Fetal ECG Signal Extraction From Long-Term Abdominal Recordings Based on Adaptive QRS Removal and Joint Blind Source Separation
Fetal ECG Signal Extraction From Long-Term Abdominal Recordings Based on Adaptive QRS Removal and Joint Blind Source Separation
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DOI：
10.1109/jsen.2022.3206225
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发表时间：
2022-11
影响因子：
4.3
通讯作者：
Lu Wang;Chunhui Zhao;M. Dong;K. Ota
中科院分区：
文献类型：
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作者：
Lu Wang;Chunhui Zhao;M. Dong;K. Ota
尽管可穿戴电子设备具有巨大的潜在应用，但记录质量还不能充分满足胎儿疾病检测的要求。在这篇文章中，我们的目标是提出一种新的框架胎儿心电图（ECG）提取，其中的混合方法主要考虑了一种新的自适应母体QRS波消除（AQRSR）算法和基于张量的联合盲源分离（JBSS）方法的组合。为了适应心电信号的动态变化，抑制孕妇强有力的心跳，采用自适应模板进行AQRSR，该模板由发生器、滤波器和Transformer组成。通过用最新的周期替换最旧的分割，该方法能够快速适应估计的输出以匹配新的输入。此外，由于目标信号与噪声之间存在显著的交叉，即使实施母体ECG去除处理，残留信号也不可避免地包含胎儿ECG、母体ECG的一部分和噪声。因此，一种新的JBSS，采用张量分解制定分离胎儿心跳从长期记录污染与母亲的动作噪声和心跳干扰。通过将采集的数据划分为多个段，该方法可以通过利用不仅在每个段内统计独立而且可以依赖于不同段的信号来提取胎儿ECG信号。实验结果表明，即使记录的信号被噪声污染，所提出的框架具有相当好的胎儿心电图提取性能。
Despite wearable electronic devices having enormous potential applications, the recording quality has not sufficiently met the requirements for fetal disease detection. In this article, we aim to propose a novel framework for fetal electrocardiography (ECG) extraction, where the hybrid approach mainly considers the combination of a novel adaptive maternal QRS removal (AQRSR) algorithm and a tensor-based joint blind source separation (JBSS) approach. To adapt to the dynamic change of the ECG signal and suppress the powerful maternal heartbeat, AQRSR is explored with an adaptive template, which is generated by a model consisting of a generator, discriminator, and transformer. By replacing the oldest segmentation with the newest cycle, the approach is capable of quickly adapting to the estimated output to match the new input. In addition, due to the significant crossover between the target signal and the noise, even if the maternal ECG removal processing is implemented, the residual signal inevitably contains fetal ECG, part of maternal ECG, and noise. As a consequence of this, a novel JBSS that incorporates tensor decomposition is formulated to separate fetal heartbeat from long-term recordings contaminated with maternal movements noise and heartbeat interference. By dividing the collected data into multiple segments, this method can extract fetal ECG signals by exploiting the signals that are not only statistically independent within each segment but can be dependent on different segments. Experimental results show that even if the recorded signals are contaminated by the noise, the proposed framework has comparable better performance on fetal ECG extraction.