Fast ICA based technique for non-invasive fetal ECG extraction

Abstract

An electrocardiogram (ECG) plays an important role in the diagnosis process and providing information regarding heart diseases, monitoring ECG signal has high clinical significance. Out of 125 babies, 1 baby born with some form of congenital heart defect every year. So analysis and synthesis of fetal electrocardiograms (FECG) for disease detection is very important. The FECG is always contaminated by mother's ECG (MECG) so, extracting the clean FECG signal is very necessary for fetal health monitoring. In this study, FastICA algorithm with kurtosis and Negentropy as a measures of non-Gaussianity is discussed. Further the algorithm is proposed based on FastICA with maternal R-peak suppression approach which gives 90.35% extraction of fetal R-peaks when validated by directly taken scalp fetal ECG. The performance of proposed algorithm is evaluated on abdominal and direct fetal ECG database from PhysioNet site.