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
Link To Document