  This paper is an attempt to separate cardiac and respiratory signals from an
electrical bio-impedance (EBI) dataset. For this two well-known algorithms,
namely Principal Component Analysis (PCA) and Independent Component Analysis
(ICA), were used to accomplish the task. The ability of the PCA and the ICA
methods first reduces the dimension and attempt to separate the useful
components of the EBI, the cardiac and respiratory ones accordingly. It was
investigated with an assumption, that no motion artefacts are present. To carry
out this procedure the two channel complex EBI measurements were provided using
classical Kelvin type four electrode configurations for the each complex
channel. Thus four real signals were used as inputs for the PCA and fast ICA.
The results showed, that neither PCA nor ICA nor combination of them can not
accurately separate the components at least are used only two complex (four
real valued) input components.
