Recognizing thoracic breathing by ensemble empirical mode decomposition

Jin Long Chen, Ya Chen Chen, Tzu-Chien Hsiao

    Research output: Contribution to conferencePaperpeer-review

    1 Scopus citations

    Abstract

    Recognizing breathing pattern is important in many fields of medicine. Ensemble empirical mode decomposition (an adaptive algorithm) was used to investigate breathing pattern, including thoracic breathing (TB) and abdominal breathing (AB). This study recognizes TB and AB by correlation coefficient and power proportion. Results indicate that the recognition accuracy of TB by correlation coefficient and power proportion are 85.2% and 93.3% respectively, and that of AB by correlation coefficient and power proportion are 54.3% and 56.2% respectively. The TB can be well defined and recognized in complex time variation. These results can be used as references to develop the real time breathing evaluation system in the future.

    Original languageEnglish
    DOIs
    StatePublished - 1 Jan 2013
    Event9th International Conference on Information, Communications and Signal Processing, ICICS 2013 - Tainan, Taiwan
    Duration: 10 Dec 201313 Dec 2013

    Conference

    Conference9th International Conference on Information, Communications and Signal Processing, ICICS 2013
    Country/TerritoryTaiwan
    CityTainan
    Period10/12/1313/12/13

    Keywords

    • abdominal breathing
    • ensemble empirical mode decomposition
    • thoracic breathing

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