Degenerate Unmixing Estimation Technique (DUET) for Fetal ECG Blind Source Separation

Abstract

Monitoring the health of fetus at early stage is very crucial. One of the non-invasive ways is by evaluating the pattern of electrocardiogram (ECG) signals of mother’s abdomen and thorax. As these raw signals are mixed signals that consist of mainly maternal ECG (MECG) and fetal ECG (FECG), an effective extraction method of FECG from the mixed signals is imperative. Fast Independent Component Analysis (FastICA) is one of the common signal processing algorithms for blind source separation (BSS). However, it works only for even-determined case when the number of sources (MECG and FECG) is equal to the number of mixtures (two mixed signals i.e. from the mother’s abdomen and the mother’s thorax). For the underdetermined case in which the number of sources would be more than the number of mixture (for the case of twins or triplets’ pregnancy), FastICA algorithm fails as the computation of the inverse mixing matrix of ICA is theoritically impossible. Thus, Degenerate Unmixing Estimation Technique (DUET) algorithm which is based on signal recovery sparsity algorithm is implemented in this research so as to discover its feasibility to solve both even-determined and underdetermined cases. From the experimental results, the DUET performance is promising. Although for even-determined case, FastICA is better than DUET performance, DUET is proved to be valuable in solving the underdetermined case problem, yet the low FECG signal to noise ratio (SNR) value is observed reflecting the high interference.