Fetal ECG Extraction Using Nonlinear Noise Reduction and Blind Source Separation

Abstract

The fetal electrocardiogram contains within it, information regarding the health of the fetus. Currently, fetal ECG is recorded directly from the scalp of the baby during labour. However, it has been shown that fetal ECG can also be measured using surface electrodes attached to a pregnant mother's abdomen. The advantage of this method lies in the fact that fetal ECG can be measured noninvasively before the onset of labour. The difficulty lies in isolating the fetal ECG from extraneous signals that are simultaneously recorded with it. Several signal processing methodologies have been put forth in order to extract the fetal ECG component from a mixture of signals. Two recent techniques that have been put forth include a scheme that has previously been used to nonlinearly reduce noise in deterministically chaotic noise and the other uses a blind source separation technique called independent component analysis. In this thesis, we describe the significance of the fetal electrocardiogram as a diagnostic tool in medicine, a brief overview of the theory behind the nonlinear noise reduction technique and blind source separation, and results from having processed synthetic and real data using both techniques. We find that although the noise reduction technique performs adequately, the blind source separation process performs faster and more robustly against similar data. The two techniques can be used in tandem to arrive at an approximate fetal ECG signal, which can be further analyzed by calculating, for example, the fetal heart rate.