Abstract
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.