Robust Noise Separation and Denoising of Ionic Current Signals for Nanopore Sensors Based on Independent Component Analysis

Nanopore technology holds great potential for broad applications of DNA sequencing, protein identification, and versatile biomedical sensing. However, the detection of ion currents through nanopores is hampered by strong background noise across the entire frequency range. Here, a denoising method based on independent component analysis (ICA) has been introduced to decompose multiple random signals into mutually components that are statistically as independent from each other as possible. The results demonstrate that ICA can effectively separate and reduce the noise across the whole frequency range, rather than simply limiting a certain cutoff frequency. The denoising effect is remarkable for the common mode noise based on the correlation of currents and voltages. The improved current signals have been identified and analyzed statistically in large quantities, while retaining more temporal features of current pulses at higher bandwidths. The robust performance of ICA offers a favorable term to promote the precision of nanopore sensors in broader applications.