基于渐进式周期性源剥离的母体与胎儿心电图无创提取
Noninvasive Extraction of Maternal and Fetal Electrocardiograms Using Progressive Periodic Source Peel-off
AI总结：
针对AECG中提取微弱fECG的挑战，本研究提出含PCFICA、奇异值分解和剥离策略的PPSP方法，在多类数据上表现优于现有方法，有效支持围产期心脏活动无创监测。
摘要：
腹部心电图（AECG）通过体表电极提供了一种安全无创的孕期胎儿健康监测方式。然而，从包含更强母体心电图（mECG）和外部噪声的AECG记录中提取微弱的胎儿心电图（fECG）具有挑战性。本研究提出一种新型渐进式周期性源剥离（PPSP）方法，用于从多通道AECG记录中提取周期性心电图源，包含三个主要模块：1）带心电图生理学约束的周期性约束快速独立成分分析（PCFICA）模块，用于提取精确的心电尖峰序列；2）用于估计心电波形的奇异值分解模块；3）通过消除已分离源或噪声来识别微弱fECG源的剥离策略。PPSP方法的性能在两个公共数据库、合成数据和我们的临床数据上进行了检验。在提取fECG尖峰序列方面，我们的PPSP方法在公共数据上的F1分数为99.59%，在最高噪声水平的合成数据上为99.50%；在临床数据上进一步获得了最低的胎儿心率均方根误差（RMSE）6.20%。它在所有数据集上的表现均显著优于其他最先进方法（p < 0.05）。本研究证明了PPSP方法在提取和分离mECG与微弱fECG信号方面的有效性，尤其在高噪声水平下具有高精度，为围产期医学中胎儿和母体心脏活动的无创测量与智能监测提供了支持。
英文摘要：
Abdominal electrocardiogram (AECG) gives a safe and non-invasive way to monitor fetal well-being during pregnancy using surface electrodes. However, it is challenging to extract weak fetal ECG (fECG) from the AECG recordings with larger maternal ECG (mECG) and external noises. In this study, we introduce a novel progressive periodic source peel-off (PPSP) method for extracting periodic ECG sources from multi-channel AECG recordings, including three main modules: 1) A periodic constrained FastICA (PCFICA) module with ECG physiology-informed constraints for extracting precise ECG spike trains, 2) A singular value decomposition module for estimating ECG waveforms, and 3) A peel-off strategy that facilitates to discern weak fECG source by eliminating previously separated sources or noises. The performance of the PPSP method was examined on two public databases, synthetic data and our clinical data. For extracting fECG spike trains, our PPSP method achieved an F1-scores of 99.59% on public data, 99.50% on synthetic data at the highest noise level. It further yielded the lowest RMSE of fetal heart rate of 6.20% on clinical data. It significantly outperformed other state-of-the-art methods on any set of data (p < 0.05). This study demonstrated effectiveness of the PPSP method for extracting and separating mECG and weak fECG signals, with high precision especially at high noise levels. Our study promotes noninvasive measurement and intelligent monitoring of both fetal and maternal heart activities towards advanced healthcare in perinatal medicine.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
