NDLI: Adaptive linear neural network filter for fetal ECG extraction

Content Provider	IEEE Xplore Digital Library
Author	Mamun Bin Ibne Reaz Lee Sze Wei
Copyright Year	2004
Description	Author affiliation: Fac. of Eng., Multimedia Univ., Selangor, Malaysia (Mamun Bin Ibne Reaz; Lee Sze Wei)
Abstract	This paper describes an adaptive method to separate fetal ECG from composite ECG that consists of both maternal and fetal ECGs by using ADALINE (Adaptive Linear Network). The fetal signal is weak under the domination of maternal signal and other noises. The network emulate maternal signal as closely as possible to abdominal signal thus only predict the maternal ECG in the abdominal ECG. The network error equals abdominal ECG minus maternal ECG, which is the fetal ECG. The characteristic that enables fetal extraction is due to correlation between maternal ECG signals with the abdominal ECG signal of pregnant woman. The network adjusts accordingly to preserve the original signal while eliminating the noises. A GUI program is written in Matlab to detect the changes in extracted fetal ECG by different values of momentum, learning rate and initial weights used in the network. It is found that filtering performs best by high learning rate, low momentum, and small initial weights.
Sponsorship	Faculty of Eng., Univ. of Melbourne IEEE Madras Section, Chennai
Starting Page	321
Ending Page	324
File Size	959660
Page Count	4
File Format	PDF
ISBN	0780382439
DOI	10.1109/ICISIP.2004.1287675
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-01-04
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Pregnancy Adaptive systems Filtering Neural networks Adaptive filters Nonlinear filters Electrocardiography Artificial intelligence Abdomen Noise level
Content Type	Text
Resource Type	Article

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