Correlation-Aware Attention CycleGAN for Accurate Fetal ECG Extraction
Xu Wang, Zhaoshui He, Zhijie Lin, Yang Han, Wenqing Su, Shengli Xie
Abstract
The fetal electrocardiogram (FECG) is of great significance for fetal monitoring during peripartum and intrapartum. However, it is difficult to extract FECG signals from the abdominal signal due to the following issues: 1) FECG signals are always corrupted by noise; 2) The FECG signal is often masked by the high-amplitude maternal electrocardiogram (MECG). To address such problems, a Correlation-Aware Attention CycleGAN (CAA-CycleGAN) is proposed for FECG extraction, where the Auto-Correlation Attention Encoder (ACAE) module, which can capture waveform details of FECG signals by modeling its autocorrelation in the current convolution layer, is first devised to extract FECG signals corrupted by noise; then, the Cross-Correlation Attention Residual (CCAR) module, which can enhance the FECG components by learning its cross-correlation between adjacent convolutional layers, is developed to discriminate FECG signals from MECG signals; finally, the Dual Cross-Correlation Attention Decoder (DCCAD) module, which can extract waveform features of FECG signals by exploring its dual cross-correlation in different-level convolutional layers, is designed to extract FECG signals masked by MECG signals. Experiments demonstrate that the proposed CAA-CycleGAN can achieve excellent performance with the mean square error of 0.082, 0.024, and 0.032 on the FECGSYNDB, ADFECGDB, and B2 LABOUR datasets, respectively, which is more accurate than the state-of-the-art methods. The project is available at https://github.com/langdecc511/CAA-CycleGAN.