Non-invasive fECG monitoring through multifaceted temporal feature interaction of maternal abdominal electrocardiography

Fetal electrocardiography (fECG) signals are an essential non-invasive indicator for monitoring fetal cardiac health. However, extracting high-quality fECG signals from maternal abdominal electrocardiography (aECG) remains challenging due to severe interference from maternal electrocardiography (mECG) and ambient noise. Existing fECG extracting methods have achieved remarkable performance. However, most works fall short in modeling the long-term dynamic changes between consecutive heartbeats, resulting in low discrimination between different heartbeats with similar semantic information, such as rhythm and amplitude variations. To this end, we propose a dual-branch architecture that accurately extracts fECG signals by combining the efficiency of CNNs in extracting local heartbeat information with the capability of Transformers to model long-term dependencies between consecutive heartbeats. A collaborative feature interaction unit is further introduced to enhance information communication within the parallel branches and exploit the complementarity between detailed morphological patterns and long-range temporal dependencies. Experimental results on two public datasets showcase the exceptional performance of our proposed method, achieving Pearson correlation coefficient of 0.90 on ADDB and 0.94 on BDDB-L in assessing fECG signal quality. These findings suggest that our approach offers a promising solution for continuous fetal heart monitoring.