Abstract: Objective To extract weak fetal ECG signals from mixed ECG signals recorded from maternal abdominal wall for accurate analysis of fetal heart rate and fetal ECG patterns. Methods By exploiting the superior nonlinear mapping ability of deep convolutional network, we developed a nonlinear adaptive noise cancelling (nonlinear ANC) extraction framework based on a temporal convolutional encoder-decoder network for extracting fetal ECG signals. We first constructed a deep temporal convolutional network (TCED-Net) model for fetal ECG signal extraction, and with the maternal chest ECG signal as the reference signal, the maternal ECG component in the abdominal mixed signal was estimated using this model. The estimated maternal ECG component was subtracted from the mixed abdominal ECG signals to obtain the fetal ECG component. Experimental analyses were performed using synthetic ECG signals (FECGSYNDB) and clinical ECG signals (NIFECGDB, PCDB) to test the performance of the propose method. Results The results of experiments on the FECGSYNDB dataset showed that the proposed approach achieved good performance in F1-score (98.89%), mean-square-error (MSE; 0.20) and quality signal-to-noise ratio (qSNR; 7.84). The F1- score reached 99.1% on the NIFECGDB dataset and 98.61% on the PCDB dataset. The R peak detection accuracy index of the proposed method was higher than the existing best-performing algorithms such as EKF (F1=93.84%), ES-RNN (F1=97.20%) and AECG-DecompNet (F1=95.43%) by 5.05%, 1.9% and 3.18%, respectively. Conclusion The fetal ECG signals extracted using the proposed method are clearer than those by the existing algorithms, suggesting the potential value this method for effective fetal health monitoring during pregnancy.

Key words: fetal monitoring; adaptive filtering; temporal convolutional neural networks; non-invasive fetal electrocardiogram; fetal heart rate