Deep learning applied to non-invasive ECG pregnancy monitoring: fetal ECG recovery and QRS complex detection - Remus, Julia C.; Silveira, Thiago L. T. da (Anais Estendidos do Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS); 2025: Anais Estendidos do XXV Simpósio Brasileiro de Computação Aplicada à Saúde, 2025)
Non-invasive electrocardiography (niECG) is an option to mitigate the limitations of the current pregnancy monitoring methods. Still, one of the main challenges in its use is the extraction of the fetal signal (fECG) from the measured abdominal signal (aECG) due to the low signal-to-noise ratio and the overlap of the maternal and fetal QRS complexes in time and frequency domains. We present two encoder-decoder deep learning models that use regions of interest (RoI) to perform simultaneous fetal QRS complex (fQRS) detection and fECG recovery. We show that such a RoI-based end-to-end approach leads to robust state-of-the-art results in inter-subject and cross-dataset evaluations.