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
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;
Conference_Titel :
Information, Communications and Signal Processing (ICICS) 2013 9th International Conference on
Conference_Location :
Tainan
Print_ISBN :
978-1-4799-0433-4
DOI :
10.1109/ICICS.2013.6782956