Segmented Wavelet Decomposition for Capnogram Feature Extraction in Asthma Classification | Zendy

Janet Pomares Betancourt | Zendy; Martin Leonard Tangel | Zendy; Fei Yan | Zendy; Marianella Otaño Diaz | Zendy; Alejandro Portela | Zendy; Fangyan Dong | Zendy; Kaoru Hirota | Zendy

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Segmented Wavelet Decomposition for Capnogram Feature Extraction in Asthma Classification

Author(s) -

Janet Pomares Betancourt,

Martin Leonard Tangel,

Fei Yan,

Marianella Otaño Diaz,

Alejandro Portela,

Fangyan Dong,

Kaoru Hirota

Publication year - 2014

Publication title -

journal of advanced computational intelligence and intelligent informatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.172

H-Index - 20

eISSN - 1343-0130

pISSN - 1883-8014

DOI - 10.20965/jaciii.2014.p0480

Subject(s) - computer science , asthma , pattern recognition (psychology) , feature extraction , artificial intelligence , wavelet , feature (linguistics) , wavelet transform , decomposition , data mining , medicine , linguistics , philosophy , ecology , biology

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.

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