Application of kernel PCA for foetal ECG estimation

Application of kernel PCA for foetal ECG estimation

For access to this article, please select a purchase option:

Buy article PDF
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Your details
Why are you recommending this title?
Select reason:
Electronics Letters — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

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.


    1. 1)
    2. 2)
    3. 3)
      • 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.
    4. 4)
    5. 5)
    6. 6)
    7. 7)
      • 7. Mika, S., Scholkopf, B., Smola, A., et al: ‘Kernel PCA and de-noising in feature spaces’, Adv. Neural Inf. Process. Syst., 1999, 11, (1), pp. 536542.
    8. 8)
    9. 9)
    10. 10)

Related content

This article has following corresponding article(s):
This is a required field
Please enter a valid email address