title: Direct identification of bacteria in blood culture samples using an electronic nose.
abstract: 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.
authors.affiliation: Center for Applied Autonomous Sensor Systems, School of Science and Technology, Orebro University, Orebro, Sweden. marco.trincavelli@oru.se
authors.forename: Marco
authors.initials: M
authors.lastname: Trincavelli
authors.forename: Silvia
authors.initials: S
authors.lastname: Coradeschi
authors.forename: Amy
authors.initials: A
authors.lastname: Loutfi
authors.forename: Bo
authors.initials: B
authors.lastname: Söderquist
authors.forename: Per
authors.initials: P
authors.lastname: Thunberg
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016470:Bacteremia / Q000097:blood*; D001419:Bacteria / Q000737:chemistry; D001431:Bacteriological Techniques / Q000379:methods*; D015374:Biosensing Techniques / Q000379:methods*; D001769:Blood / Q000382:microbiology*; D005740:Gases / Q000032:analysis; D006801:Humans; D009812:Odorants / Q000032:analysis*; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted
chemical_list: D005740:Gases