　exhibiting　potential　desirable　applications　for　medical　treatment.　(C)　2015　Elsevier　B.V.　All　rights　reserved.