Maternal ECG-guided neural network for improved fetal electrocardiogram extraction.

• The proposed M aternal E CG- G uided Neural Net work (MEG-Net) integrates maternal ECG as a reference, enhancing fetal ECG extraction by providing spatial and temporal information about maternal ECG components. • This approach is particularly beneficial in challenging scenarios where fetal and maternal QRS complexes coincide. • MEG-Net is rigorously compared against other learning-based approaches and conventional abdominal electrode-sourced (AES) and combined sources (CS) methods, aiming to demonstrate its effectiveness and potential advantages in fetal ECG extraction. Historically, acquiring a reliable and accurate non-invasive fetal electrocardiogram has several significant challenges in both data acquisition and attenuation of maternal signals. These barriers include maternal physical/physiological parameters, hardware sensitivity, and the effectiveness of signal processing algorithms in separating maternal and fetal electrocardiograms. In this paper, we focus on the evaluation of signal-processing algorithms. Here, we propose a learning-based method based on the integration of maternal electrocardiogram acquired as guidance for transabdominal fetal electrocardiogram signal extraction. The results demonstrate that incorporating the maternal electrocardiogram signal as input for training the neural network outperforms the network solely trained using information from the abdominal electrocardiogram. This indicates that leveraging the maternal electrocardiogram serves as a suitable prior for effectively attenuating maternal electrocardiogram from the abdominal electrocardiogram. [ABSTRACT FROM AUTHOR]