Epilepsy　is　a　neurological　disorder　characterized　by　recurrent　abnormal　neuronal　discharges.　Electroencephalogram　is　often　used　clinically　to　assist　in　the　diagnosis　and　treatment　of　epilepsy.　The　spikes　and　sharps　contain　a　large　amount　of　epilepsy-related　pathological　information,　so　the　detection　of　spikes　and　sharps　among　the　abnormal　epileptic　waveforms　has　more　clinical　diagnostic　value.　There　are　two　problems　in　using　machine　learning　to　achieve　automatic　recognition　of　spike　and　sharp　waves.　One　is　that　most　of　the　EEG　data　are　unlabeled　data,　and　it　is　difficult　to　obtain　a　large　number　of　labeled　training　sets;　the　other　is　that　spikes　and　sharps　are　mixed　with　plenty　of　background　waves,　which　lead　to　a　data　imbalance　trouble.　Based　on　the　above　backdrop,　this　paper　implements　a　detection　framework　of　epileptiform　spikes　using　active　learning　in　order　to　achieve　better　recognition　results　with　as　little　cost　as　possible,　and　its　major　contributions　are　as　follows:　(1)The　KNN　attention　layer　is　introduced　in　the　learning　engine　to　improve　the　generalization　ability　of　the　model　in　the　case　of　few　samples;　(2)In　terms　of　the　sampling　engine,　MPGR　(Manifold　Preserving　Graph　Reduction)　pre-processing　is　first　performed　to　initially　reduce　the　imbalance　rate　of　the　data　and　remove　redundant　points,　then　density-weighted　uncertainty　based　on　GAN　is　used　to　accelerate　the　efficiency　of　active　learning,　and　finally　boundary　distance-based　clustering　sampling　is　used,　which　is　to　ensure　diversity　while　taking　balanced　samples　as　much　as　possible.　Results　of　experiments　conducted　on　a　hospital-supplied　dataset　show　that　the　proposed　framework　is　effective.　©　2021　IEEE.