• DocumentCode
    1715330
  • Title

    Recognizing thoracic breathing by ensemble empirical mode decomposition

  • Author

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

  • Author_Institution
    Dept. of Med. Inf., Tzu Chi Univ., Hualien, Taiwan
  • fYear
    2013
  • Firstpage
    1
  • Lastpage
    5
  • 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.
  • Keywords
    diseases; pneumodynamics; abdominal breathing; adaptive algorithm; complex time variation; correlation coefficient; ensemble empirical mode decomposition; power proportion; real time breathing evaluation system; recognizing thoracic breathing pattern; Accuracy; Correlation coefficient; Empirical mode decomposition; Lungs; Muscles; Noise; Pattern recognition; abdominal breathing; ensemble empirical mode decomposition; thoracic breathing;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information, Communications and Signal Processing (ICICS) 2013 9th International Conference on
  • Conference_Location
    Tainan
  • Print_ISBN
    978-1-4799-0433-4
  • Type

    conf

  • DOI
    10.1109/ICICS.2013.6782956
  • Filename
    6782956