End-to-End Deep Learning Architecture for Separating Maternal and Fetal ECGs Using W-Net

Abstract

Fetal cardiac monitoring and assessment during pregnancy play a critical role in the early detection of the potential risk of fetal cardiac problems, thus allowing for timely preventive measures and healthy births. It is necessary to continuously monitor the fetal heart for this purpose. Methods of fetal cardiac monitoring by extracting maternal and fetal electrocardiograms (ECGs) from maternal abdominal ECGs have been extensively investigated. However, the extraction of a clear fetal ECG is a major challenge because fetal signals are typically dominated by maternal ECG signals and noise. Most existing methods for fetal ECG extraction involve several steps, such as extracting and removing the maternal ECG and then extracting the fetal ECG. To address the complexity of this process, we propose a novel method for effectively decomposing a single-channel maternal abdominal ECG into a maternal ECG and fetal ECG without using multiple steps by employing an end-to-end deep learning network architecture using W-net. Model training is performed using a simulation dataset. Then, a fetal ECG is extracted from a real maternal abdominal ECG. The performance of the proposed architecture is compared with that of other state-of-the-art deep learning models on the basis of the detection of QRS complexes. The proposed model shows higher precision and recall values and F1 scores. This demonstrates that the proposed model can effectively extract a fetal ECG from a single-channel maternal abdominal ECG. The model is expected to contribute to commercial applications for long-term maternal and fetal monitoring.