Summary: A measure of the information content of EIT data
, Richard Youmaran2
, William R.B. Lionheart3
Systems and Computer Engineering, Carleton University, Ottawa, Canada
School of Information Technology and Engineering, University of Ottawa, Canada
School of Mathematics, University of Manchester, U.K.
Abstract. We ask: how many bits of information (in the Shannon sense) do we
get from a set of EIT measurements? Here, the term information in measurements
(IM) is defined as: the decrease in uncertainty about the contents of a medium, due
to a set of measurements. This decrease in uncertainly is quantified by the change
from the the inter-class model, q, defined by the prior information, to the intra-class
model, p, given by the measured data (corrupted by noise). IM is measured by the
expected relative entropy (Kullback-Leibler divergence) between distributions q and
p, and corresponds to the channel capacity in an analogous communications system.
Based on a Gaussian model of the measurement noise, n, and a prior model of the