Single channel (univariate) signal - Practical Biomedical Signal Analysis Using MATLAB - page 74

data epochs are needed. In case of biomedical signals long stationary data segments
are often hard to find. Another problem is connected with the fact, that the non-linear
methods are prone to systematic errors connected with the arbitrary choices (e.g., of
the bin length in estimating probabilities, or lag of embedding procedure).
However, the biggest problem in application of non-linear methods is the fact that
they are very sensitive to noise. As was pointed out by [Kantz and Schreiber, 2000]
for a low-dimensional deterministic signal with a noise component of the order 2-
3%, it will be difficult to reasonably estimate correlation dimension, since in this
case a significant scaling region of C

( )

could not be found; the reason being that
the artifacts of the noise meet the artifacts of the overall shape of the attractor. Since
biomedical signals contain a significant noise component one should approach non-
linear methods with caution and apply them only in cases where the non-linearity of
a signal is well established and linear methods seem not to work properly.

ε