Background:　Epilepsy　is　a　neurological　disease　characterized　by　unprovoked　seizures　in　the　brain.　The　recent　advances　in　sensor　technologies　allow　researchers　to　analyze　the　collected　biological　records　to　improve　the　treatment　of　epilepsy.　Electroencephalogram　(EEG)　is　the　most　commonly　used　biological　measurement　to　effectively　capture　the　abnormalities　of　different　brain　areas　during　the　EEG　seizures.　To　avoid　manual　visual　inspection　from　long-term　EEG　readings,　automatic　epileptic　EEG　seizure　detection　has　become　an　important　research　issue　in　bioinformatics.　Results:　We　present　a　multi-context　learning　approach　to　automatically　detect　EEG　seizures　by　incorporating　a　feature　fusion　strategy.　We　generate　EEG　scalogram　sequences　from　the　EEG　records　by　utilizing　waveform　transform　to　describe　the　frequency　content　over　time.　We　propose　a　multi-stage　unsupervised　model　that　integrates　the　features　extracted　from　the　global　handcrafted　engineering,　channel-wise　deep　learning,　and　EEG　embeddings,　respectively.　The　learned　multi-context　features　are　subsequently　merged　to　train　a　seizure　detector.　Conclusions:　To　validate　the　effectiveness　of　the　proposed　approach,　extensive　experiments　against　several　baseline　methods　are　carried　out　on　two　benchmark　biological　datasets.　The　experimental　results　demonstrate　that　the　representative　context　features　from　multiple　perspectives　can　be　learned　by　the　proposed　model,　and　further　improve　the　performance　for　the　task　of　EEG　seizure　detection.