Representation　learning　for　time　series　has　gained　increasing　attention　in　healthcare　domain.　The　recent　advancement　in　semantic　learning　allows　researcher　to　learn　meaningful　deep　representations　of　clinical　medical　concepts　from　Electronic　Health　Records　(EHRs).　However,　existing　models　cannot　deal　with　continuous　physiological　records,　which　are　often　included　in　EHRs.　The　major　challenges　for　this　task　are　to　model　non-obvious　representations　from　observed　high-resolution　biosignals,　and　to　interpret　the　learned　features.　To　address　these　issues,　we　propose　Wave2Vec,　an　end-to-end　deep　representation　learning　model,　to　bridge　the　gap　between　biosignal　processing　and　semantic　learning.　Wave2Vec　not　only　jointly　learns　both　inherent　and　temporal　representations　of　biosignals,　but　also　allows　us　to　interpret　the　learned　representations　reasonably　over　time.　We　propose　two　embedding　mechanisms　to　capture　the　temporal　knowledge　within　signals,　and　discover　latent　knowledge　from　signals　in　time-frequency　domain,　namely　component-based　motifs.　To　validate　the　effectiveness　of　our　model　in　clinical　task,　we　carry　out　experiments　on　two　real-world　benchmark　biosignal　datasets.　Experimental　results　demonstrate　that　the　proposed　Wave2Vec　model　outperforms　six　feature　learning　baselines　in　biosignal　processing.　Analytical　results　show　that　the　proposed　model　can　incorporate　both　motif　co-occurrence　information　and　time　series　information　of　biosignals,　and　hence　provides　clinically　meaningful　interpretation.　(C)　2018　Elsevier　B.V.　All　rights　reserved.