The proposed attention-based CycleGAN method mapped abdominal maternal ECG to scalp fetal ECG with an average 98% R-Square and achieved a 99.7% F1-score for fetal QRS detection on the A&D FECG dataset.
An attention-based CycleGAN algorithm successfully extracts fetal ECG from maternal abdominal ECG with high fidelity and accurate QRS detection, offering a robust non-invasive monitoring tool.
A non-invasive fetal electrocardiogram (FECG) is used to monitor the electrical pulse of the fetal heart. Decomposing the FECG signal from the maternal ECG (MECG) is a blind source separation problem, which is hard due to the low amplitude of the 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 the MECG to the FECG. The high correlation between maternal and fetal ECG fragments decreases the performance of convolution layers. Therefore, the masking region of interest based on the attention mechanism was performed to improve the signal generators' precision. The sine activation function was also used to retain more details when converting two signal domains. Three available datasets from the Physionet, including the A thus, the proposed algorithm has the potential to be used for high-performance signal-to-signal conversion.
Mohebbian et al. (Mon,) conducted a other in Fetal Electrocardiogram (FECG) Extraction (n=88). Attention-Based CycleGAN vs. State-of-the-art methods (e.g., Encoder-Decoder, SVD-SW) was evaluated on R-Square goodness of fit for mapping abdominal maternal ECG to scalp fetal ECG on the A&D FECG dataset (95% CI 97%-99%). The proposed attention-based CycleGAN method mapped abdominal maternal ECG to scalp fetal ECG with an average 98% R-Square and achieved a 99.7% F1-score for fetal QRS detection on the A&D FECG dataset.