基于单通道干布线电极的非侵入性胎儿心电图提取的复数 UNet 方法

连续、非侵入式的妊娠监测对于减少潜在并发症至关重要。胎儿心电图（fECG）代表了一种评估胎儿健康的有前景的工具，可在临床环境之外进行。家庭监测需要使用最少数量舒适且耐用的电极，例如干布线电极。然而，这一设置面临诸多挑战，包括增加的噪声和运动伪迹，这些因素 complicate the accurate extraction of fECG signals. 为克服这些挑战，我们引入了一种从单通道干布线电极记录中提取fECG的 pioneering 方法。我们通过模拟腹部记录，包括密切 resemble real-world characteristics of in-vivo recordings through dry textile electrodes, alongside mECG and fECG, created a new dataset. To ensure the reliability of the extracted fECG, we propose an innovative pipeline based on a complex-valued denoising network, Complex UNet. Unlike previous approaches that focused solely on signal magnitude, our method processes both real and imaginary components of the spectrogram, addressing phase information and preventing incongruous predictions. We evaluated our novel pipeline against traditional, well-established approaches, on both simulated and real data in terms of fECG extraction and R-peak detection. The results showcase that our suggested method achieves new state-of-the-art results, enabling an accurate extraction of fECG morphology across all evaluated settings. This method is the first to effectively extract fECG signals from single-channel recordings using dry textile electrodes, making a significant advancement towards a fully non-invasive and self-administered fECG extraction solution.