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 language | English |
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Pages (from-to) | 480-488 |
Number of pages | 9 |
Journal | Journal of Advanced Computational Intelligence and Intelligent Informatics |
Volume | 18 |
Issue number | 4 |
DOIs | |
Publication status | Published - Jul 2014 |
Externally published | Yes |
Keywords
- Asthma
- Capnogram
- Feature extraction
- Wavelet