  In Blind Source Separation (BSS), one estimates sources from data mixtures
where the mixing coefficients are unknown. In the particular case of Sparse
Component Analysis (SCA), each underlying source exists for only a finite
amount of time when other sources are negligible. In this paper, one approach
to SCA is presented where the data are represented using phase space analysis
and one estimates the main source from the maximum in the phase plot. Deflation
is used to estimate the other sources. The proposed method is tested on
simulated data and experimental ECG data taken from an expectant mother. It is
shown that, in most cases, the performance of the proposed method is comparable
to that of Principal Component Analysis (PCA) and FastICA for clean data. In
the case of noisy data, PCA is found to be more robust for higher noise levels.
For situations where the sources have coincident peaks, the method breaks down
as expected, as the maximum in the phase plot does not correspond to an
individual source.
