Signal processing for rapid bacterial detection

Vivek Nandakumar; Jeffrey LaBelle; Terry Alford

doi:10.1109/ACSSC.2008.5074777

Signal processing for rapid bacterial detection

Vivek Nandakumar, Jeffrey LaBelle, Terry Alford

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Scopus citations

Abstract

This paper proposes a methodology based on Bayesian decision theory and electrochemical impedance spectroscopy for rapid and reliable detection of Salmonella typhimurium bacteria. The proposed technique has been validated using a cost-effective and portable biosensor. Results indicate a detection time of approximately 6 minutes (5 minutes for data acquisition;1 minute for analysis) for pre-infectious bacterial concentrations. The technique has demonstrated high accuracy and can be generalized for the detection of other pathogens with healthcare relevance. Furthermore, the technique has a low computational complexity and is therefore well-suited for use in hand-held pathogen detectors.

Original language	English (US)
Title of host publication	2008 42nd Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2008
Pages	1979-1981
Number of pages	3
DOIs	https://doi.org/10.1109/ACSSC.2008.5074777
State	Published - Dec 1 2008
Event	2008 42nd Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2008 - Pacific Grove, CA, United States Duration: Oct 26 2008 → Oct 29 2008

Publication series

Name	Conference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)	1058-6393

Other

Other	2008 42nd Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2008
Country/Territory	United States
City	Pacific Grove, CA
Period	10/26/08 → 10/29/08

ASJC Scopus subject areas

Signal Processing
Computer Networks and Communications

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10.1109/ACSSC.2008.5074777

Cite this

Signal processing for rapid bacterial detection. / Nandakumar, Vivek; LaBelle, Jeffrey; Alford, Terry.
2008 42nd Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2008. 2008. p. 1979-1981 5074777 (Conference Record - Asilomar Conference on Signals, Systems and Computers).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Nandakumar, V, LaBelle, J & Alford, T 2008, Signal processing for rapid bacterial detection. in 2008 42nd Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2008., 5074777, Conference Record - Asilomar Conference on Signals, Systems and Computers, pp. 1979-1981, 2008 42nd Asilomar Conference on Signals, Systems and Computers, ASILOMAR 2008, Pacific Grove, CA, United States, 10/26/08. https://doi.org/10.1109/ACSSC.2008.5074777

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abstract = "This paper proposes a methodology based on Bayesian decision theory and electrochemical impedance spectroscopy for rapid and reliable detection of Salmonella typhimurium bacteria. The proposed technique has been validated using a cost-effective and portable biosensor. Results indicate a detection time of approximately 6 minutes (5 minutes for data acquisition;1 minute for analysis) for pre-infectious bacterial concentrations. The technique has demonstrated high accuracy and can be generalized for the detection of other pathogens with healthcare relevance. Furthermore, the technique has a low computational complexity and is therefore well-suited for use in hand-held pathogen detectors.",

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AB - This paper proposes a methodology based on Bayesian decision theory and electrochemical impedance spectroscopy for rapid and reliable detection of Salmonella typhimurium bacteria. The proposed technique has been validated using a cost-effective and portable biosensor. Results indicate a detection time of approximately 6 minutes (5 minutes for data acquisition;1 minute for analysis) for pre-infectious bacterial concentrations. The technique has demonstrated high accuracy and can be generalized for the detection of other pathogens with healthcare relevance. Furthermore, the technique has a low computational complexity and is therefore well-suited for use in hand-held pathogen detectors.

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Signal processing for rapid bacterial detection

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