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Direct identification of bacteria in blood culture samples using an electronic nose
Örebro University, School of Science and Technology.ORCID iD: 0000-0003-0195-2102
Örebro University, School of Science and Technology.
Örebro University, School of Science and Technology.ORCID iD: 0000-0002-3122-693X
Örebro University Hospital, Örebro, Sweden .ORCID iD: 0000-0001-5939-2932
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2010 (English)In: IEEE Transactions on Biomedical Engineering, ISSN 0018-9294, E-ISSN 1558-2531, Vol. 57, no 12, 2884-2890 p.Article in journal (Refereed) Published
Abstract [en]

In this paper, we introduce a method for identification of bacteria in human blood culture samples using an electronic nose. The method uses features, which capture the static (steady state) and dynamic (transient) properties of the signal from the gas sensor array and proposes a means to ensemble results from consecutive samples. The underlying mechanism for ensembling is based on an estimation of posterior probability, which is extracted from a support vector machine classifier. A large dataset representing ten different bacteria cultures has been used to validate the presented methods. The results detail the performance of the proposed algorithm and show that through ensembling decisions on consecutive samples, significant reliability in classification accuracy can be achieved.

Place, publisher, year, edition, pages
Piscataway, USA: Institute of Electrical and Electronics Engineers (IEEE), 2010. Vol. 57, no 12, 2884-2890 p.
Keyword [en]
Bacteria identification, electronic nose, sepsis
National Category
Computer Science Robotics
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:oru:diva-12831DOI: 10.1109/TBME.2010.2049492ISI: 000284360100011PubMedID: 20460199Scopus ID: 2-s2.0-78649274345OAI: oai:DiVA.org:oru-12831DiVA: diva2:385198
Available from: 2011-01-11 Created: 2011-01-03 Last updated: 2017-10-18Bibliographically approved
In thesis
1. Gas discrimination for mobile robots
Open this publication in new window or tab >>Gas discrimination for mobile robots
2010 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The problem addressed in this thesis is discrimination of gases with an array of partially selective gas sensors. Metal oxide gas sensors are the most common gas sensing technology since they have, compared to other gas sensing technologies, a high sensitivity to the target compounds, a fast response time,they show a good stability of the response over time and they are commercially available. One of the most severe limitation of metal oxide gas sensors is the scarce selectivity, that means that they do not respond only to the compound for which they are optimized but also to other compounds. One way to enhance the selectivity of metal oxide gas sensors is to build an array of sensorswith different, and partially overlapping, selectivities and then analyze the response of the array with a pattern recognition algorithm. The concept of anarray of partially selective gas sensors used together with a pattern recognition algorithm is known as an electronic nose (e-nose).In this thesis the attention is focused on e-nose applications related mobile robotics. A mobile robot equipped with an e-nose can address tasks like environmental monitoring, search and rescue operations or exploration of hazardous areas. In e-noses mounted on mobile robots the sensing array is most often directly exposed to the environment without the use of a sensing chamber.This choice is often made because of constraints in weight, costs and because the dynamic response obtained by the direct interaction of the sensors with the gas plume contains valuable information. However, this setup introduces additional challenges due to the gas dispersion that characterize natural environments.Turbulent and chaotic gas dispersal causes the array of sensors to be exposed to rapid changes in concentration that cause the sensor response to behighly dynamic and to seldom reach a steady state. Therefore the discriminationof gases has to be performed on features extracted from the dynamics of the signal. The problem is further complicated by variations in temperature and humidity, physical variables to which metal oxide gas sensors are crossensitive.For these reasons the problem of discrimination of gases when an array of sensors is directly exposed to the environment is different from when the array of sensors is in a controlled chamber.

This thesis is a compilation of papers whose contributions are two folded.On one side new algorithms for discrimination of gases with an array of sensors directly exposed to the environment are presented. On the other side, innovative experimental setups are proposed. These experimental setups enable the collection of high quality data that allow a better insight in the problem of discrimination of gases with mobile robots equipped with an e-nose. The algorithmic contributions start with the design and validation of a gas discrimination algorithm for gas sensors array directly exposed to the environment. The algorithmis then further developed in order to be able to run online on a robot, thereby enabling the possibility of creating an olfactory driven path-planning strategy. Additional contributions aim at maximizing the generalization capabilitiesof the gas discrimination algorithm with respect to variations in the environmental conditions. First an approach in which the odor discrimination is performed by an ensemble of linear classifiers is considered. Then a feature selection method that aims at finding a feature set that is insensitive to variations in environmental conditions is designed. Finally, a further contribution in this thesis is the design of a pattern recognition algorithm for identification of bacteria from blood vials. In this case the array of gas sensors was deployed ina controlled sensing chamber.

Place, publisher, year, edition, pages
Örebro: Örebro universitet, 2010. 69 p.
Series
Örebro Studies in Technology, ISSN 1650-8580 ; 41
National Category
Engineering and Technology Computer and Information Science Computer Science
Research subject
Computer and Systems Science
Identifiers
urn:nbn:se:oru:diva-11901 (URN)978-91-7668-762-8 (ISBN)
Public defence
2010-12-03, Hörsal Teknik (HST), Fakultetsgatan 1, Örebro, 13:15 (English)
Opponent
Supervisors
Available from: 2010-09-24 Created: 2010-09-24 Last updated: 2017-10-17Bibliographically approved

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