Accurate　automatic　sleep　staging　is　crucial　for　diagnosing　sleep　disorders.　However,　most　existing　automatic　data-driven　sleep　staging　methods　could　not　perfectly　learn　the　complex　knowledge　of　sleep　staging　criteria　such　as　the　American　Academy　of　Sleep　Medicine　(AASM)　based　on　the　limited　labeled　data.　This　paper　proposes　a　novel　multimodal　and　multiscale　automatic　sleep　staging　framework,　MMS-SleepNet,　which　explicitly　incorporates　AASM　knowledge.　It　employs　a　deep　learning　multimodal　feature　extraction　module　(MMS-FE),　embedding　expert　knowledge　to　effectively　capture　multimodal　features　for　each　stage　and　fine-grained　EEG　features　at　various　frequencies.　The　module　utilizes　an　attention　mechanism　to　seamlessly　fuse　extracted　multimodal　features,　significantly　enhancing　classification　accuracy.　To　further　improve　the　performance　of　MMS-SleepNet,　a　contrastive　learning　module　and　a　data　balancing　strategy　are　proposed,　addressing　class　confusion　and　data　imbalance　issues　in　existing　models.　Specifically,　the　contrastive　classification　module　(CCM)　emphasizes　intra-class　similarity　and　inter-class　disparity,　effectively　alleviating　class　confusion.　A　simple　yet　effective　data　balancing　mechanism　augments　the　number　of　samples　for　the　N1　sleep　stage,　guaranteeing　that　the　model　is　trained　on　a　more　balanced　dataset　and　proficiently　resolves　the　long-tail　distribution　problem　stemming　from　class　imbalance.　Experimental　results　on　two　public　datasets　validate　the　effectiveness　of　MMS-SleepNet,　achieving　a　remarkable　accuracy　of　92.9%　on　the　Sleep-EDF-20　dataset,　surpassing　other　methods.　Notably,　it　attains　a　74.1%　accuracy　in　the　challenging　N1　stage,　outperforming　other　methods　by　19.6–49.2%.　©　2024　Elsevier　Ltd