Literature DB >> 19163494

Empirical mode decomposition applied to tissue artifact removal from respiratory signal.

Shaopeng Liu1, Qingbo He, Robert X Gao, Patty Freedson.   

Abstract

Estimation of respiration commonly employs piezoelectric sensors secured to rib cage and abdominal belts. However, these respiratory signals are often contaminated by tissue artifact. This paper presents a signal decomposition technique for tissue artifact removal in respiratory signals, based on empirical mode decomposition (EMD). After introducing the theoretical foundation, this method is performed on three synthetic signals, and performance of tissue artifact removal using EMD is compared with low-pass filter and independent component analysis (ICA) techniques. A simulation study and experimental results show that EMD can effectively remove tissue artifact in respiratory signals.

Mesh:

Year:  2008        PMID: 19163494     DOI: 10.1109/IEMBS.2008.4649991

Source DB:  PubMed          Journal:  Conf Proc IEEE Eng Med Biol Soc        ISSN: 1557-170X


  4 in total

1.  Calibrating a novel multi-sensor physical activity measurement system.

Authors:  D John; S Liu; J E Sasaki; C A Howe; J Staudenmayer; R X Gao; P S Freedson
Journal:  Physiol Meas       Date:  2011-08-03       Impact factor: 2.833

2.  Improved regression models for ventilation estimation based on chest and abdomen movements.

Authors:  Shaopeng Liu; Robert Gao; Qingbo He; John Staudenmayer; Patty Freedson
Journal:  Physiol Meas       Date:  2012-01       Impact factor: 2.833

3.  Tissue artifact removal from respiratory signals based on empirical mode decomposition.

Authors:  Shaopeng Liu; Robert X Gao; Dinesh John; John Staudenmayer; Patty Freedson
Journal:  Ann Biomed Eng       Date:  2013-01-17       Impact factor: 3.934

4.  Jump point detection using empirical mode decomposition.

Authors:  Benson S Y Lam; Carisa K W Yu; Siu-Kai Choy; Jacky K T Leung
Journal:  Land use policy       Date:  2016-07-17
  4 in total

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