TCGAN: Temporal Convolutional Generative Adversarial Network for Fetal ECG Extraction Using Single-Channel Abdominal ECG

Noninvasive fetal ECG (FECG) monitoring holds significant importance in ensuring the normal development of the fetus. Since FECG is usually submerged by maternal ECG (MECG) and background noise in abdominal ECG (AECG), it is challenging to exactly restore the waveform details of FECG from AECG. To address this issue, a temporal convolutional generative adversarial network (TCGAN) is proposed for FECG extraction using single-channel AECG. In order to utilize both the global and local ECG features in time domain, we built an encoder-decoder architecture for designing generator. The model architecture consists of temporal convolution blocks, transpose convolutions and skip connections. The skip connections attempt to achieve the purpose of amalgamating information from feature maps extracted by convolutional layers using transpose convolution operations, which facilitates the decoder for extracting more detail information. TCGAN is rigorously evaluated using both synthetic dataset FECGSYDB and real-world dataset ADFECGDB. The experimental results on above datasets demonstrate the outstanding performance of TCGAN in terms of fetal QRS complex detection, achieving PPV of 99.54% and 99.02%, respectively. Comparing with the state-of-the-art methods, TCGAN could extract FECG with well-preserved waveform details. This helps doctors achieve more accurate assessment of fetal development.