期刊文献+

The on-line direct fitting of low signal-noise ratio single ion channel recordings based on hidden Markov models

The on-line direct fitting of low signal-noise ratio single ion channel recordings based on hidden Markov models
下载PDF
导出
摘要 Many kinds of channel currents are especially weak and the background noise dominates in the patch clamp recordings. This makes the threshold detection fail during estimating of the transition probabilities. So direct fitting of the patch clamp recording, not of the histogram coming from the recordings, is a desirable way to estimate the transition probabilities. Iterative batch EM algorithm based on hidden markov model has been used in this field but which has the "curse of dimensionality" and besides cant keep tracking the varying of the parameters. A new on line sequential iterative one is proposed here, which needs fewer computational efforts and can adaptively keep tracking the varying of parameters. Simulations suggest its robust, effective and convenient. Many kinds of channel currents are especially weak and the background noise dominates in the patch clamp recordings. This makes the threshold detection fail during estimating of the transition probabilities. So direct fitting of the patch clamp recording, not of the histogram coming from the recordings, is a desirable way to estimate the transition probabilities. Iterative batch EM algorithm based on hidden markov model has been used in this field but which has the 'curse of dimensionality' and besides cant keep tracking the varying of the parameters. A new on line sequential iterative one is proposed here, which needs fewer computational efforts and can adaptively keep tracking the varying of parameters. Simulations suggest its robust, effective and convenient.
出处 《Chinese Journal of Biomedical Engineering(English Edition)》 2002年第2期51-60,共10页 中国生物医学工程学报(英文版)
关键词 SINGLE ion channel RECORDING hidden MARKOV model (HMM) on line algorithm Kullback Leibler (KL) information measure single ion channel recording, hidden Markov model (HMM), on line algorithm,Kullback Leibler (KL) information measure
  • 相关文献

参考文献4

  • 1V. Krishnamurthy,Andrew Logothetis.Iterative and recursive estimators for hidden Markov errors-in-variables models[].IEEE Transactions on Signal Processing.1996
  • 2Ehud Weinstein,Merd Feder,Alan V.Oppenheim.Sequential algorithm for parameter estimation based on the Kullback-Leibler information measure[].IEEE Transactions on Signal Processing.1990
  • 3S. H. Chung,J. Moore,L. Xia,et al.Characterization of single channel currents using digital signal processing techniques based on hidden Markov models[].Phil Trans R Soc Lond B.1990
  • 4L. Venkataramanan,J. L. Walsh,Fred J. Sigworth.Identification of hidden Markov models for ion channel currents-part 1: colored background noise[].IEEE Transactions on Signal Processing.1998

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部