Inference of Nonlinear State-Space Models for Sandwich-Type Lateral Flow Immunoassay Using Extended Kalman Filtering
Inference of Nonlinear State-Space Models for Sandwich-Type Lateral Flow Immunoassay Using Extended Kalman Filtering
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DOI：
10.1109/tbme.2011.2106502
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发表时间：
2011-01
影响因子：
4.6
通讯作者：
Nianyin Zeng;Zidong Wang;Yurong Li;Min Du;Xiaohui Liu
中科院分区：
文献类型：
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作者：
Nianyin Zeng;Zidong Wang;Yurong Li;Min Du;Xiaohui Liu
本文建立了利用短有效时间序列进行三明治型横向流动免疫分析的数学模型。考虑一个由生化反应系统方程和观测方程组成的非线性动态随机模型。在确定模型结构后，应用扩展卡尔曼滤波（EKF）算法对非线性状态空间模型的状态和参数进行识别。结果表明，在非线性动态随机模型中，EKF算法可以利用少量观测值，通过迭代过程准确地识别参数并预测系统状态。所确定的数学模型为检验系统假设和检验各种设计参数的影响提供了一种快速而廉价的工具。此外，通过建立的模型，可以预测抗原和抗体浓度的动态变化，从而为我们分析、优化和设计侧流免疫分析装置的性能提供了可能。
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.