Morphology Extraction of Fetal Electrocardiogram by Slow-Fast LSTM Network

The morphology of Fetal Electrocardiogram (FECG) plays an important role in the early diagnosis of fetal health condition. However, it is intractable to extract the clean morphology of FECG signals, which are usually contaminated by Maternal ECG (MECG) and various noises. To extract the clean morphology of FECG signals from noninvasive abdominal ECG records, a high-performance and high-efficient two-stage Slow-Fast Long Short Term Memory (SFLSTM) based architecture is proposed. The MECG elimination and the FECG enhancement are realized by the elaborately designed slow LSTM and fast LSTM to filter out the MECG and the residual noise components, respectively. Qualitative and quantitative experiments are conducted on the records from two public databases. The experimental results show that our proposed MECG elimination and FECG enhancement schemes improve the Signal-to-Noise Ratio (SNR) by 3.09 dB and 1.81 dB, respectively. The proposed fast LSTM reduces the amount
of computation by approximately 50%, without any degradation in performance. Our proposed method may leverage the noninvasive FECG monitoring for the early detection of fetal heart diseases.