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

科研成果: 期刊稿件 › 文章 › 同行评审

11 引用（Scopus）

摘要

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.

源语言	英语
页（从-至）	480-488
页数	9
期刊	Journal of Advanced Computational Intelligence and Intelligent Informatics
卷	18
期	4
DOI	https://doi.org/10.20965/jaciii.2014.p0480
出版状态	已出版 - 7月 2014
已对外发布	是

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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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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

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N2 - 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.

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

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Segmented wavelet decomposition for capnogram feature extraction in asthma classification

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