Literature DB >> 20232149

Combined method for reduction of high frequency interferences in surface electroenterogram (EEnG).

Y Ye-Lin1, J Garcia-Casado, G Prats-Boluda, J L Martinez-de-Juan.   

Abstract

Surface electroenterogram (EEnG) recording is a novel technique for monitoring intestinal motility non-invasively. However, surface EEnG recordings are contaminated by cardiac activity, the respiratory artefact, movement artefacts and other types of interference. The goal of this work is to remove electrocardiogram (ECG) interference and movement artefacts from surface EEnG by means of a combined method of empirical mode decomposition and independent component analysis. For this purpose, 11 recording sessions were conducted on animal models. In order to quantify the effectiveness of the proposed method, several parameters were calculated from each session: signal-to-ECG interference ratio (S/I), energy over 2 Hz (EF2) which quantifies the intestinal motility index of external EEnG recording and the variation of EF2. The proposed method removes both ECG interference and movement artefacts from surface EEnG, obtaining a significantly higher S/I ratio and considerably reducing the non-physiological variation of EF2. Furthermore, after applying the combined method, the correlation coefficient between internal recording EF2 and surface recording EF2 rises significantly. The proposed method could therefore be a useful tool to reduce high frequency interference in EEnG recording and to provide more robust non-invasive intestinal motility indexes.

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Year:  2010        PMID: 20232149     DOI: 10.1007/s10439-010-9991-8

Source DB:  PubMed          Journal:  Ann Biomed Eng        ISSN: 0090-6964            Impact factor:   3.934


  4 in total

1.  Empirical mode decomposition and neural network for the classification of electroretinographic data.

Authors:  Abdollah Bagheri; Dominique Persano Adorno; Piervincenzo Rizzo; Rosita Barraco; Leonardo Bellomonte
Journal:  Med Biol Eng Comput       Date:  2014-06-13       Impact factor: 2.602

2.  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

3.  Iterative Covariance-Based Removal of Time-Synchronous Artifacts: Application to Gastrointestinal Electrical Recordings.

Authors:  Jonathan C Erickson; Joy Putney; Douglas Hilbert; Niranchan Paskaranandavadivel; Leo K Cheng; Greg O'Grady; Timothy R Angeli
Journal:  IEEE Trans Biomed Eng       Date:  2016-01-26       Impact factor: 4.538

4.  Automatic identification of motion artifacts in EHG recording for robust analysis of uterine contractions.

Authors:  Yiyao Ye-Lin; Javier Garcia-Casado; Gema Prats-Boluda; José Alberola-Rubio; Alfredo Perales
Journal:  Comput Math Methods Med       Date:  2014-01-09       Impact factor: 2.238

  4 in total

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