Key result
An algorithm based on a variation of independent component analysis using time delay from autocorrelation analysis effectively removed maternal fMCG signals and was computationally efficient.
A novel algorithm based on dependent component analysis effectively extracts fetal magnetocardiogram signals from maternal interference with high computational efficiency.
May aid noninvasive fetal cardiac monitoring; leaves open prospective validation before clinical adoption.
Fetal magnetocardiography (fMCG) has been extensively reported in the literature as a non-invasive, prenatal technique that can be used to monitor various functions of the fetal heart. However, fMCG signals often have low signal-to-noise ratio (SNR) and are contaminated by strong interference from the mother's magnetocardiogram signal. A promising, efficient tool for extracting signals, even under low SNR conditions, is blind source separation (BSS), or independent component analysis (ICA). Herein we propose an algorithm based on a variation of ICA, where the signal of interest is extracted using a time delay obtained from an autocorrelation analysis. We model the system using autoregression, and identify the signal component of interest from the poles of the autocorrelation function. We show that the method is effective in removing the maternal signal, and is computationally efficient. We also compare our results to more established ICA methods, such as FastICA.
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Araújo et al. (2005) studied Fetal magnetocardiography (fMCG) signal extraction. Algorithm based on a variation of independent component analysis (ICA) using time delay from autocorrelation analysis vs. Established ICA methods (e.g., FastICA) was evaluated on Removal of maternal signal and computational efficiency. An algorithm based on a variation of independent component analysis using time delay from autocorrelation analysis effectively removed maternal fMCG signals and was computationally efficient.
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