G. Vasios, A. Prentza, D. Blana, E. Salamalekis, and P. Thomopoulos, “Classification of fetal heart rate tracings based on wavelet transform and self organizing map neu-ral networks,” Proceedings of the 23rd Annual Interna-tional conference of the IEEE, Istanbul, Turkey, Vol. 2, pp. 1633–1636, 2001.

has been cited by the following article:

• TITLE: Fetal ECG Extraction from Maternal Abdominal ECG Using Neural Network

  AUTHORS: M. A. HASAN, M. I. IBRAHIMY, M. B. I. REAZ

  KEYWORDS: Neural Network, FECG, Abdominal ECG, Heart Rate

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.2 No.5, December 28, 2009

  ABSTRACT: 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.