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*, and Kaoru Hirota*
*Tokyo Institute of Technology, G3-49, 4259 Nagatsuta, Midori-ku, Yokohama 226-8502, Japan
**Central Institute of Digital Research, 202, No.1704, Siboney, Playa, Havana, Cuba
***Polyclinic “Julian Grimau” 10 de Octubre Ave., No.1004, Arroyo Naranjo, Havana, Cuba
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.
Marianella Otaño Diaz, Alejandro Ernesto Portela Otaño,
Fangyan Dong, and Kaoru Hirota, “Segmented Wavelet Decomposition for Capnogram Feature Extraction in Asthma Classification,” J. Adv. Comput. Intell. Intell. Inform., Vol.18, No.4, pp. 480-488, 2014.
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