Nonlinear state estimation using the Unscented Kalman filter in electrical impedance tomography.

Author(s)

Fernando Silva de Moura

• Document type: Doctoral Thesis
• Press: São Paulo.
• Institution: Universidade de São Paulo (USP). Escola Politécnica (EP/BC)
• Defense date: 2013-02-26

Examining board members

Raúl González Lima; Marcelo Britto Passos Amato; Jari Kaipio; Helcio Rangel Barreto Orlande; Clodoaldo Grotta Ragazzo

• Advisor: Raúl González Lima; Jari Kaipio

Abstract

Electrical impedance tomography estimates the electrical impedance distribution within a region given a set of electrical potential measurements acquired along its boundary at the same time that electrical currents are imposed on the same boundary. One of the applications of this technology is lung monitoring of patients in Intensive Care Units. One class of algorithms employed for the estimation are the Kalman filters which deal with the estimation problem in a probabilistic framework, looking for the probability density function of the state conditioned to the acquired measurements. In order to use such filters, an evolution models of the system must be employed. This thesis proposes an evolution model of the variation of air in the lungs of patients under artificial ventilation. This model is used on the Unscented Kalman Filter, a nonlinear extension of the Kalman filter. This model is adjusted in parallel to the state estimation, in a dual estimation scheme. An image segmentation algorithm is proposed for identifying the lungs in the images. In order to improve the estimate, the approximation error method is employed for mitigating the observation model errors and prior information is added for the solution of the ill-posed inverse problem. The method is evaluated with numerical simulations and with experimental data of a volunteer. The results show that the proposed method increases the quality of the estimates, allowing the visualization of absolute and dynamic images, with good level of contrast between the tissues and internal organs. (AU)