Wavelet-Transformer Attention Network for Accurate Fetal ECG Estimation from Multi-Channel Abdominal Signals.
Where this comes from
- Record sourced from PubMed, PMID 42085408.
- Also identified by DOI 10.1109/JBHI.2026.3690589.
- No licence information is recorded for this record.
- Because redistribution is not established, this page shows the abstract only. Follow the links below for the full text.
Abstract
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.