Identification of hidden Markov models for ion channel currents - Part III: Bandlimited, sampled data

Hidden Markov models (HMM's) have been used to model single channel current s as recorded with the patch clamp technique from living cells. Continuous time patch-clamp recordings are typically passed through an antialiasing fi lter and sampled before analysis. In this paper, an adaptation of the Baum- Welch weighted least squares (BW-WLS) algorithm called the H-noise algorith m is presented to estimate the HMM and noise model parameters from bandlimi ted, sampled data. The effects of the antialiasing filter and the correlate d background noise are considered in a metastate or vector HMM framework. T he "correlated emission probability," which plays a central role in the alg orithm, is redefined to consider the noise correlation in successive filter ed, sampled data points. The performance of the H-noise algorithm is demons trated with simulated data.