Re: code for estimation of correlation dimension

From: Jürgen Kahrs (Juergen.KahrsDELETETHIS_at_vr-web.de)
Date: 12/20/04 

Date: Mon, 20 Dec 2004 17:03:41 +0100

Lou Pecora wrote:

> Having said that, I suspect that you are not going to get anything much
> out of cardiac time series. Most biological data is too non-stationary
> and/or too high dimensional to do attractor reconstruction. In

What Lou Pecora says about biological time series
is definitely right.

Eric, before you go and analyze the data you should
also find out which kind of "pre-processing" has been
done on the data to "clean it up". Traditional measurement
devices often do some kind of analog or digital filtering
to emphasize those aspects of the signal that are interesting.
Nonrecursive filters are "smearing" the original signal,
making it hard to detect all dimensions. Recursive filters
can introduce additional dimensions that don't actually
exist in the signal but only in the device's filter.

> particular, I doubt the correlation dimension will be insightful or even
> meaningful. It's notoriously prone to errors from noise or
> non-stationarity.

I can confirm this from my own experience with analyzing data.
But I can also confirm that there are tons of publications
filled with "results" from analyzing this kind of data. For
example, there are textbooks (authored by established scientists)
which show how the correlation dimension of the EEG signal
is distributed over surface of the human head. If Eric does
his research mainly for advancing his position in a scientific
community, then he can safely ignore what I said here:
Algorithms for calculation of correlation dimension always
produce some output which is open to the most sophisticated
kind of interpretation.

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