Capnogram feature extraction based on wavelet decomposition by segments for classification of asthma severity

Janet Pomares Betancourt; Martin Leonard Tangel; Fei Yan; Marianela Otaño; Alejandro E. Portela; Fang Yan Dong; Kaoru Hirota

Capnogram feature extraction based on wavelet decomposition by segments for classification of asthma severity

Janet Pomares Betancourt
, Martin Leonard Tangel
, Fei Yan
, Marianela Otaño
, Alejandro E. Portela
, Fang Yan Dong
, Kaoru Hirota

Research output: Contribution to conference › Paper › peer-review

Abstract

A method for feature extraction of the capnogram for asthma classification is proposed based on wavelet decomposition. The method requires low computational cost and shows adequate performance for a real time classification of asthma severity. The experiments include testing 23 capnograms collected from an Asthma Camp in Cuba on a personal computer architecture running Matlab. An estimation of the execution time for a physiological multiparameter monitor is obtained, showing an average of 8 sec to determine the suitable features. The proposal aims to be a part in the decision support system for asthma classification that is under development by research group of TITECH and TMDU.

Original language	English
Publication status	Published - 2014
Externally published	Yes
Event	3rd International Workshop on Advanced Computational Intelligence and Intelligent Informatics, IWACIII 2013 - Shanghai, China Duration: 18 Oct 2013 → 21 Oct 2013

Conference

Conference	3rd International Workshop on Advanced Computational Intelligence and Intelligent Informatics, IWACIII 2013
Country/Territory	China
City	Shanghai
Period	18/10/13 → 21/10/13

Keywords

Asthma
Capnogram
Feature extraction
Wavelet

Cite this

@conference{edaa6ab8723e4c5fbe12c4c5ea8396f8,

title = "Capnogram feature extraction based on wavelet decomposition by segments for classification of asthma severity",

abstract = "A method for feature extraction of the capnogram for asthma classification is proposed based on wavelet decomposition. The method requires low computational cost and shows adequate performance for a real time classification of asthma severity. The experiments include testing 23 capnograms collected from an Asthma Camp in Cuba on a personal computer architecture running Matlab. An estimation of the execution time for a physiological multiparameter monitor is obtained, showing an average of 8 sec to determine the suitable features. The proposal aims to be a part in the decision support system for asthma classification that is under development by research group of TITECH and TMDU.",

keywords = "Asthma, Capnogram, Feature extraction, Wavelet",

author = "Betancourt, \{Janet Pomares\} and Tangel, \{Martin Leonard\} and Fei Yan and Marianela Ota{\~n}o and Portela, \{Alejandro E.\} and Dong, \{Fang Yan\} and Kaoru Hirota",

year = "2014",

language = "English",

note = "3rd International Workshop on Advanced Computational Intelligence and Intelligent Informatics, IWACIII 2013 ; Conference date: 18-10-2013 Through 21-10-2013",

}

Capnogram feature extraction based on wavelet decomposition by segments for classification of asthma severity. / Betancourt, Janet Pomares; Tangel, Martin Leonard; Yan, Fei et al.
2014. Paper presented at 3rd International Workshop on Advanced Computational Intelligence and Intelligent Informatics, IWACIII 2013, Shanghai, China.

Research output: Contribution to conference › Paper › peer-review

TY - CONF

T1 - Capnogram feature extraction based on wavelet decomposition by segments for classification of asthma severity

AU - Betancourt, Janet Pomares

AU - Tangel, Martin Leonard

AU - Yan, Fei

AU - Otaño, Marianela

AU - Portela, Alejandro E.

AU - Dong, Fang Yan

AU - Hirota, Kaoru

PY - 2014

Y1 - 2014

N2 - A method for feature extraction of the capnogram for asthma classification is proposed based on wavelet decomposition. The method requires low computational cost and shows adequate performance for a real time classification of asthma severity. The experiments include testing 23 capnograms collected from an Asthma Camp in Cuba on a personal computer architecture running Matlab. An estimation of the execution time for a physiological multiparameter monitor is obtained, showing an average of 8 sec to determine the suitable features. The proposal aims to be a part in the decision support system for asthma classification that is under development by research group of TITECH and TMDU.

AB - A method for feature extraction of the capnogram for asthma classification is proposed based on wavelet decomposition. The method requires low computational cost and shows adequate performance for a real time classification of asthma severity. The experiments include testing 23 capnograms collected from an Asthma Camp in Cuba on a personal computer architecture running Matlab. An estimation of the execution time for a physiological multiparameter monitor is obtained, showing an average of 8 sec to determine the suitable features. The proposal aims to be a part in the decision support system for asthma classification that is under development by research group of TITECH and TMDU.

KW - Asthma

KW - Capnogram

KW - Feature extraction

KW - Wavelet

UR - https://www.scopus.com/pages/publications/84917710972

M3 - Paper

AN - SCOPUS:84917710972

T2 - 3rd International Workshop on Advanced Computational Intelligence and Intelligent Informatics, IWACIII 2013

Y2 - 18 October 2013 through 21 October 2013

ER -

Capnogram feature extraction based on wavelet decomposition by segments for classification of asthma severity

Abstract

Conference

Keywords

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