Adaptive linear neural network filter for fetal ECG extraction

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.