Blind EGG separation using ICA neural networks

Author

Wang, Zhishun ; Zhenya He ; Chen, Z.

Author_Institution

Dept. of Radio Eng., Southeast Univ., Nanjing, China

Volume

3

fYear

1997

fDate

30 Oct-2 Nov 1997

Firstpage

1351

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, electrocardiogram (ECG) and so on, even though no prior information on such contaminating can be obtained, which is closer to practical situations in clinical applications

Keywords

bioelectric potentials; medical signal processing; neural nets; ECG; blind electrogastrogram signal separation technology; clinic applications; contaminated multichannel EGG data; electrodiagnostics; measurement artefacts; motion artefacts; neural network-based independent component analysis; respiratory artefacts; Electrocardiography; Filtering; Frequency; Independent component analysis; Interference; Low pass filters; Neural networks; Noise cancellation; Pollution measurement; Stomach;

fLanguage

English

Publisher

ieee

Conference_Titel

Engineering in Medicine and Biology Society, 1997. Proceedings of the 19th Annual International Conference of the IEEE

Conference_Location

Chicago, IL

ISSN

1094-687X

Print_ISBN

0-7803-4262-3

Type

conf

DOI

10.1109/IEMBS.1997.756627

Filename

756627