Complexity-Reduced End-to-End Fetal ECG Signal Recovery and QRS Complex Detection - Remus, Julia C.; Silveira, Thiago L. T. da (Anais do Simpósio Brasileiro de Computação Aplicada à Saúde (SBCAS); 2025: Anais do XXV Simpósio Brasileiro de Computação Aplicada à Saúde, 2025)
Non-invasive electrocardiogram (niECG) enables pregnancy monitoring by assessing the mother’s and fetus’ health through maternal abdominal signal acquisition. However, isolating fetal ECG (fECG) is challenging due to low signal-to-noise ratio and time and frequency overlap with maternal cardiac activity. Prior studies focused on fetal R-peak detection and more recent works target full fECG waveform noise-free reconstruction, but at a high computational cost, precluding real-world applications. This paper explores different ways of reducing the complexity of state-of-the-art deep learning-based method for fECG recovery and fetal QRS complex localization. Results indicate that one encoder-decoder block pair can be removed without significantly impacting the metrics. While the other complexity-reduction options yield slightly lower metrics compared to the baseline and related works, they can be fine-tuned and adapted based on the specific requirements and objectives of the application.