An effective feature extraction method used in breath analysis

Haifen Chen, Guangming Lu, Dongmin Guo, Dapeng Zhang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

It has been reported that human breath could represent some kinds of diseases. By analyzing the components of breath odor, it is easy to detect the diseases the subjects infected. The accuracy of breath analysis depends greatly on what feature are extracted from the response curve of breath analysis system. In this paper, we proposed an effective feature extraction method based on curve fitting for breath analysis, where breath odor were captured and processed by a self-designed breath analysis system. Two parametric analytic models were used to fit the ascending and descending part of the sensor signals respectively, and the set of best-fitting parameters were taken as features. This process is fast, robust, and with less fitting error than other fitting models. Experimental results showed that the features extracted by our method can significantly enhance the performance of subsequent classification algorithms.
Original languageEnglish
Title of host publicationMedical Biometrics - Second International Conference, ICMB 2010, Proceedings
Pages33-41
Number of pages9
DOIs
Publication statusPublished - 21 Jul 2010
Event2nd International Conference on Medical Biometrics, ICMB 2010 - Hong Kong, Hong Kong
Duration: 28 Jun 201030 Jun 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6165 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference2nd International Conference on Medical Biometrics, ICMB 2010
CountryHong Kong
CityHong Kong
Period28/06/1030/06/10

Keywords

  • Breath odor
  • Curve fitting
  • Feature extraction

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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