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A Guide to Signal Processing Algorithms for Nanopore Sensors

Wen, Chenyu ; Dematties, Dario ; Zhang, Shi-Li

Uppsala universitet, Fasta tillståndets elektronik 2021

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  • Título:
    A Guide to Signal Processing Algorithms for Nanopore Sensors
  • Autor: Wen, Chenyu ; Dematties, Dario ; Zhang, Shi-Li
  • Assuntos: analyte identification ; Annan elektroteknik och elektronik ; feature extraction ; machine learning ; nanopore sensing ; neural network ; Other Electrical Engineering, Electronic Engineering, Information Engineering ; pulse-like signal ; signal processing algorithm ; spike recognition
  • Notas: PMID 34601866
    ACS Sensors, 2021, 6:10, s. 3536-3555
    0000-0003-4395-7905
    ISI:000711034500005
    0000-0003-2417-274x
    doi:10.1021/acssensors.1c01618
    http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-459212
  • Descrição: 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.
  • Editor: Uppsala universitet, Fasta tillståndets elektronik
  • Data de criação/publicação: 2021
  • Idioma: Inglês
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