Every year, about one out of 125 babies born with some form of congenital heart defect. Therefore extraction of fetal electrocardiogram (FECG) from maternal skin electrode measurements will be raised as a prominent issue. Because of fetal heart farness from sensors, muscle contraction, instrumentation noise and etc, recorded signals from mother’s abdomin is strongly distorted by noise. So desired signal (FECG) must be extracted purely. This problem can be modelled from the perspective of Blind Source Separation (BSS), almost all the BSS algorithms can be used to separate the fetal ECG. Since separating all the sources from a large number of sensor signals is not necessary, blind source extraction (BSE) methods may be a better choice. In this paper we proposed a lightweight algorithm, which extracts the fetal ECG with a preknowledge about its skewness. By using skewness, we defined a cost function by which we updated weight vector and through this we extracted fetal ECG as a desired signal. Experimental results show that the proposed method improved quality of extracted signal by increasing SNRsvd and SNRcor. Also computational cost required for extracting FECG was decreased.
