Journal
NATURE COMMUNICATIONS
Volume 13, Issue 1, Pages -Publisher
NATURE PORTFOLIO
DOI: 10.1038/s41467-022-28703-z
Keywords
-
Categories
Funding
- National Institutes of Health [1R01AI116989-01, 1R01EB028608]
- National Science Foundation [CBET-1703058]
- Cisco University Research Program Fund [2020-224954]
Ask authors/readers for more resources
This article introduces a signal analysis technique based on a massively parallel continuous wavelet transform algorithm, which can accurately and quickly handle weak signals and background noise. Through experimental demonstrations, the superiority of this method is proved, and the performance improvement in detecting bacterial DNA is shown.
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available