Adaptive independent component analysis of multichannel electrogastrograms.
The electrogastrogram (EGG), a cutaneous measurement of gastric electrical activity, can be severely contaminated by endogenous biological noise sources such as respiratory signal. Therefore it is important to establish effective artifact removal methods. In this paper, a novel blind signal separation method with a flexible non-linearity is introduced and applied to extract the gastric slow wave from multichannel EGGs. Simulation results show that our algorithm is able to separate a wide range of source signals, including mixtures of Gaussian sources. On real data, we demonstrate the successful applications of our procedure to extract the gastric slow wave from multichannel EGGs. As a result, the extracted clean gastric slow wave can be used to facilitate further analysis, e.g. as a reference signal for multichannel adaptive enhancement of the EGG.
Liang
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Center for Complex Systems and Brain Sciences, Florida Atlantic University, P.O. Box 3091, Boca Raton, FL 33431, USA. liang@walt.ccs.fau.edu
D000465:Algorithms; D001698:Biomedical Engineering; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D004576:Electromyography / Q000379:methods* / Q000706:statistics & numerical data; D006801:Humans; D015233:Models, Statistical; D012815:Signal Processing, Computer-Assisted; D013270:Stomach / Q000502:physiology*