With　the　advances　in　pervasive　sensor　technologies,　physiological　signals　can　be　captured　continuously　to　prevent　the　serious　outcomes　caused　by　epilepsy.　Detection　of　epileptic　seizure　onset　on　collected　multi-channel　electroencephalogram　(EEG)　has　attracted　lots　of　attention　recently.　Deep　learning　is　a　promising　method　to　analyze　large-scale　unlabeled　data.　In　this　paper,　we　propose　a　multi-view　deep　learning　model　to　capture　brain　abnormality　from　multi-channel　epileptic　EEG　signals　for　seizure　detection.　Specifically,　we　first　generate　EEG　spectrograms　using　short-time　Fourier　transform　(STFT)　to　represent　the　time-frequency　information　after　signal　segmentation.　Second,　we　adopt　stacked　sparse　denoising　autoencoders　(SSDA)　to　unsupervisedly　learn　multiple　features　by　considering　both　intra　and　inter　correlation　of　EEG　channels,　denoted　as　intra-channel　and　cross-channel　features,　respectively.　Third,　we　add　an　SSDA-based　channel　selection　procedure　using　proposed　response　rate　to　reduce　the　dimension　of　intra-channel　feature.　Finally,　we　concatenate　the　learned　multi-features　and　apply　a　fully-connected　SSDA　model　with　softmax　classifier　to　jointly　learn　the　cross-patient　seizure　detector　in　a　supervised　fashion.　To　evaluate　the　performance　of　the　proposed　model,　we　carry　out　experiments　on　a　real　world　benchmark　EEG　dataset　and　compare　it　with　six　baselines.　Extensive　experimental　results　demonstrate　that　the　proposed　learning　model　is　able　to　extract　latent　features　with　meaningful　interpretation,　and　hence　is　effective　in　detecting　epileptic　seizure.