Time Series Modeling of Nano-Gold Immunochromatographic Assay via Expectation Maximization Algorithm
Time Series Modeling of Nano-Gold Immunochromatographic Assay via Expectation Maximization Algorithm
复制标题
通过期望最大化算法对纳米金免疫层析测定进行时间序列建模
DOI：
10.1109/tbme.2013.2260160
复制
发表时间：
2013-12-01
影响因子：
4.6
通讯作者：
Liu, Xiaohui
中科院分区：
文献类型：
--
作者：
Zeng, Nianyin;Wang, Zidong;Liu, Xiaohui
在本文中，通过测试线和对照线的测量信号强度的可用时间序列，将期望最大化（EM）算法应用于纳米金免疫层析测定（nano-GICA）的建模。纳米 GICA 的模型被开发为随机动态模型，由一阶自回归随机动态过程和噪声测量组成。通过使用EM算法，可以同时识别模型参数、测试线和控制线的实际信号强度以及噪声强度。采用有关目标浓度的三个不同时间序列数据集来证明所引入算法的有效性。还提出了几个指标来评估推断模型。结果表明，该模型与数据拟合得很好。
In this paper, the expectation maximization (EM) algorithm is applied to the modeling of the nano-gold immunochromatographic assay (nano-GICA) via available time series of the measured signal intensities of the test and control lines. The model for the nano-GICA is developed as the stochastic dynamic model that consists of a first-order autoregressive stochastic dynamic process and a noisy measurement. By using the EM algorithm, the model parameters, the actual signal intensities of the test and control lines, as well as the noise intensity can be identified simultaneously. Three different time series data sets concerning the target concentrations are employed to demonstrate the effectiveness of the introduced algorithm. Several indices are also proposed to evaluate the inferred models. It is shown that the model fits the data very well.