Introduction:　In　the　clinical　setting,　it　becomes　increasingly　important　to　detect　epileptic　seizures　automatically　since　it　could　significantly　reduce　the　burden　for　the　care　of　patients　suffering　from　intractable　epilepsy.　Electroencephalography　(EEG)　signals　record　the　brain＇s　electrical　activity　and　contain　rich　information　about　brain　dysfunction.　As　a　non-invasive　and　inexpensive　tool　for　detecting　epileptic　seizures,　visual　evaluation　of　EEG　recordings　is　labor-intensive　and　subjective　and　requires　significant　improvement.　Methods:　This　study　aims　to　develop　a　new　approach　to　recognize　seizures　automatically　using　EEG　recordings.　During　feature　extraction　of　EEG　input　from　raw　data,　we　construct　a　new　deep　neural　network　(DNN)　model.　Deep　feature　maps　derived　from　layers　placed　hierarchically　in　a　convolution　neural　network　are　put　into　different　kinds　of　shallow　classifiers　to　detect　the　anomaly.　Feature　maps　are　reduced　in　dimensionality　using　Principal　Component　Analysis　(PCA).　Results:　By　analyzing　the　EEG　Epilepsy　dataset　and　the　Bonn　dataset　for　epilepsy,　we　conclude　that　our　proposed　method　is　both　effective　and　robust.　These　datasets　vary　significantly　in　the　acquisition　of　data,　the　formulation　of　clinical　protocols,　and　the　storage　of　digital　information,　making　processing　and　analysis　challenging.　On　both　datasets,　extensive　experiments　are　performed　using　a　cross-validation　by　10　folds　strategy　to　demonstrate　approximately　100%　accuracy　for　binary　and　multi-category　classification.　Discussion:　In　addition　to　demonstrating　that　our　methodology　outperforms　other　up-to-date　approaches,　the　results　of　this　study　also　suggest　that　it　can　be　applied　in　clinical　practice　as　well.　Copyright　©　2023　Zeng,　Shan,　Su　and　Du.