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

Author

Venkataramanan, Lalitha ; Kuc, Roman ; Sigworth, Fred J.

Author_Institution

Schlumberger-Doll Res., Ridgefield, CT, USA

Volume

48

Issue

2

fYear

2000

fDate

2/1/2000 12:00:00 AM

Firstpage

376

Lastpage

385

Abstract

For pt.II. see ibid., vol.46, p.1916-29 (1998). Hidden Markov models (HMMs) 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

Keywords

bandlimited signals; biological techniques; biomembrane transport; correlation methods; electric current; filtering theory; hidden Markov models; identification; least squares approximations; noise; signal sampling; Baum-Welch weighted least squares algorithm; H-noise algorithm; HMM parameter estimation; HMMs identification; antialiasing filter; bandlimited sampled data; continuous time patch-clamp recordings; correlated background noise; correlated emission probability; hidden Markov models; ion channel currents; living cell membrane; metastate framework; noise correlation; noise model parameter estimation; patch clamp technique; simulated data; single channel currents; vector HMM framework; Additive noise; Band pass filters; Displays; Filtering; Hidden Markov models; Least squares approximation; Markov processes; Metastasis; Parameter estimation; Proteins;

fLanguage

English

Journal_Title

Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

1053-587X

Type

jour

DOI

10.1109/78.823965

Filename

823965