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Application of kernel PCA for foetal ECG estimation

Application of kernel PCA for foetal ECG estimation

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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.

References

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      • 3. Zheng, W., Hongxing, L., Jianchun, C.: ‘An adaptive filtering in phase space for fetal ECG estimation from an abdominal ECG signal and a thoracic ECG signal’, Signal Process., 2012, 6, (3), pp. 171177.
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