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 electrocardiogram (ECG) due to the following problems: 1) the FECG signal is weak and often masked by noise and 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 an 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, an 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 antialiasing 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 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.