Identification of hidden Markov models for ion channel currents. II. State-dependent excess noise

Author

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

Author_Institution

Dept. of Electr. Eng., Yale Univ., New Haven, CT, USA

Volume

46

Issue

7

fYear

1998

fDate

7/1/1998 12:00:00 AM

Firstpage

1916

Lastpage

1929

Abstract

For pt.I see ibid., vol.46, no.7, p.1901 (1998). Hidden Markov modeling (HMM) techniques have been applied in the past few years to characterize single ion channel current events at low signal-to-noise ratios (SNRs). In this paper, an adaptation of the forward-backward procedure and Baum-Welch algorithm is presented to model ion channel kinetics under conditions of correlated and state-dependent excess noise like that observed in patch-clamp recordings. An autoregressive with additive nonstationary (ARANS) noise model is introduced to model the experimentally observed noise, and an algorithm called the Baum-Welch weighted least squares (BW-WLS) procedure is presented to re-estimate the noise model parameters along with the parameters of the underlying HMM. The performance of the algorithm is demonstrated with simulated data

Keywords

autoregressive processes; bioelectric phenomena; biomembrane transport; hidden Markov models; interference (signal); least squares approximations; medical signal processing; molecular biophysics; noise; parameter estimation; physiological models; proteins; Baum-Welch algorithm; Baum-Welch weighted least squares procedure; autoregressive with additive nonstationary noise model; cell membrane; correlated noise; forward-backward procedure; hidden Markov models; identification; ion channel currents; ion channel kinetics; low signal-to-noise ratios; modeling; noise model parameters estimation; patch-clamp recordings; performance; proteins; simulated data; state-dependent excess noise; Additive noise; Biomembranes; Cells (biology); Gaussian noise; Genetic mutations; Hidden Markov models; Kinetic theory; Parameter estimation; Signal to noise ratio; Topology;

fLanguage

English

Journal_Title

Signal Processing, IEEE Transactions on

Publisher

ieee

ISSN

1053-587X

Type

jour

DOI

10.1109/78.700964

Filename

700964