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Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/5745

Title: Inference of nonlinear state-space models for sandwich-type lateral flow immunoassay using extended Kalman filtering
Authors: Zeng, N
Wang, Z
Li, Y
Du, M
Liu, X
Keywords: Extended Kalman filtering (EKF)
Gold immunochromatographic strip
Lateral flow immunoassay
Mathematical model
Parameter estimation
Publication Date: 2011
Publisher: IEEE
Citation: IEEE Transactions on Biomedical Engineering, 58(7): 1959-1966, 2011
Abstract: In this paper, a mathematical model for sandwich-type lateral flow immunoassay is developed via short available time series. A nonlinear dynamic stochastic model is considered that consists of the biochemical reaction system equations and the observation equation. After specifying the model structure, we apply the extended Kalman filter (EKF) algorithm for identifying both the states and parameters of the nonlinear state-space model. It is shown that the EKF algorithm can accurately identify the parameters and also predict the system states in the nonlinear dynamic stochastic model through an iterative procedure by using a small number of observations. The identified mathematical model provides a powerful tool for testing the system hypotheses and also for inspecting the effects from various design parameters in both rapid and inexpensive way. Furthermore, by means of the established model, the dynamic changes in the concentration of antigens and antibodies can be predicted, thereby making it possible for us to analyze, optimize, and design the properties of lateral flow immunoassay devices.
URI: http://bura.brunel.ac.uk/handle/2438/5745
DOI: http://dx.doi.org/10.1109/TBME.2011.2106502
ISSN: 0018-9294
Replaces: http://bura.brunel.ac.uk/handle/2438/4726
2438/4726
Appears in Collections:Computer Science
Dept of Computer Science Research Papers

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