Document Detail

Tissue artifact removal from respiratory signals based on empirical mode decomposition.
MedLine Citation:
PMID:  23325303     Owner:  NLM     Status:  MEDLINE    
On-line measurement of respiration plays an important role in monitoring human physical activities. Such measurement commonly employs sensing belts secured around the rib cage and abdomen of the test object. Affected by the movement of body tissues, respiratory signals typically have a low signal-to-noise ratio. Removing tissue artifacts therefore is critical to ensuring effective respiration analysis. This paper presents a signal decomposition technique for tissue artifact removal from respiratory signals, based on the empirical mode decomposition (EMD). An algorithm based on the mutual information and power criteria was devised to automatically select appropriate intrinsic mode functions for tissue artifact removal and respiratory signal reconstruction. Performance of the EMD-algorithm was evaluated through simulations and real-life experiments (N = 105). Comparison with low-pass filtering that has been conventionally applied confirmed the effectiveness of the technique in tissue artifacts removal.
Shaopeng Liu; Robert X Gao; Dinesh John; John Staudenmayer; Patty Freedson
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Publication Detail:
Type:  Journal Article; Research Support, N.I.H., Extramural     Date:  2013-01-17
Journal Detail:
Title:  Annals of biomedical engineering     Volume:  41     ISSN:  1573-9686     ISO Abbreviation:  Ann Biomed Eng     Publication Date:  2013 May 
Date Detail:
Created Date:  2013-04-12     Completed Date:  2013-11-01     Revised Date:  2014-05-07    
Medline Journal Info:
Nlm Unique ID:  0361512     Medline TA:  Ann Biomed Eng     Country:  United States    
Other Details:
Languages:  eng     Pagination:  1003-15     Citation Subset:  IM    
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MeSH Terms
Automatic Data Processing / methods*
Pulmonary Ventilation*
Respiratory Mechanics*
Respiratory Sounds*
Grant Support
U01 CA130783/CA/NCI NIH HHS; U01 CA130783/CA/NCI NIH HHS

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

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