Background　Sleep　is　a　complex　and　dynamic　biological　process　characterized　by　different　sleep　patterns.　Comprehensive　sleep　monitoring　and　analysis　using　multivariate　polysomnography　(PSG)　records　has　achieved　significant　efforts　to　prevent　sleep-related　disorders.　To　alleviate　the　time　consumption　caused　by　manual　visual　inspection　of　PSG,　automatic　multivariate　sleep　stage　classification　has　become　an　important　research　topic　in　medical　and　bioinformatics.　Results　We　present　a　unified　hybrid　self-attention　deep　learning　framework,　namely　HybridAtt,　to　automatically　classify　sleep　stages　by　capturing　channel　and　temporal　correlations　from　multivariate　PSG　records.　We　construct　a　new　multi-view　convolutional　representation　module　to　learn　channel-specific　and　global　view　features　from　the　heterogeneous　PSG　inputs.　The　hybrid　attention　mechanism　is　designed　to　further　fuse　the　multi-view　features　by　inferring　their　dependencies　without　any　additional　supervision.　The　learned　attentional　representation　is　subsequently　fed　through　a　softmax　layer　to　train　an　end-to-end　deep　learning　model.　Conclusions　We　empirically　evaluate　our　proposed　HybridAtt　model　on　a　benchmark　PSG　dataset　in　two　feature　domains,　referred　to　as　the　time　and　frequency　domains.　Experimental　results　show　that　HybridAtt　consistently　outperforms　ten　baseline　methods　in　both　feature　spaces,　demonstrating　the　effectiveness　of　HybridAtt　in　the　task　of　sleep　stage　classification.