基于随机松弛的离散HMM参数估计和信号恢复

Parameter Estimation and Signal Restoration of Discrete HMM Based on Stochastic Relaxation

细胞膜离子单通道信号是皮安级的跨膜随机离子电流,由于信号的微弱性,膜片钳技术记录中单通道电流往往淹没在强背景噪声中。传统上采用阈值检测器来恢复通道电流信号,这需要人为设定阈值,尤其是信噪比低时,阈值检测器失效。本研究采用隐马尔可夫模型(HMM)的通道信号恢复及参数估计技术,首先利用基于随机松弛(SR)的离散HMM参数全局优化算法,估计通道的动力学参数,确保模型训练中参数收敛到全局最优。在此基础上,从噪声污染的膜片钳记录中恢复通道电流信号。理论和实验结果表明,在低信噪比情况下(SNR<5.0),该方法用于白噪声背景下细胞膜离子单通道参数估计和信号恢复时,参数收敛速度快,信号恢复精度高,算法抗噪能力强,可以较好地描述实际对象特性。

Ionic single-channel signal of cell membrane is a stochastic ionic current in the order of picoampere （pA）. Because of weakness of the signal, the background noise always dominates in the patch-clamp recordings. The threshold detector was traditionally used to denoise and restore the single channel current, however, often failed when signal-to-noise ratio was lower. An approach based on hidden Markov model （HMM） was presented in this article to restore ionic single-channel current under the strong background noise. In the study, a global optimization algorithm based on stochastic relaxation （SR） was used to estimate the HMM＇s parameters and to ensure the parameters＇ convergence to a global optimization. The ideal channel currents were reconstructed applying Viterbi algorithm from the patch-clamp recordings contaminated by noise. The theory and experiments have shown that the method performed effectively under the lower signal-to-noise ratio （SNR 〈 5.0） and had fast model parameter convergence, high restoration precision and strong denoising performance.