Combined methods for recovery of gastric slow waves from multichannel electrogastrograms

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

Abstract

The electrogastrogram (EGG), a cutaneous measurement of gastric electrical activity, is a mixture of gastric slow waves and noise. To detect the propagation of gastric slow waves, it is desired to obtain multichannel gastric slow waves from multichannel EGGs. In this paper, a combined method for recovery of multichannel gastric signals is presented. This method consists of a novel blind source separation method and an adaptive noise canceller. The main advantage of the proposed method is that no prior knowledge on the structure of sources is needed. The results from both computer simulations and real EGG data demonstrate the combined method is able to recover gastric slow waves from each of multichannel EGGs. Therefore, the proposed method can be used to detect propagation of gastric slow waves from multichannel EGGs.

Original languageEnglish
Title of host publicationProceedings of the 22nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society
PublisherIEEE
Pages1345-1348
Number of pages4
Volume2
ISBN (Print)0780364651
DOIs
Publication statusPublished - Jul 2000
Externally publishedYes
Event22nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Chicago, IL, United States
Duration: 23 Jul 200028 Jul 2000

Publication series

NameAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
PublisherIEEE
ISSN (Print)0589-1019

Conference

Conference22nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Country/TerritoryUnited States
CityChicago, IL
Period23/07/0028/07/00

Keywords

  • Adaptive filter
  • Blind source separation
  • Electrogastrogram
  • Independent component analysis

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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