Characterization of single channel currents using digital signal processing techniques based on Hidden Markov Models
| Autor: | L. S. Premkumar, John B. Moore, Peter W. Gage, Lige Xia, Shin-Ho Chung |
|---|---|
| Rok vydání: | 1990 |
| Předmět: |
Potassium Channels
Noise measurement Noise (signal processing) Computer science business.industry Markov process Signal Processing Computer-Assisted Markov model Signal Models Biological General Biochemistry Genetics and Molecular Biology Ion Channels Markov Chains Rats Background noise symbols.namesake Kinetics symbols Animals General Agricultural and Biological Sciences business Hidden Markov model Algorithm Digital signal processing gamma-Aminobutyric Acid |
| Zdroj: | Philosophical transactions of the Royal Society of London. Series B, Biological sciences. 329(1254) |
| ISSN: | 0962-8436 |
| Popis: | Techniques for extracting small, single channel ion currents from background noise are described and tested. It is assumed that single channel currents are generated by a first-order, finite-state, discrete-time, Markov process to which is added 'white’ background noise from the recording apparatus (electrode, amplifiers, etc.). Given the observations and the statistics of the background noise, the techniques described here yielda posterioriestimates of the most likely signal statistics, including the Markov model state transition probabilities, duration (open- and closed-time) probabilities, histograms, signal levels, and the most likely state sequence. Using variations of several algorithms previously developed for solving digital estimation problems, we have demonstrated that: (1) artificial, small, first-order, finite-state, Markov model signals embedded in simulated noise can be extracted with a high degree of accuracy, (2) processing can detect signals that do not conform to a first-order Markov model but the method is less accurate when the background noise is not white, and (3) the techniques can be used to extract from the baseline noise single channel currents in neuronal membranes. Some studies have been included to test the validity of assuming a first-order Markov model for biological signals. This method can be used to obtain directly from digitized data, channel characteristics such as amplitude distributions, transition matrices and open- and closed-time durations. |
| Databáze: | OpenAIRE |
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