Department of Electrical Engineering and Computer Science, University of California Irvine, Irvine, CA 92697, USA.
Sensoriis, Inc., Edmonds, WA 98026, USA.
Sensors (Basel). 2022 Apr 5;22(7):2788. doi: 10.3390/s22072788.
Fetal electrocardiogram (fECG) assessment is essential throughout pregnancy to monitor the wellbeing and development of the fetus, and to possibly diagnose potential congenital heart defects. Due to the high noise incorporated in the abdominal ECG (aECG) signals, the extraction of fECG has been challenging. And it is even a lot more difficult for fECG extraction if only one channel of aECG is provided, i.e., in a compact patch device. In this paper, we propose a novel algorithm based on the Ensemble Kalman filter (EnKF) for non-invasive fECG extraction from a single-channel aECG signal. To assess the performance of the proposed algorithm, we used our own clinical data, obtained from a pilot study with 10 subjects each of 20 min recording, and data from the PhysioNet 2013 Challenge bank with labeled QRS complex annotations. The proposed methodology shows the average positive predictive value (PPV) of 97.59%, sensitivity (SE) of 96.91%, and F1-score of 97.25% from the PhysioNet 2013 Challenge bank. Our results also indicate that the proposed algorithm is reliable and effective, and it outperforms the recently proposed extended Kalman filter (EKF) based algorithm.
胎儿心电图（fECG）评估在整个怀孕期间至关重要，可用于监测胎儿的健康和发育情况，并可能诊断潜在的先天性心脏缺陷。由于腹部心电图（aECG）信号中包含很高的噪声，因此提取 fECG 一直具有挑战性。如果仅提供 aECG 的一个通道，即紧凑贴片设备中的一个通道，则提取 fECG 更加困难。在本文中，我们提出了一种基于集合卡尔曼滤波器（EnKF）的新算法，用于从单通道 aECG 信号中提取非侵入性 fECG。为了评估所提出算法的性能，我们使用了自己的临床数据，这些数据是从 10 名受试者（每名受试者记录 20 分钟）的试点研究中获得的，以及来自 PhysioNet 2013 挑战赛库的数据，其中带有标记的 QRS 复合体注释。所提出的方法在 PhysioNet 2013 挑战赛库中的平均阳性预测值（PPV）为 97.59%、灵敏度（SE）为 96.91%和 F1 得分为 97.25%。我们的结果还表明，所提出的算法可靠且有效，并且优于最近提出的基于扩展卡尔曼滤波器（EKF）的算法。
