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Distinguishability in EIT using a hypothesis-testing model Andy Adler1, Pascal Gaggero2 and Yasheng Maimaitijiang1
 

Summary: Distinguishability in EIT using a hypothesis-testing model
Andy Adler1, Pascal Gaggero2 and Yasheng Maimaitijiang1
1Carleton University, Ottawa, Canada
2Centre Suisse d'Electronique et de Microtechnique, Landquart, Switzerland
Abstract: In this paper we propose a novel formulation for the distinguishability
of conductivity targets in electrical impedance tomography (EIT). It is formulated
in terms of a classic hypothesis test to make it directly applicable to experimen-
tal configurations. We test to distinguish conductivity distributions 2 from 1,
from which EIT measurements are obtained with added white Gaussian noise with
covariance n. In order to distinguish the distributions, we must reject the null
hypothesis H0: ^x = 0, which has a probability based on the z-score: z = x
x
. This
result shows that distinguishability is a product of the impedance change amplitude,
the measurement strategy and the inverse of the noise amplitude. This approach is
used to explore different current stimulation strategies.
1 Introduction
Electrical impedance tomography (EIT) attempts to reconstruct the impedance distribution
within a body from electrical stimulation and measurement at a series of electrodes attached
to body surface. One key figure of merit for a given EIT system is its distinguishability, which

  

Source: Adler, Andy - Department of Systems and Computer Engineering, Carleton University

 

Collections: Computer Technologies and Information Sciences