Comparing higher order statistics of three ICA methods in wavelets-based single-channel fetal ECG extraction

Independent component analysis (ICA) is an effective technique for separation of statistically independent sources. Generally, ICA requires that the number of sensors must be no less than the number of independent sources to ensure enough information for separation of all sources. In some practical applications, this requirement of ICA is not met and we are interested in separation of only one source. A new method called wavelet-ICA filter is proposed that attempts to extract the fetal ECG from the single-channel and 2-channels mixture of fetal and maternal ECG then Higher order statistics of extracted fetal ECG compared with multi-channel independent component analysis results. The method employs ICA to regularize the wavelet decompositions of a signal to find the independent feature. Morlet wavelet is employed in this application for its nonorthogonality.