Segmented wavelet decomposition for capnogram feature extraction in asthma classification

Janet Pomares Betancourt, Martin Leonard Tangel, Fei Yan, Marianella Otaño Diaz, Alejandro Ernesto Portela Otaño, Fangyan Dong, Kaoru Hirota

Research output: Contribution to journalArticlepeer-review

11 Citations (Scopus)

Abstract

A feature extraction method from capnograms used for classifying asthma is proposed based on wavelet decomposition. Its computational cost is low and its performance is adequate for classifying asthma in real time. Experiments performed using 23 capnograms from an asthma camp in Cuba showed 97.39% best classification accuracy. The time required for a physiological multiparameter monitor to determine the suitable features of capnograms averaged 8 seconds. The proposal is to be used as part of a decision support system for asthma classification being developed by TITECH and TMDU research groups.

Original languageEnglish
Pages (from-to)480-488
Number of pages9
JournalJournal of Advanced Computational Intelligence and Intelligent Informatics
Volume18
Issue number4
DOIs
Publication statusPublished - Jul 2014
Externally publishedYes

Keywords

  • Asthma
  • Capnogram
  • Feature extraction
  • Wavelet

Fingerprint

Dive into the research topics of 'Segmented wavelet decomposition for capnogram feature extraction in asthma classification'. Together they form a unique fingerprint.

Cite this