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

2 Citations (Scopus)

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 - http://www.scopus.com/inward/record.url?scp=84917710972&partnerID=8YFLogxK

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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