Depression　is　a　common　mental　disease　characterized　by　significant　sadness　and　feeling　blue　all　the　time.　At　present,　most　classifications　and　predictions　of　depression　rely　on　different　characteristics.　Comparing　with　the　previous　work,　we　use　local　binary　pattern　(LBP)　and　signal　singular　spectrum　analysis　(SSA)　technology　to　extract　features　from　the　original　signal.　Firstly,　the　LBP　signal　is　obtained　by　encoding　the　segmented　signal.　Then,　we　use　SSA　to　decompose　and　reconstruct　the　LBP　signal　to　remove　noise　and　divide　the　frequency　band.　Finally,　we　feed　the　data　of　each　frequency　band　to　K-nearest　neighbor　(KNN),　decision　tree　(DT),　support　vector　machine　(SVM)　and　extreme　learning　machine　(ELM)　for　classification.　The　experimental　results　show　that　LBP　and　SSA　features　achieve　the　best　classification　effect　on　SVM,　and　the　accuracy　of　beta　band　is　the　highest　with　99.24%　accuracy,　99.34%　sensitivity　and　99.12%　specificity　respectively.　©　2020,　Springer　Nature　Switzerland　AG.