Recently,　significant　efforts　have　been　made　to　explore　comprehensive　sleep　monitoring　to　prevent　sleep-related　disorders.　Multivariate　sleep　stage　classification　has　garnered　great　interest　among　researchers　in　health　informatics.　In　this　paper,　we　propose　HybridAtt,　a　unified　hybrid　self-attentive　deep　learning　network,　to　classify　sleep　stages　from　multivariate　polysomnography　(PSG)　records.　HybridAtt　is　an　end-to-end　model　that　explicitly　captures　the　complex　correlations　among　biomedical　channels　and　the　dynamic　relationships　over　time.　By　constructing　a　new　multi-view　convolutional　representation　module,　HybridAtt　is　able　to　extract　hidden　features　from　both　channel-specific　and　global　views　of　the　heterogeneous　PSG　inputs.　In　order　to　enhance　feature　representation,　a　new　fusion-based　attention　mechanism　is　also　proposed　to　integrate　the　complementary　information　carried　by　each　feature　view.　To　evaluate　the　performance　of　our　model,　we　carry　out　experiments　on　a　benchmark　PSG　dataset.　Experimental　results　show　that　the　proposed　HybridAtt　model　achieves　better　performance　compared　to　ten　baseline　methods,　demonstrating　the　effectiveness　of　HybridAtt　in　the　task　of　sleep　stage　classification.