Sleep　stage　classification　based　on　physiological　signals　is　essential　for　monitoring　sleep　and　diagnosing　sleep　disorders.　Artificial　sleep　stage　classification　method　is　time-consuming,　inefficient,　and　subjective.　In　recent　years,　automatic　sleep　stage　classification　method　has　attracted　more　attention　due　to　its　efficiency　and　accuracy.　Therefore,　the　automatic　sleep　stage　classification　algorithms　are　reviewed　from　the　past　six　years　based　on　the　perspective　of　algorithm.　The　literature　is　classified　as　traditional　machine　learning　and　deep　learning,　and　each　category　is　further　summarized　based　on　single-　and　multichannel　physiological　signal　inputs,　illustrating　algorithms,　signal　types,　and　sleep　staging　performance.　Comparison　across　the　methods　indicates　that　the　single-channel　input　reduces　the　cost　of　signal　acquisition,　making　it　more　suitable　for　home　sleep　monitoring.　The　multi-channel　input　is　closer　with　sleep　staging　guidelines,　which　is　more　appropriate　for　clinical　analysis.　Compared　with　traditional　machine　learning　methods,　deep　learning　methods　offer　more　promising　researching　prospects,　because　they　utilize　deep　neural　networks　to　automatically　learn　representation,　which　efficiently　handle　large-scale　dataset　and　provide　better　sleep　staging　performance.　Existing　works　demonstrate　that　the　future　sleep　staging　research　of　deep　learning　should　focus　more　on　improving　model　interpretability　and　generalization　instead　of　model　design,　to　promote　the　application　of　deep　neural　networks　in　sleep　medicine　field.　©　2025　Beijing　University　of　Technology.　All　rights　reserved.