Identification of hidden Markov models for ion channel currents .III. Bandlimited, sampled data

Hidden Markov models (HMM’s) have been used to model single channel currents as recorded with the patch clamp technique from living cells. Continuous time patch-clamp recordings are typically passed through an antialiasing filter and sampled before analysis. In this paper, an adaptation of the Baum-Welch weighted least squares (BW-WLS) algorithm called the H-noise algorithm is presented to estimate the HMM and noise model parameters from bandlimited, sampled data. The effects of the antialiasing filter and the correlated background noise are considered in a metastate or vector HMM framework. The “correlated emission probability,” which plays a central role in the algorithm, is redefined to consider the noise correlation in successive filtered, sampled data points. The performance of the H-noise algorithm is demonstrated with simulated data.