Fetal ECG Extraction from Maternal ECG using Attention-based CycleGAN

• URL: http://arxiv.org/abs/2011.12138v2
• Date: Tue, 9 Feb 2021 22:02:27 GMT
• Title: Fetal ECG Extraction from Maternal ECG using Attention-based CycleGAN
• Authors: Mohammad Reza Mohebbian, Seyed Shahim Vedaei, Khan A. Wahid, Anh Dinh, Hamid Reza Marateb, Kouhyar Tavakolian
• Abstract summary: Non-invasive fetal electrocardiogram (FECG) is used to monitor the electrical pulse of the fetal heart. Traditional decomposition techniques, such as adaptive filters, require tuning, alignment, or pre-configuration. to map MECG to FECG efficiently. The proposed method could map abdominal MECG to scalp FECG with an average 98% R-Square as the goodness of fit on A&D FECG dataset. 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%:
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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