ECG　(ElectroCardioGram)　signals　are　widely　used　in　the　medical　detection　of　heart　disease,　and　wearable　dynamic　ECG　monitoring　devices　enable　the　detection　and　early　warning　of　cardiac　arrhythmias.　Compared　to　resting　ECG　signals,　dynamic　ECG　signals　are　more　susceptible　to　interference　from　motion　artifacts　during　the　data　acquisition　process.　These　motion　artifacts　can　obscure　critical　information　within　the　ECG　signal,　limiting　its　clinical　utility.　In　this　paper,　taking　into　account　the　local　and　global　characteristics　of　the　ECG　signal　and　using　its　periodicity,　a　two-stage　adaptive　threshold　filtering　algorithm　is　investigated　to　process　the　low-frequency　PT　wave　and　the　high-frequency　QRS　wave　group　separately,　which　is　suitable　for　motion　artifact　filtering　in　single-channel　ECG　signal.　In　the　first　step,　motion　artifacts　in　the　low-frequency　part　of　the　ECG　signal　are　suppressed　by　a　multi-resolution　threshold.　In　the　second　step,　the　imbalanced　QRS　wave　affected　by　motion　artifacts　is　repaired　by　adaptive　threshold,　adjusting　the　QRS　waveform　to　reduce　motion　artifacts　in　the　high-frequency　portion　of　the　ECG　signal,　while　setting　adaptive　thresholds　to　process　the　wavelet　coefficients　corresponding　to　the　P-wave　and　T-wave　of　the　ECG　signal.　Wavelet　coefficients　beyond　the　adaptive　threshold　range　are　adjusted　via　waveform　scaling　to　further　suppress　the　low-frequency　motion　artifacts.　In　this　paper,　the　performance　of　the　algorithm　is　evaluated　using　different　ECG　databases.　When　the　input　SNR　changes　from　-10~10　dB,　the　SNR　of　the　ECG　signal　increases　by　10.912　2　dB　and　4.391　2　dB,　respectively,　and　the　correlation　coefficients　between　the　filtered　ECG　signal　and　the　pure　ECG　signal　are　0.687　6　and　0.978　3,　respectively,　the　correlation　coefficients　between　the　extracted　motion　artifacts　and　the　original　motion　artifacts　are　0.953　0　and　0.852　9,　respectively.　The　experimental　results　show　that　under　different　noise　levels,　the　proposed　algorithm　can　effectively　recover　the　ECG　waveform　characteristics　contaminated　by　motion　artifacts　by　exploiting　the　advantages　of　adaptive　threshold,　and　retain　the　clinical　information　of　ECG　signals　to　the　maximum　extent,　and　can　be　used　as　an　effective　tool　for　filtering　motion　artifacts　in　wearable　ECG　devices.　©　2024　Chinese　Institute　of　Electronics.　All　rights　reserved.