A Generalized Transformer-Based Pulse Detection Algorithm

作者：Darío Demattíes, Chenyu Wen, Shi‐Li Zhang · 发表于：ACS Sensors · 年份：2022 · DOI：10.1021/acssensors.2c01218 · 被引用次数：11 · 研究领域：Nanopore and Nanochannel Transport Studies、Spectroscopy Techniques in Biomedical and Chemical Research、Ion-surface interactions and analysis

, 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.