A Generalized Transformer-Based Pulse Detection Algorithm

Dario Dematties, Chenyu Wen, Shi Li Zhang*

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

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Abstract

Pulse-like signals are ubiquitous in the field of single molecule analysis, e.g., electrical or optical pulses caused by analyte translocations in nanopores. The primary challenge in processing pulse-like signals is to capture the pulses in noisy backgrounds, but current methods are subjectively based on a user-defined threshold for pulse recognition. Here, we propose a generalized machine-learning based method, named pulse detection transformer (PETR), for pulse detection. PETR determines the start and end time points of individual pulses, thereby singling out pulse segments in a time-sequential trace. It is objective without needing to specify any threshold. It provides a generalized interface for downstream algorithms for specific application scenarios. PETR is validated using both simulated and experimental nanopore translocation data. It returns a competitive performance in detecting pulses through assessing them with several standard metrics. Finally, the generalization nature of the PETR output is demonstrated using two representative algorithms for feature extraction.

Original languageEnglish
Pages (from-to)2710-2720
Number of pages11
JournalACS Sensors
Volume7
Issue number9
DOIs
Publication statusPublished - 2022

Keywords

  • artificial neural network
  • generalized algorithm
  • machine learning
  • nanopore sensing
  • spike recognition
  • transformer

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