A non-invasive fetal electrocardiogram (FECG) is used to monitor the electrical pulse of the fetal heart. Decomposing the FECG signal from the maternal ECG (MECG) is a blind source separation problem, which is hard due to the low amplitude of the FECG, the overlap of R waves, and the potential exposure to noise from different sources. Traditional decomposition techniques, such as adaptive filters, require tuning, alignment, or pre-configuration, such as modeling the noise or desired signal to map the MECG to the FECG. The high correlation between maternal and fetal ECG fragments decreases the performance of convolution layers. Therefore, the masking region of interest based on the attention mechanism was performed to improve the signal generators' precision. The sine activation function was also used to retain more details when converting two signal domains. Three available datasets from the Physionet, including the A&D FECG, NI-FECG, and NI-FECG challenge, and one synthetic dataset using FECGSYN toolbox, were used to evaluate the performance. The proposed method could map an abdominal MECG to a scalp FECG with an average of 98% R-Square [CI 95%: 97%, 99%] as the goodness of fit on the A&D FECG dataset. Moreover, it achieved 99.7% F1-score [CI 95%: 97.8-99.9], 99.6% F1-score [CI 95%: 98.2%, 99.9%] and 99.3% F1-score [CI 95%: 95.3%, 99.9%] for fetal QRS detection on the A&D FECG, NI-FECG and NI-FECG challenge datasets, respectively. Also, the distortion was in the “very good” and “good” ranges. These results are comparable to the state-of-the-art results; thus, the proposed algorithm has the potential to be used for high-performance signal-to-signal conversion.

中文翻译：

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使用基于注意力的 CycleGAN 从母体心电图中提取胎儿心电图


无创胎儿心电图（FECG）用于监测胎儿心脏的电脉冲。分解来自母体心电图 (MECG) 的 FECG 信号是一个盲源分离问题，由于 FECG 振幅低、R 波重叠以及可能暴露于不同来源的噪声，因此该问题很难实现。传统的分解技术（例如自适应滤波器）需要调整、对齐或预配置，例如对噪声或所需信号进行建模以将 MECG 映射到 FECG。母体和胎儿心电图片段之间的高度相关性降低了卷积层的性能。因此，基于注意力机制对感兴趣区域进行掩蔽，以提高信号发生器的精度。正弦激活函数还用于在转换两个信号域时保留更多细节。使用来自 Physionet 的三个可用数据集（包括 A&D FECG、NI-FECG 和 NI-FECG 挑战）以及使用 FECGSYN 工具箱的一个合成数据集来评估性能。所提出的方法可以将腹部 MECG 映射到头皮 FECG，平均 R 方 [CI 95%: 97%, 99%] 作为 A&D FECG 数据集的拟合优度。此外，它还实现了 99.7% F1 分数 [CI 95%: 97.8-99.9]、99.6% F1 分数 [CI 95%: 98.2%, 99.9%] 和 99.3% F1 分数 [CI 95%: 95.3%, 99.9] %] 分别用于 A&D FECG、NI-FECG 和 NI-FECG 挑战数据集上的胎儿 QRS 检测。此外，失真度处于“非常好”和“好”范围内。这些结果与最先进的结果相当；因此，所提出的算法有潜力用于高性能信号到信号转换。