  Non-invasive fetal electrocardiogram (FECG) is used to monitor the electrical
pulse of the fetal heart. Decomposing the FECG signal from maternal ECG (MECG)
is a blind source separation problem, which is hard due to the low amplitude of
FECG, the overlap of R waves, and the potential exposure to noise from
different sources. Traditional decomposition techniques, such as adaptive
filters, require tuning, alignment, or pre-configuration, such as modeling the
noise or desired signal. to map MECG to FECG efficiently. The high correlation
between maternal and fetal ECG parts decreases the performance of convolution
layers. Therefore, the masking region of interest using the attention mechanism
is performed for improving signal generators' precision. The sine activation
function is also used since it could retain more details when converting two
signal domains. Three available datasets from the Physionet, including A&D
FECG, NI-FECG, and NI-FECG challenge, and one synthetic dataset using FECGSYN
toolbox, are used to evaluate the performance. The proposed method could map
abdominal MECG to scalp FECG with an average 98% R-Square [CI 95%: 97%, 99%] as
the goodness of fit on A&D FECG dataset. Moreover, it achieved 99.7 % F1-score
[CI 95%: 97.8-99.9], 99.6% F1-score [CI 95%: 98.2%, 99.9%] and 99.3% F1-score
[CI 95%: 95.3%, 99.9%] for fetal QRS detection on, A&D FECG, NI-FECG and
NI-FECG challenge datasets, respectively. These results are comparable to the
state-of-the-art; thus, the proposed algorithm has the potential of being used
for high-performance signal-to-signal conversion.
