NDLI: Respiratory rate detection by empirical mode decomposition method applied to diaphragm mechanomyographic signals

Content Provider	IEEE Xplore Digital Library
Author	Estrada, L. Torres, A. Sarlabous, L. Fiz, J.A. Jane, R.
Copyright Year	2014
Description	Author affiliation: Hosp. Germans Trias i Pujol, Spain (Fiz, J.A.) \|\| Inst. de Bioenginyeria de Catalunya (IBEC), Univ. Politec. de Catalunya (UPC), Barcelona, Spain (Estrada, L.; Torres, A.; Jane, R.) \|\| CIBER-BBN, Spain (Sarlabous, L.)
Abstract	Non-invasive evaluation of respiratory activity is an area of increasing research interest, resulting in the appearance of new monitoring techniques, ones of these being based on the analysis of the diaphragm mechanomyographic (MMGdi) signal. The MMGdi signal can be decomposed into two parts: (1) a high frequency activity corresponding to lateral vibration of respiratory muscles, and (2) a low frequency activity related to excursion of the thoracic cage. The purpose of this study was to apply the empirical mode decomposition (EMD) method to obtain the low frequency of MMGdi signal and selecting the intrinsic mode functions related to the respiratory movement. With this intention, MMGdi signals were acquired from a healthy subject, during an incremental load respiratory test, by means of two capacitive accelerometers located at left and right sides of rib cage. Subsequently, both signals were combined to obtain a new signal which contains the contribution of both sides of thoracic cage. Respiratory rate (RR) measured from the mechanical activity $(RR_{Mmg})$ was compared with that measured from inspiratory pressure signal $(RR_{P}).$ Results showed a Pearson's correlation coefficient (r = 0.87) and a good agreement (mean bias = -0.21 with lower and upper limits of -2.33 and 1.89 breaths per minute, respectively) between $RR_{mmg}$ and $RR_{P}$ measurements. In conclusion, this study suggests that RR can be estimated using EMD for extracting respiratory movement from low mechanical activity, during an inspiratory test protocol.
Sponsorship	IEEE Eng. Med. Biol. Soc.
Starting Page	3204
Ending Page	3207
File Size	1030220
Page Count	4
File Format	PDF
ISBN	9781424479290
ISSN	1557170X
DOI	10.1109/EMBC.2014.6944304
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-08-26
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Muscles Accelerometers IP networks Empirical mode decomposition Estimation Biomedical measurement Band-pass filters
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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