The proposed Wavelet-Transformer Attention Network (WTA-Net) outperformed state-of-the-art methods for fetal QRS detection, achieving positive predictive values of 99.82% and 99.87% on two databases.
The WTA-Net algorithm demonstrates high accuracy in extracting fetal ECG signals from abdominal recordings, potentially improving prenatal monitoring reliability.
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 B2LABOUR databases, respectively, further enhancing the reliability of prenatal monitoring.
Wang et al. (Thu,) conducted a other in Fetal electrocardiogram extraction. Wavelet-Transformer Attention Network (WTA-Net) vs. State-of-the-art methods was evaluated on Fetal QRS detection (positive predictive value). The proposed Wavelet-Transformer Attention Network (WTA-Net) outperformed state-of-the-art methods for fetal QRS detection, achieving positive predictive values of 99.82% and 99.87% on two databases.