A Dual Attention-Based Autoencoder Model for Fetal ECG Extraction From Abdominal Signals

Fetal electrocardiogram (FECG) signals contain important information about the conditions of the fetus during pregnancy. Currently, pure FECG signals can only be obtained through an invasive acquisition process, which is life threatening to both mother and fetus. In this study, single-channel ECG signals from the mother's abdomen are analyzed with the aim of extracting the clean FECG waveform. This is a challenging task due to the very low amplitude of the FECG, various noises involved in the signal acquisition, and the overlap of R waves. To address this problem, we propose a novel convolutional autoencoder (AE) network architecture to learn and extract the FECG patterns. The proposed model is equipped with a dual attention mechanism, composed of squeeze-and-excitation and channel-wise (CW) modules, in the encoder and decoder blocks, respectively. It also benefits from a bidirectional long short-term memory (LSTM) layer. This unique combination allows the proposed network to accurately attend to and extract the FECG signals from abdominal data. Three well-established datasets are considered in our experiments. The obtained results of FECG extraction are promising and confirm the effectiveness of using attention modules within the deep learning model. The results also suggest that the proposed AE network can accurately extract the FECG signals where no information about maternal ECG (MECG) is available.

Automated summary

Fetal electrocardiogram (FECG) signals contain important information about the conditions of the fetus during pregnancy. This study proposes a novel network architecture that combines attention mechanisms and deep learning models to accurately extract FECG signals from abdominal data.