A new method to extract the foetal electrocardiogram (ECG) using an abdominal ECG signal and a thoracic ECG signal of the mother is presented. The nonlinear mapping of the maternal ECG component from thoracic signal to an abdominal signal is approximated by the support vector regression (SVR) method. The foetal ECG signal is then extracted by subtracting the mapped thoracic signal from an abdominal signal. In an SVR algorithm, the nonlinear regression problem in an original signal space is transformed into a linear regression problem in high‐dimensional Hilbert space by kernel technology. It avoids the ‘overfitting’ problem and could obtain good results for the small sample training set. The validity of the proposed method is validated by the real ECG signals, and compared with the existing method.

A new method of estimating the antepartum foetal electrocardiogram (ECG) signal by kernel principal component analysis (PCA) is presented. For ECG signals collected from the body surface of the pregnant woman, the powerful maternal ECG is the most PC, compared with the foetal ECG and other noises. Utilising the correlation between the maternal components in different lead ECG signals, the maternal components can be removed from the abdominal signal to obtain the foetal ECG estimation. However, it shows a strong non‐linearity between the maternal components in every collected signal due to the diversity of propagation path. Kernel PCA can be seen as a non‐linear form of PCA which can extract the non‐linear PCs from multidimensional data. Thus, it can be applied to the multiple leads ECG signals to eliminate the maternal ECG components and estimate the foetal ECG signal precisely. The effectiveness of the proposed method is verified by the real data experiment and compared with the existing work.

This paper introduces an integrated approach to estimate the fetal heart rate from the abdominal Electrocardiogram (ECG) signal. Empirical mode decomposition (EMD) can decompose the fetal ECG signal into a set of intrinsic mode functions, which can be used as the indicator of the occurrence for the fetal heartbeats. The decomposition basis functions are directly derived from the fetal signal under test, which make the detection process robust and adaptive. Multiple signal classification (MUSIC) is a high resolution algorithm for frequency estimation, which can be applied to the fetal heartbeats indicator sequence output from the preceding EMD, estimating the fetal heart rate in frequency domain without the heartbeat wave detection. Compared with the popular Independent component analysis (ICA) method, the proposed method has shown improved robustness and fidelity in estimation of the fetal heart rate during testing with real fetal ECG database from DaISy and PhysioNet.