Reconstruction of ionic single-channel currents based on hidden Markov model

Single ion channel signal of cell membrane is a stochastic ionic currents in the order of picoampere (pA). Because of the weakness of the signal, the background noise always dominates in the patch-clamp recordings. The threshold detector is traditionally used to eliminate noise and restore the single channel signal. However, this method cannot work satisfactorily when signal-to-noise ratio is lower. An approach based on hidden Markov model (HMM) is used to reconstruct ionic single-channel currents and estimate model parameters under white background noise. Firstly, ionic single-channel currents were depicted and analyzed by HMM. Then, an iterative algorithm of Baum-Welch was introduced to train HMM and estimate the model parameters. Finally, the ideal channel currents were reconstructed by Viterbi algorithm. Compared HMM with the threshold detection by computer simulating under different transition probabilities and signal-to-noise ratios, and the results have shown that the method performs effectively under the low signal-to-noise ratio (SNR<;5.0) and has fast model parameter convergence, high restoration precision and strong noise robustness.