Electroencephalography　(EEG)　is　a　commonly　used　clinical　approach　for　the　diagnosis　of　epilepsy　which　is　a　life-threatening　neurological　disorder.　Many　algorithms　have　been　proposed　for　the　automatic　detection　of　epileptic　seizures　using　traditional　machine　learning　and　deep　learning.　Although　deep　learning　methods　have　achieved　great　success　in　many　fields,　their　performance　in　EEG　analysis　and　classification　is　still　limited　mainly　due　to　the　relatively　small　sizes　of　available　datasets.　In　this　paper,　we　propose　an　automatic　method　for　the　detection　of　epileptic　seizures　based　on　deep　metric　learning　which　is　a　novel　strategy　tackling　the　few-shot　problem　by　mitigating　the　demand　for　massive　data.　First,　two　one-dimensional　convolutional　embedding　modules　are　proposed　as　a　deep　feature　extractor,　for　single-channel　and　multichannel　EEG　signals　respectively.　Then,　a　deep　metric　learning　model　is　detailed　along　with　a　stage-wise　training　strategy.　Experiments　are　conducted　on　the　publicly-available　Bonn　University　dataset　which　is　a　benchmark　dataset,　and　the　CHB-MIT　dataset　which　is　larger　and　more　realistic.　Impressive　averaged　accuracy　of　98.60%　and　specificity　of　100%　are　achieved　on　the　most　difficult　classification　of　interictal　(subset　D)　vs　ictal　(subset　E)　of　the　Bonn　dataset.　On　the　CHB-MIT　dataset,　an　averaged　accuracy　of　86.68%　and　specificity　of　93.71%　are　reached.　With　the　proposed　method,　automatic　and　accurate　detection　of　seizures　can　be　performed　in　real　time,　and　the　heavy　burden　of　neurologists　can　be　effectively　reduced.