Foetal ECG extraction using BPN and UWT
by R. Parameshwari; C. Emlyn Gloria Ponrani; S. Shenbaga Devi
International Journal of Biomedical Engineering and Technology (IJBET), Vol. 22, No. 1, 2016

Abstract: Foetal electrocardiogram (FECG) signal could assist the clinicians in making appropriate and timely decisions during pregnancy and labour since it contains information about the status of the foetus. The extraction and detection of the FECG signal from the composite abdominal signals of the mother with powerful and advance methodology is becoming a very important requirement in foetal monitoring. This paper illustrates the algorithm developed based on neural network approach to provide efficient and effective way of separating FECG signal from the abdominal ECG (AECG) signals. The FECG signal is isolated from the abdominal signal by back propagation neural network approach. The input signal is considered as maternal ECG (MECG) obtained from the thorax of the mother, and the target signal is abdominal signal (AECG) which has both MECG and FECG. The output of the network is subtracted from the target input (AECG). To reduce the difference between the input and target signal the weights have been updated every step, so that acceptable FECG signal can be obtained. To enhance the FECG signal, post-processing steps are followed which involve undecimated wavelet transform (UWT), thresholding and filtering technique.

Online publication date: Thu, 08-Sep-2016

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Biomedical Engineering and Technology (IJBET):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com