Abstract：
BACKGROUND: The utilization of non-invasive techniques for fetal cardiac health surveillance is pivotal in evaluating fetal well-being throughout the gestational period. This process requires clean and interpretable fetal Electrocardiogram (fECG) signals.
METHODS: The proposed work is the novel framework for the elicitation of fECG signals from abdominal ECG (aECG) recordings of the pregnant mother. The comprehensive approach encompasses pre-processing of the raw ECG signal, Blind Source Separation techniques (BSS), Decomposition techniques like Empirical Mode Decomposition (EMD), and its variants like Ensemble Empirical Mode Decomposition (EEMD), and Complete Ensemble Empirical Mode Decomposition with Additive Noise (CEEMDAN). The Robust Set Membership Affine Projection (RSMAP) Algorithm is deployed for the enhancement of the obtained fECG signal.
RESULTS: The results show significant improvements in the elicited fECG signal with a maximum Signal Noise Ratio (SNR) of 31.72 dB and correlation coefficient = 0.899, Maximum Heart Rate(MHR) obtained in the range of 108-142 bpm for all the records of abdominal ECG signals. The statistical test gave a p-value of 0.21 accepting the null hypothesis. The Abdominal and Direct Fetal Electrocardiogram Database (ABDFECGDB) from PhysioNet has been used for this analysis.
CONCLUSIONS: The proposed framework demonstrates a robust and effective method for the elicitation and enhancement of fECG signals from the abdominal recordings.
METHODS: The proposed work is the novel framework for the elicitation of fECG signals from abdominal ECG (aECG) recordings of the pregnant mother. The comprehensive approach encompasses pre-processing of the raw ECG signal, Blind Source Separation techniques (BSS), Decomposition techniques like Empirical Mode Decomposition (EMD), and its variants like Ensemble Empirical Mode Decomposition (EEMD), and Complete Ensemble Empirical Mode Decomposition with Additive Noise (CEEMDAN). The Robust Set Membership Affine Projection (RSMAP) Algorithm is deployed for the enhancement of the obtained fECG signal.
RESULTS: The results show significant improvements in the elicited fECG signal with a maximum Signal Noise Ratio (SNR) of 31.72 dB and correlation coefficient = 0.899, Maximum Heart Rate(MHR) obtained in the range of 108-142 bpm for all the records of abdominal ECG signals. The statistical test gave a p-value of 0.21 accepting the null hypothesis. The Abdominal and Direct Fetal Electrocardiogram Database (ABDFECGDB) from PhysioNet has been used for this analysis.
CONCLUSIONS: The proposed framework demonstrates a robust and effective method for the elicitation and enhancement of fECG signals from the abdominal recordings.
摘要：
背景：使用非侵入性技术进行胎儿心脏健康监测对于评估整个妊娠期的胎儿健康状况至关重要。该过程需要清洁且可解释的胎儿心电图(fECG)信号。
方法：所提出的工作是一种新颖的框架，用于从怀孕母亲的腹部ECG（aECG）记录中引出fECG信号。全面的方法包括对原始ECG信号进行预处理，盲源分离技术(BSS)，分解技术，如经验模式分解(EMD)，及其变体，如集合经验模式分解(EEMD)，具有加性噪声的完整集合经验模式分解(CEEMDAN)。稳健集成员仿射投影(RSMAP)算法被部署用于增强所获得的fECG信号。
结果：结果表明，所引发的fECG信号的显着改善，最大信噪比（SNR）为31.72dB，相关系数=0.899，最大心率（MHR）在108-142bpm范围内获得腹部ECG信号的所有记录。统计检验给出的p值为0.21，接受零假设。来自PhysioNet的腹部和直接胎儿心电图数据库（ABDFECGDB）已用于此分析。
结论：所提出的框架证明了一种用于从腹部记录中激发和增强fECG信号的鲁棒有效方法。
方法：所提出的工作是一种新颖的框架，用于从怀孕母亲的腹部ECG（aECG）记录中引出fECG信号。全面的方法包括对原始ECG信号进行预处理，盲源分离技术(BSS)，分解技术，如经验模式分解(EMD)，及其变体，如集合经验模式分解(EEMD)，具有加性噪声的完整集合经验模式分解(CEEMDAN)。稳健集成员仿射投影(RSMAP)算法被部署用于增强所获得的fECG信号。
结果：结果表明，所引发的fECG信号的显着改善，最大信噪比（SNR）为31.72dB，相关系数=0.899，最大心率（MHR）在108-142bpm范围内获得腹部ECG信号的所有记录。统计检验给出的p值为0.21，接受零假设。来自PhysioNet的腹部和直接胎儿心电图数据库（ABDFECGDB）已用于此分析。
结论：所提出的框架证明了一种用于从腹部记录中激发和增强fECG信号的鲁棒有效方法。
