Key result
An adaptive linear neural network with a high learning rate and low momentum successfully isolated the fetal ECG signal from the maternal abdominal ECG.
An adaptive neural network approach can effectively extract fetal ECG signals from maternal abdominal ECGs, which may assist in non-invasive fetal monitoring.
May aid non-invasive fetal monitoring; leaves open prospective clinical validation before adoption.
FECG (Fetal ECG) signal contains potentially precise information that could assist clinicians in making more appro-priate and timely decisions during pregnancy and labor. The extraction and detection of the FECG signal from com-posite maternal abdominal signals with powerful and advance methodologies is becoming a very important requirement in fetal monitoring. The purpose of this paper is to illustrate the developed algorithms on FECG signal extraction from the abdominal ECG signal using Neural Network approach to provide efficient and effective ways of separating and understanding the FECG signal and its nature. The FECG signal was isolated from the abdominal signal by neural network approach with different learning constant value and momentum as well so that acceptable signal can be con-sidered. According to the output it can be said that the algorithm is working satisfactory on high learning rate and low momentum value. The method appears to be exceedingly robust, correctly isolate the FECG signal from abdominal ECG.
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Hasan et al. (2009) studied Fetal ECG extraction. Adaptive linear neural network (ADALINE) with Tapped Delay Line (TDL) was evaluated on Extraction of fetal ECG from maternal abdominal ECG. An adaptive linear neural network with a high learning rate and low momentum successfully isolated the fetal ECG signal from the maternal abdominal ECG.
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