In　dynamic　ECG　signals,　EMG　interference　is　a　common　type　of　noise　and　overlaps　with　the　ECG　signal　spectrum.　Herein,　we　propose　an　EMG-filtering　method　that　combines　an　adaptive　wavelet-Wiener　filter　and　an　adaptive　moving　average　filter.　First,　the　R-peaks　detection　method　is　used　to　divide　the　signal　into　individual　heartbeats,　and　wavelet　threshold　processing　is　used　to　evaluate　signal-to-noise　ratio.　The　wavelet-Wiener　filter＇s　parameters　are　then　set　adaptively　according　to　the　SNR,　and　the　resulting　adaptive　wavelet-Wiener　filter　is　applied　to　each　heartbeat.　Next,　the　low　frequency　components　between　two　heartbeats　are　windowed　to　calculate　standard　deviation,　and　the　order　of　the　moving　average　filter　is　set　adaptively　according　to　the　estimated　SNR　and　standard　deviation.　Finally,　a　moving　average　filter　is　applied　to　the　low　frequency　components　to　filter　residual　EMG　noise.　Before　and　after　applying　the　moving　average　filter,　splicing　and　discarding　are　performed　on　both　sides　of　the　low　frequency　components,　effectively　preventing　distortion　at　the　transition　between　the　high　and　low　frequency　components.　We　evaluated　the　proposed　method＇s　performance　using　different　ECG　databases,　including　arrhythmia　and　dynamic　ECG　signals　with　motion　artifacts,　and　compared　its　performance　with　that　of　other　methods.　Experimental　results　show　that　the　proposed　method　can　significantly　improve　SNR　and　effectively　preserve　clinical　information,　especially　in　environments　with　severe　noise.　The　proposed　method　utilizes　the　advantages　of　both　applied　techniques　to　reduce　the　noise　in　ECG　signals　with　minimal　distortion　and　can　be　used　as　an　effective　tool　for　denoising　ECG　signals.