Fetal ECG Signal Enhancement

Abstract

Fetal heart monitoring yields vital information about the fetus health and can support medical decision making in critical situations. A compound signal is obtained non-invasively by placing electrodes on the abdomen area of the mother which contains maternal and fetal ECG signals contaminated by various other signals from body and externally induced noises. The Polynomial Networks technique has been exploited to isolate fetal electrocardiogram (FECG) from the undesired mapped maternal electrocardiogram (mapped MECG). Wavelet transform has been used as a post processing tool to de-noise the extracted FECG. This thesis addresses the enhancements achievable by the application of wavelet transform to FECG signals extracted by polynomial networks. Processing of both real and synthetic ECG data have been examined with proposed pre and post wavelet de-noising algorithms. Test results show improved extraction performance and successful removal of baseline wandering. Numerical results on signal-to-noise ratio for synthetic data are presented and results compared with various configurations of processing blocks. The characteristics of the FECG signal were shown to be preserved and a relatively clean FECG signal is obtained.