PA²Net: Period-Aware Attention Network for Robust Fetal ECG Detection

The noninvasive fetal ECG (FECG) is used to monitor fetal well-being at prenatal and intrapartum. However, it is challenging to detect the FECG signal from the abdominal ECG due to the following problems: 1) the FECG signal is weak and often masked by noise; 2) the FECG signal is mixed or overlapped with the maternal ECG (MECG) signal. To solve such problems, a Period-Aware Attention Network (PA²Net) is proposed for FECG detection by designing three modules, where a FECG period-aware attention module (FPAM), which is designed to suppress the noise interference by modeling the periods and features of signals, is first employed to detect FECG signals masked in noise; next, a MECG period-aware attention module, which is generated from the FPAM by the KL-divergence-based weight sharing module, can collaborate with the FPAM to search for mixed ECG signals of FECG and MECG; finally, an anti-aliasing signal separation module is developed to estimate FECG signals from mixed ECG signals. Experiments conducted on the benchmarks show that the proposed PA²Net can achieve the excellent performance with the PPV of 98.83%, 99.74%, 99.67%, and 98.47% on PCDB, ADFECGDB, NIFECGDB, and LHDB databases, respectively, allowing obstetricians to use PA²Net to monitor fetal health and diagnose diseases.