Hidden Markov modeling for single channel kinetics with filtering and correlated noise
Hidden Markov modeling for single channel kinetics with filtering and correlated noise
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DOI：
10.1016/s0006-3495(00)76442-3
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发表时间：
2000-10-01
影响因子：
3.4
通讯作者：
Sachs, F
中科院分区：
文献类型：
--
作者：
Qin, F;Auerbach, A;Sachs, F
隐马尔可夫模型（HMM）可以应用于提取单通道动力学的信噪比太低，传统的分析。隐马尔可夫模型有两种常用的方法：传统的Baum重估计和直接优化。优化方法具有直接优化速率常数的优点。这允许对速率常数设置约束，在不同的实验条件下拟合多个数据集，并处理非平稳通道，其中通道的起始概率取决于未知的动力学。我们在这里提出了这种方法的扩展，解决了低通滤波和相关噪声的其他问题。使用应用于基础信号的有限脉冲响应（FIR）滤波器来对滤波进行建模，并且使用自回归（AR）过程来考虑噪声相关性。除了相关背景噪声之外，该算法还允许可以是白色或相关的过量开放信道噪声。为了最大限度地提高算法的效率，我们推导出的分析导数的似然函数相对于所有未知的模型参数。使用可变度量方法执行似然空间的搜索。扩展的算法，包含多个通道的数据进行说明。算例表明了该算法的适用性和有效性。实际问题，如适当的噪声AR阶数的选择也通过例子进行了讨论。
Hidden Markov modeling (HMM) can be applied to extract single channel kinetics at signal-to-noise ratios that are too low for conventional analysis. There are two general HMM approaches: traditional Baum's reestimation and direct optimization. The optimization approach has the advantage that it optimizes the rate constants directly. This allows setting constraints on the rate constants, fitting multiple data sets across different experimental conditions, and handling nonstationary channels where the starting probability of the channel depends on the unknown kinetics. We present here an extension of this approach that addresses the additional issues of low-pass filtering and correlated noise. The filtering is modeled using a finite impulse response (FIR) filter applied to the underlying signal, and the noise correlation is accounted for using an autoregressive (AR) process, In addition to correlated background noise, the algorithm allows for excess open channel noise that can be white or correlated. To maximize the efficiency of the algorithm, we derive the analytical derivatives of the likelihood function with respect to all unknown model parameters. The search of the likelihood space is performed using a variable metric method. Extension of the algorithm to data containing multiple channels is described. Examples are presented that demonstrate the applicability and effectiveness of the algorithm. Practical issues such as the selection of appropriate noise AR orders are also discussed through examples.