Objective.　The　fast　fixed-point　algorithm　for　independent　component　analysis　(FastICA)　has　been　widely　used　in　fetal　electrocardiogram　(ECG)　extraction.　However,　the　FastICA　algorithm　is　sensitive　to　the　initial　weight　vector,　which　affects　the　convergence　of　the　algorithm.　In　order　to　solve　this　problem,　an　improved　FastICA　method　was　proposed　to　extract　fetal　ECG.　Methods.　First,　the　maternal　abdominal　mixed　signal　was　centralized　and　whitened,　and　the　overrelaxation　factor　was　incorporated　into　Newton＇s　iterative　algorithm　to　process　the　initial　weight　vector　randomly　generated.　The　improved　FastICA　algorithm　was　used　to　separate　the　source　components,　selected　the　best　maternal　ECG　from　the　separated　source　components,　and　detected　the　R-wave　location　of　the　maternal　ECG.　Finally,　the　maternal　ECG　component　in　each　channel　was　removed　by　the　singular　value　decomposition　(SVD)　method　to　obtain　a　clean　fetal　ECG　signal.　Results.　An　annotated　clinical　fetal　ECG　database　was　used　to　evaluate　the　improved　algorithm　and　the　conventional　FastICA　algorithm.　The　average　number　of　iterations　of　the　algorithm　was　reduced　from　35　before　the　improvement　to　13.　Correspondingly,　the　average　running　time　was　reduced　from　1.25　s　to　1.04　s　when　using　the　improved　algorithm.　The　signal-to-noise　ratio　(SNR)　based　on　eigenvalues　of　the　improved　algorithm　was　1.55,　as　compared　to　0.99　of　the　conventional　FastICA　algorithm.　The　SNR　based　on　cross-correlation　coefficients　of　the　conventional　algorithm　was　also　improved　from　0.59　to　2.02.　The　sensitivity,　positive　predictive　accuracy,　and　harmonic　mean　(F-1)　of　the　improved　method　were　99.37%,　99.00%,　and　99.19%,　respectively,　while　these　metrics　of　the　conventional　FastICA　method　were　99.03%,　98.53%,　and　98.78%,　respectively.　Conclusions.　The　proposed　improved　FastICA　algorithm　based　on　the　overrelaxation　factor,　while　maintaining　the　rate　of　convergence,　relaxes　the　requirement　of　initial　weight　vector,　avoids　the　unbalanced　convergence,　reduces　the　number　of　iterations,　and　improves　the　convergence　performance.