Han, Zhenzhe, Cavallo, Francesca, Nikolic, Konstantin ORCID: https://orcid.org/0000-0002-6551-2977, Mirza, Khalid and Toumazou, Christofer (2021) Signal identification of DNA amplification curves in custom-PCR platforms. In: 2021 IEEE International Symposium on Circuits and Systems (ISCAS), 23-26 May 2021, Daegu, South Korea.

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Abstract

Custom-made, point-of-care PCR platforms are a necessary tool for rapid, point-of-care diagnostics in situations such as the current Covid-19 pandemic. However, a common issue faced by them is noisy fluorescence signals, which consist of a drifting baseline or noisy sigmoidal curve. This makes automated detection difficult and requires human verification. In this paper, we have tried to use nonlinear fitting for automated classification of PCR waveforms to identify whether amplification has taken place or not. We have presented several novel signal reconstruction techniques based on nonlinear fitting which will enable better pre-processing and automated differentiation of a valid or invalid PCR amplification curve. We have also tried to perform this classification at lower PCR cycles to reduce decision times in diagnostic tests.

Item Type:	Conference or Workshop Item (Paper)
ISSN:	2158-1525
ISBN:	9781728192017
Identifier:	10.1109/ISCAS51556.2021.9401777
Page Range:	pp. 1-5
Identifier:	10.1109/ISCAS51556.2021.9401777
Additional Information:	© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Keywords:	qPCR, noisy, automated detection, nonlinear curve fitting, classification, lower PCR cycles
Subjects:	Construction and engineering > Biomedical engineering
Related URLs:	Publisher
Depositing User:	Konstantin Nikolic
Date Deposited:	15 Jun 2021 13:03
Last Modified:	04 Nov 2024 12:47
URI:	https://repository.uwl.ac.uk/id/eprint/7999

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