Abstract: The use of fetal electrocardiogram (fECG) during pregnancy aids in the detection, examination, and tracking of a number of congenital cardiac diseases (CHDs). It is obtained through both invasive and non-invasive techniques, the latter is selected as it safer and convenient in use. The extraction of a clean fECG signal through a non-invasive method is essential because of various noises that interfere with it, such as: power line disturbances, baseline wander, motion artifact, uterine contraction, and high frequency noises. This study aims to provide a robust methodology for extracting clear and accurate fECG from abdominal ECG (aECG) that includes fECG, mECG, and noises. The proposed methodology consists of empirical mode decomposition (EMD) and wavelet decomposition (WD). EMD aids in the decomposition of non-stationary and non-linear signals such as ECG into intrinsic mode functions (IMFs) that use the signal as their basis. This facilitates the identification of the different frequency components seen in an ECG. The data matrix is created by combining selective IMFs with residuals, and then wavelet decomposition is applied to identify different frequency components in distinct sub-bands. mECG and fECG are identified from the sub-band reconstructed signals. The work has been validated on FECGSYNDB and ADFECG database from PhysioBank ATM and the DaISy database. After being post-processed by FIR filters, the signals are assessed to calculate the heart rate variability (HRV) of the clean, extracted fECG signals.

Abstract: Extraction of fetal ECG (fECG) signal is essential for monitoring the health of fetus during pregnancy and helps in early diagnosis of heart abnormalities, which leads to increased infant mortality rate and post-natal complications. In real scenarios, extraction of clear fECG is challenging due to maternal ECG (mECG) and other contaminated noise (such as: baseline wander and high frequency noise). This paper is focused on design, implementation, and verification of a robust approach for fECG extraction, recorded by non-invasive procedure from the pregnant women, using empirical mode decomposition (EMD), independent component analysis (ICA), and FIR filtering. The combined EMD and ICA approach are found suitable for effective extraction in real and synthetic data. EMD separates the non-stationary and non-linear time varying signals like ECG into various modes, having high to low frequencies using signal itself as a basis. The coefficients obtained during this decomposition are called intrinsic mode functions (IMFs) representing various frequency components. Different number of IMFs are combined with the residuals to create the data matrix (or mixed signals), which are fed to the ICA (extended efficient Fast-ICA and multi-combi ICA) for separating the independent components (ICs) due to their strength in separating the combination of various distribution signals. These extracted ICs (such as: thorax ECG, fECG, and noises etc.,) are subjected to FIR filtering to obtain the fECG and its corresponding heart rate (HR). This technique is validated on simulated signals for separation, prior to applying on fECG synthetic-data and aECG-data collected from PhysioBank ATM. The performance of ICA algorithm is evaluated by API.