Advancing non-invasive fetal health monitoring: A time–frequency approach to extracting fetal electrocardiogram signals

Abstract

The fetal electrocardiogram (FECG) provides an important way for clinicians to assess fetal health and to monitor pregnancy conditions. Although FECG can be captured through abdominal electrocardiogram (AECG) recordings from pregnant women, it often receives interference from maternal cardiac activity and other external sources. Particularly, as the high-amplitude maternal electrocardiogram (MECG) typically obscures the waveform of the FECG, isolating FECG from the AECG signals is challenging. Nevertheless, the differences in frequency patterns between MECG and FECG suggest a potential way for reconstructing FECG by combining the frequency and amplitude information. To this end, we proposed a novel method for separating FECG signals from the AECG via the time–frequency representation. We designed an encoder–decoder architecture based hierarchical multiscale-aware network to capture features embedded in both the time and frequency domains. Equipped with a holistic attention-aware module (HAA), the model extracts and consolidates fetal heart activity-related features in the time–frequency domain. Further introducingan attention gate module (AG) to emphasize key features during encoder-to-decoder transmission, the model enhances the quality and efficiency of FECG extraction without any redundancy in high-quality feature mapping. The model was trained using a hybrid time-domain and frequency-domain loss function and validated on two public databases. Excellent performances, such as an average Pearson correlation coefficient of 0.85 for estimating the quality of FECG signals, were observed demonstrating its excellent performance and providing a promising non-invasive method for monitoring fetal health. These findings indicate that the proposed method effectively captures the amplitude differences between fetal and maternal heartbeats across various frequency components within the AECG, leading to the extraction of high-quality FECG signals.