Objective.　Signal　denoising　methods　based　on　deep　learning　have　been　extensively　adopted　on　electroencephalogram　devices.　However,　they　are　unable　to　deploy　on　edge-based　portable　or　wearable　(P/W)　electronics　due　to　the　high　computational　complexity　of　the　existed　models.　To　overcome　such　issue,　we　propose　an　edge-based　lightweight　Kalman　filter　network　(EKFNet)　that　does　not　require　manual　prior　knowledge　estimation.　Approach.　Specifically,　we　construct　a　multi-scale　feature　fusion　module　to　capture　multi-scale　feature　information　and　implicitly　compute　the　prior　knowledge.　Meanwhile,　we　design　an　adaptive　gain　estimation　module　that　incorporates　long　short-term　memory　and　sequential　channel　attention　module　to　dynamically　predict　the　Kalman　gain.　Furthermore,　we　present　an　optimization　strategy　utilizing　operator　fusion　and　constant　folding　to　reduce　the　model＇s　computational　overhead　and　memory　footprint.　Main　results.　Experimental　results　show　that　the　EKFNet　reduces　the　sum　of　the　square　of　the　distances　by　at　least　12%　and　improves　the　cosine　similarity　by　at　least　2.2%　over　the　state-of-the-art　methods.　Besides,　the　model　optimization　shortens　the　inference　time　by　approximately　3.3x.　The　code　of　our　EKFNet　is　available　at　https://github.com/cathnat/EKFNet.　Significance.　By　integrating　Kalman　filter　with　deep　learning,　the　approach　addresses　the　parameter-setting　challenges　in　traditional　algorithms　while　reducing　computational　overhead　and　memory　consumption,　which　exhibits　a　good　tradeoff　between　algorithm　performance　and　computing　power.