Fetal　electrocardiograms　(FECGs)　provide　important　clinical　information　for　early　diagnosis　and　intervention.　However,　FECG　signals　are　extremely　weak　and　are　greatly　influenced　by　noises.　FECG　signal　extraction　and　detection　are　still　challenging.　In　this　work,　we　combined　the　fast　independent　component　analysis　(FastICA)　algorithm　with　singular　value　decomposition　(SVD)　to　extract　FECG　signals.　The　improved　wavelet　mode　maximum　method　was　applied　to　detect　QRS　waves　and　ST　segments　of　FECG　signals.　We　used　the　abdominal　and　direct　fetal　ECG　database　(ADFECGDB)　and　the　Cardiology　Challenge　Database　(PhysioNet2013)　to　verify　the　proposed　algorithm.　The　signal-to-noise　ratio　of　the　best　channel　signal　reached　45.028　dB　and　the　issue　of　missing　waveforms　was　addressed.　The　sensitivity,　positive　predictive　value　and　F1　score　of　fetal　QRS　wave　detection　were　96.90%,　98.23%,　and　95.24%,　respectively.　The　proposed　algorithm　may　be　used　as　a　new　method　for　FECG　signal　extraction　and　detection.