Correlation-Aware Attention CycleGAN for Accurate Fetal ECG Extraction

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 (ADS) due to the following issues: 1) FECG signals are always corrupted by noise and 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 autocorrelation 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 (MSE) 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.

I. Introduction

Worldwide, there are 3 million stillbirths that occur yearly, of which heart disease is one of the leading causes of fetal death [1]. To obtain the relevant information on fetal heart activity, a large number of studies on fetal monitoring have been reported [2], [3], [4], and many electronic devices have also been applied for fetal clinical diagnosis, for example, cardiotocography (CTG) [5], Doppler ultrasound [6], and fetal magnetocardiography (FMCG) [7]. However, these devices show low sensitivity (SEN) in acquiring fetal heart activities [5]. Therefore, it is essential to design an effective tool to provide reliable information that reflects fetal cardiac activity.