Journal
ACS SENSORS
Volume 6, Issue 10, Pages 3536-3555Publisher
AMER CHEMICAL SOC
DOI: 10.1021/acssensors.1c01618
Keywords
nanopore sensing; signal processing algorithm; pulse-like signal; spike recognition; feature extraction; analyte identification; machine learning; neural network
Funding
- Swedish Research Council [621-2014-6300]
- Stiftelsen Olle Engkvist Byggmastare [2016/39, 194-0646, 211-0085]
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Nanopore technology shows great potential in various applications, with signal processing being a crucial component for identifying hidden features in sensing. Algorithms are categorized into Machine Learning (ML)-based and non-ML-based, with discussions on their underlying architectures and properties to help readers understand how to extract and analyze useful information from signals.
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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