Statistical Analysis of Ion Channel Data Using Hidden Markov Models With Correlated State-Dependent Noise and Filtering

A hidden Markov model that describes ion channel data, including correlated, state-dependent noise and filter characteristics, and a Markov chain Monte Carlo algorithm that enables Bayesian inference under this model are presented. The method provides parameter estimates and an estimate of the noiseless signal. It was tested on simulated data and applied to real recordings to estimate the model parameters. Modeling the noise and filter correctly turned out to be crucial for the analyzed data sets. The assumption of white noise is too simple, and negligence of the smoothing effect of the low-pass filter leads to errors in the detection of rapid transitions. The hidden Markov model that we propose treats these effects simultaneously.

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