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Artifact reduction in electrogastrogram based on empirical mode decomposition method.

H Liang1, Z Lin, R W McCallum

  • 1Center for Complex Systems & Brain Sciences, Florida Atlantic University, Boca Raton, USA. liang@walt.ccs.fau.edu

Medical & Biological Engineering & Computing
|June 1, 2000
PubMed
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This study introduces a new method using empirical mode decomposition (EMD) to remove artifacts from electrogastrogram (EGG) recordings. This technique effectively cleans EGG data for better analysis and interpretation.

Area of Science:

  • Biomedical Engineering
  • Physiological Measurement
  • Signal Processing

Background:

  • Electrogastrogram (EGG) analysis is challenged by severe signal contamination from respiratory, motion, and cardiac artifacts.
  • Artifacts and myoelectrical activity from other organs complicate EGG interpretation, hindering accurate gastric function assessment.

Purpose of the Study:

  • To propose a generally applicable method for removing diverse artifacts from electrogastrogram (EGG) recordings.
  • To enhance the accuracy and reliability of EGG data analysis through artifact suppression.

Main Methods:

  • Utilized the empirical mode decomposition (EMD) method, an adaptive technique suitable for nonlinear, non-stationary data.
  • Combined EMD with instantaneous frequency analysis for artifact identification and removal.

Related Experiment Videos

Main Results:

  • The proposed method effectively separated and identified various artifactual sources in EGG recordings.
  • Demonstrated successful removal of contamination, significantly improving the quality of EGG signals.

Conclusions:

  • Empirical mode decomposition (EMD) provides a robust solution for artifact removal in electrogastrograms (EGG).
  • This method enhances EGG analysis by improving signal clarity, aiding in the accurate assessment of gastric electrical activity.