Wavelet-Transformer Attention Network for Accurate Fetal ECG Estimation from Multi-Channel Abdominal Signals

Accurate fetal electrocardiogram extraction from abdominal recordings remains challenging due to strong maternal electrocardiogram artifacts and low signal quality. To address these issues, a Wavelet-Transformer Attention Network (WTA-Net) is proposed for fetal electrocardiogram extraction, where the Cross-Attention Transformer (CAT) module is devised to suppress maternal interference by modeling cross-modal interactions, and the Residual Shrinkage (RS) module is designed to attenuate noise artifact through adaptive thresholding. Validation findings reveal that the proposed WTA-Net outperforms state-of-the-art methods, achieving positive predictive values of 99.82% and 99.87% for fetal QRS detection on the ADFECGDB and B2_LABOUR databases, respectively, further enhancing the reliability of prenatal monitoring.