  Monitoring the electrical pulse of fetal heart through a non-invasive fetal
electrocardiogram (fECG) can easily detect abnormalities in the developing
heart to significantly reduce the infant mortality rate and post-natal
complications. Due to the overlapping of maternal and fetal R-peaks, the low
amplitude of the fECG, systematic and ambient noises, typical signal extraction
methods, such as adaptive filters, independent component analysis, empirical
mode decomposition, etc., are unable to produce satisfactory fECG. While some
techniques can produce accurate QRS waves, they often ignore other important
aspects of the ECG. Our approach, which is based on 1D CycleGAN, can
reconstruct the fECG signal from the mECG signal while maintaining the
morphology due to extensive preprocessing and appropriate framework. The
performance of our solution was evaluated by combining two available datasets
from Physionet, "Abdominal and Direct Fetal ECG Database" and "Fetal
electrocardiograms, direct and abdominal with reference heartbeat annotations",
where it achieved an average PCC and Spectral-Correlation score of 88.4% and
89.4%, respectively. It detects the fQRS of the signal with accuracy,
precision, recall and F1 score of 92.6%, 97.6%, 94.8% and 96.4%, respectively.
It can also accurately produce the estimation of fetal heart rate and R-R
interval with an error of 0.25% and 0.27%, respectively. The main contribution
of our work is that, unlike similar studies, it can retain the morphology of
the ECG signal with high fidelity. The accuracy of our solution for fetal heart
rate and R-R interval length is comparable to existing state-of-the-art
techniques. This makes it a highly effective tool for early diagnosis of fetal
heart diseases and regular health checkups of the fetus.
