Fast custom wavelet analysis technique for single molecule detection and identification

Many sensors operate by detecting and identifying individual events in a time-dependent signal which is challenging if signals are weak and background noise is present. We introduce a powerful, fast, and robust signal analysis technique based on a massively parallel continuous wavelet transform (CWT...

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Bibliographic Details
Published in:Nature communications 2022-02, Vol.13 (1), p.1035-1035, Article 1035
Main Authors: Ganjalizadeh, Vahid, Meena, Gopikrishnan G., Wall, Thomas A., Stott, Matthew A., Hawkins, Aaron R., Schmidt, Holger
Format: Article
Language:English
Subjects:
49/62
639/166/987
639/624/1075/1079
639/624/1111/1117
639/925/927/511
96/10
96/31
96/34
Algorithms
Background noise
Continuous wavelet transform
Data analysis
Data processing
Fluorescence
Humanities and Social Sciences
multidisciplinary
Noise
Real time
Science
Science (multidisciplinary)
Sensors
Signal analysis
Signal Processing, Computer-Assisted
Time dependence
Wavelet Analysis
Wavelet transforms
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Summary:Many sensors operate by detecting and identifying individual events in a time-dependent signal which is challenging if signals are weak and background noise is present. We introduce a powerful, fast, and robust signal analysis technique based on a massively parallel continuous wavelet transform (CWT) algorithm. The superiority of this approach is demonstrated with fluorescence signals from a chip-based, optofluidic single particle sensor. The technique is more accurate than simple peak-finding algorithms and several orders of magnitude faster than existing CWT methods, allowing for real-time data analysis during sensing for the first time. Performance is further increased by applying a custom wavelet to multi-peak signals as demonstrated using amplification-free detection of single bacterial DNAs. A 4x increase in detection rate, a 6x improved error rate, and the ability for extraction of experimental parameters are demonstrated. This cluster-based CWT analysis will enable high-performance, real-time sensing when signal-to-noise is hardware limited, for instance with low-cost sensors in point of care environments. The authors introduce an accurate, fast and efficient technique to analyze sensory data. They use a continuous wavelet transform concept to look for certain patterns in noisy raw data. The superiority of this approach is demonstrated with fluorescence signals from a chip-based, optofluidic single particle sensor.
ISSN:2041-1723
2041-1723
DOI:10.1038/s41467-022-28703-z