Assaleh Khaled
Department of Electrical Engineering, American University of Sharjah, P. O. Box 26666, UAE.
IEEE Trans Biomed Eng. 2007 Jan;54(1):59-68. doi: 10.1109/TBME.2006.883728.
In this paper, we investigate the use of adaptive neuro-fuzzy inference systems (ANFIS) for fetal electrocardiogram (FECG) extraction from two ECG signals recorded at the thoracic and abdominal areas of the mother's skin. The thoracic ECG is assumed to be almost completely maternal (MECG) while the abdominal ECG is considered to be composite as it contains both the mother's and the fetus' ECG signals. The maternal component in the abdominal ECG signal is a nonlinearly transformed version of the MECG. We use an ANFIS network to identify this nonlinear relationship, and to align the MECG signal with the maternal component in the abdominal ECG signal. Thus, we extract the FECG component by subtracting the aligned version of the MECG signal from the abdominal ECG signal. We validate our technique on both real and synthetic ECG signals. Our results demonstrate the effectiveness of the proposed technique in extracting the FECG component from abdominal signals of very low maternal to fetal signal-to-noise ratios. The results also show that the technique is capable of extracting the FECG even when it is totally embedded within the maternal QRS complex.
在本文中，我们研究了使用自适应神经模糊推理系统（ANFIS）从记录在母亲皮肤胸部和腹部区域的两个心电图信号中提取胎儿心电图（FECG）。假设胸部心电图几乎完全是母体心电图（MECG），而腹部心电图被认为是复合的，因为它包含母亲和胎儿的心电图信号。腹部心电图信号中的母体成分是MECG的非线性变换版本。我们使用ANFIS网络来识别这种非线性关系，并使MECG信号与腹部心电图信号中的母体成分对齐。因此，我们通过从腹部心电图信号中减去MECG信号的对齐版本来提取FECG成分。我们在真实和合成心电图信号上验证了我们的技术。我们的结果证明了所提出技术在从母体与胎儿信号噪声比非常低的腹部信号中提取FECG成分方面的有效性。结果还表明，即使FECG完全嵌入母体QRS复合波中，该技术也能够提取FECG。
