Noise Reduction Method of Nanopore Based on Wavelet and Kalman Filter - Zhouchang Huang; Xiaoqing Zeng; Deqiang Wang; Shaoxi Fang (Applied Sciences, 2022)
Nanopore detection technology has now developed into an indispensable tool for single molecule experiments, in which useful information on molecular properties can be obtained from the ion current flow induced by individual molecules, but the relatively high background noise affects the data analysis. Therefore, a nanopore signal noise reduction method based on wavelet transform and Kalman filter is proposed, which can achieve multi-scale decomposition and optimal estimation of the nanopore signal. The real measurement shows that the Kalman filter based on the wavelet mode maxima method reduces the root mean square (RMS) of the background noise by 17.8%, and the noise reduction effect is better than the traditional Kalman filter method.