Electrical Noises Reduction in Nanopores Experiments Based on Consensus Filter

Yan, Binbin; Cui, Haixu; Zhou, Jiale; Wang, Huifeng

doi:10.21577/0100-4042.20170560

Cited by 1 publication

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“… 30 By confirming the consistency of signals from multiple readout channels of the same nanopore, a consensus filter is adopted to remove the uncorrelated events as noise from each channel. 31 In addition, a weighted network among the single nodes gradually builds up and converges to stable values of the weights. This network can deliver consentient events, i.e., the highly correlated signals from each node, and abandon the uncorrelated events, i.e., noise.…”

Section: Non-ml-based Signal Processing For Nanopore Sensingmentioning

confidence: 99%

“…The separability can be further enhanced by adopting multiple levels of wavelet transform, and a simple achievement for discrete signals is a bank of low- and high-pass filters . By confirming the consistency of signals from multiple readout channels of the same nanopore, a consensus filter is adopted to remove the uncorrelated events as noise from each channel . In addition, a weighted network among the single nodes gradually builds up and converges to stable values of the weights.…”

Section: Non-ml-based Signal Processing For Nanopore Sensingmentioning

confidence: 99%

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A Guide to Signal Processing Algorithms for Nanopore Sensors

2021

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Nanopore technology holds great promise for a wide range of applications such as biomedical sensing, chemical detection, desalination, and energy conversion. For sensing performed in electrolytes in particular, abundant information about the translocating analytes is hidden in the fluctuating monitoring ionic current contributed from interactions between the analytes and the nanopore. Such ionic currents are inevitably affected by noise; hence, signal processing is an inseparable component of sensing in order to identify the hidden features in the signals and to analyze them. This Guide starts from untangling the signal processing flow and categorizing the various algorithms developed to extracting the useful information. By sorting the algorithms under Machine Learning (ML)-based versus non-ML-based, their underlying architectures and properties are systematically evaluated. For each category, the development tactics and features of the algorithms with implementation examples are discussed by referring to their common signal processing flow graphically summarized in a chart and by highlighting their key issues tabulated for clear comparison. How to get started with building up an ML-based algorithm is subsequently presented. The specific properties of the ML-based algorithms are then discussed in terms of learning strategy, performance evaluation, experimental repeatability and reliability, data preparation, and data utilization strategy. This Guide is concluded by outlining strategies and considerations for prospect algorithms.

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Section: Non-ml-based Signal Processing For Nanopore Sensingmentioning

confidence: 99%

Section: Non-ml-based Signal Processing For Nanopore Sensingmentioning

confidence: 99%