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In Blind Source Separation (BSS), one estimates sources from data mixtureswhere the mixing coefficients are unknown. In the particular case of SparseComponent Analysis (SCA), each underlying source exists for only a finiteamount of time when other sources are negligible. In this paper, one approachto SCA is presented where the data are represented using phase space analysisand one estimates the main source from the maximum in the phase plot. Deflationis used to estimate the other sources. The proposed method is tested onsimulated data and experimental ECG data taken from an expectant mother. It isshown that, in most cases, the performance of the proposed method is comparableto that of Principal Component Analysis (PCA) and FastICA for clean data. Inthe 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 downas expected, as the maximum in the phase plot does not correspond to anindividual source.

Estimating Sparse Sources from Data Mixtures using Maxima in Phase Space Plots