Blind separation of fetal ECG from single mixture using SVD and ICA

In this paper, we propose a novel blind-source separation method to extract fetal ECG from a single-channel signal measured on the abdomen of the mother. The signal is a mixture of the fetal ECG, the maternal ECG and noise. The key idea is to project the signal into higher dimensions, and then use an assumption of statistical independence between the components to separate them from the mixtures. This is achieved by applying singular value decomposition (SVD) on the spectrogram, followed by an iterated application of independent component analysis (ICA) on the principle components. The SVD contributes to the separability of each component and the ICA contributes to the independence of the two components. We further refine and adapt the above general idea to ECG by exploiting a-prior knowledge of the maternal ECG frequency distribution and other characteristics of ECG. Experimental studies show that the proposed method is more accurate than using SVD only. Because our method does not exploit extensive domain knowledge of the ECGs, the idea of combining SVD and ICA in this way can be applied to other blind separation problems.