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; Kaoru Hirota

doi:10.20965/jaciii.2014.p0480

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, Kaoru Hirota

Research output: Contribution to journal › Article › peer-review

11 Citations (Scopus)

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
Pages (from-to)	480-488
Number of pages	9
Journal	Journal of Advanced Computational Intelligence and Intelligent Informatics
Volume	18
Issue number	4
DOIs	https://doi.org/10.20965/jaciii.2014.p0480
Publication status	Published - Jul 2014
Externally published	Yes

Keywords

Asthma
Capnogram
Feature extraction
Wavelet

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10.20965/jaciii.2014.p0480

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title = "Segmented wavelet decomposition for capnogram feature extraction in asthma classification",

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.",

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language = "English",

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AU - Betancourt, Janet Pomares

AU - Tangel, Martin Leonard

AU - Yan, Fei

AU - Diaz, Marianella Otaño

AU - Otaño, Alejandro Ernesto Portela

AU - Dong, Fangyan

AU - Hirota, Kaoru

PY - 2014/7

Y1 - 2014/7

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AB - 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.

KW - Asthma

KW - Capnogram

KW - Feature extraction

KW - Wavelet

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JO - Journal of Advanced Computational Intelligence and Intelligent Informatics

JF - Journal of Advanced Computational Intelligence and Intelligent Informatics

IS - 4

ER -

Segmented wavelet decomposition for capnogram feature extraction in asthma classification

Abstract

Keywords

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