A dynamic oppositional biogeography-based optimization approach for time-varying electrical impedance tomography. (20th May 2016)

• Record Type: Journal Article
• Authors: Rashid, A; Kim, S; Liu, D; Kim, K Y
• Is Part Of: Physiological measurement. Volume 37:Number 6(2016:Jun.)
• Page Start: 820
• Page End: 842
• Publication Date: 2016-05-20
• Subjects: electrical impedance tomography -- boundary estimation -- dynamic optimization -- inverse problem -- dynamic evolutionary algorithms -- biogeography-based optimization; Physiology -- Measurement -- Periodicals; Patient monitoring -- Periodicals; 612
• DOI: 10.1088/0967-3334/37/6/820
• Languages: English
• ISSNs: 0967-3334

Abstract

Abstract: Dynamic electrical impedance tomography-based image reconstruction using conventional algorithms such as the extended Kalman filter often exhibits inferior performance due to the presence of measurement noise, the inherent ill-posed nature of the problem and its critical dependence on the selection of the initial guess as well as the state evolution model. Moreover, many of these conventional algorithms require the calculation of a Jacobian matrix. This paper proposes a dynamic oppositional biogeography-based optimization (OBBO) technique to estimate the shape, size and location of the non-stationary region boundaries, expressed as coefficients of truncated Fourier series, inside an object domain using electrical impedance tomography. The conductivity of the object domain is assumed to be known a priori . Dynamic OBBO is a novel addition to the family of dynamic evolutionary algorithms. Moreover, it is the first such study on the application of dynamic evolutionary algorithms for dynamic electrical impedance tomography-based image reconstruction. The performance of the algorithm is tested through numerical simulations and experimental study and is compared with state-of-the-art gradient-based extended Kalman filter. The dynamic OBBO is shown to be far superior compared to the extended Kalman filter. It is found to be robust to measurement noise as well as the initial guess, and does not rely on a priori knowledge of the state evolution model.