Blind EGG separation using ICA neural networks

Zhishun Wang, Zhenya He, Jiande Z. Chen

Research output: Contribution to journalConference article

Abstract

Blind Electrogastrogram (EGG) signal separation technology by using neural network-based independent component analysis (ICA) is presented in this paper. The experimental results show that using this technology, the true EGG components can be separated from the multi-channel EGG data contaminated by measurement artefacts, such as respiratory, motion, elcctrocardiogram (ECG) and so on, even though no prior information on such contaminating can be obtained, which is closer to practical situations in clinic applications.

Original languageEnglish (US)
Pages (from-to)1351-1354
Number of pages4
JournalAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Volume3
StatePublished - Dec 1 1997
Externally publishedYes
EventProceedings of the 1997 19th Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Chicago, IL, USA
Duration: Oct 30 1997Nov 2 1997

ASJC Scopus subject areas

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

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