Extracting　clean　fetal　electrocardiogram　(ECG)　signals　is　very　important　in　fetal　monitoring.　In　this　paper,　we　proposed　a　new　method　for　fetal　ECG　extraction　based　on　wavelet　analysis,　the　least　mean　square　(LMS)　adaptive　filtering　algorithm,　and　the　spatially　selective　noise　filtration　(SSNF)　algorithm.　First,　abdominal　signals　and　thoracic　signals　were　processed　by　stationary　wavelet　transform　(SWT),　and　the　wavelet　coefficients　at　each　scale　were　obtained.　For　each　scale,　the　detail　coefficients　were　processed　by　the　LMS　algorithm.　The　coefficient　of　the　abdominal　signal　was　taken　as　the　original　input　of　the　LMS　adaptive　filtering　system,　and　the　coefficient　of　the　thoracic　signal　as　the　reference　input.　Then,　correlations　of　the　processed　wavelet　coefficients　were　computed.　The　threshold　was　set　and　noise　components　were　removed　with　the　SSNF　algorithm.　Finally,　the　processed　wavelet　coefficients　were　reconstructed　by　inverse　SWT　to　obtain　fetal　ECG.　Twenty　cases　of　simulated　data　and　12　cases　of　clinical　data　were　used.　Experimental　results　showed　that　the　proposed　method　outperforms　the　LMS　algorithm:　(1)　it　shows　improvement　in　case　of　superposition　R-peaks　of　fetal　ECG　and　maternal　ECG;　(2)　noise　disturbance　is　eliminated　by　incorporating　the　SSNF　algorithm　and　the　extracted　waveform　is　more　stable;　and　(3)　the　performance　is　proven　quantitatively　by　SNR　calculation.　The　results　indicated　that　the　proposed　algorithm　can　be　used　for　extracting　fetal　ECG　from　abdominal　signals.　(C)　2013　Elsevier　Ltd.　All　rights　reserved.