Blind separation of multichannel electrogastrograms using independent component analysis based on a neural network.

The electrogastrogram (EGG) is an abdominal surface measurement of gastric myo-electrical activity which regulates gastric contractions. It is of great clinical importance to record and analyse multichannel EGGs, which provide more information on the propagation and co-ordination of gastric contractions. EGGs are, however, contaminated by myo-electric interference from other organs and artefacts such as motion and respiration. The aim of the study is to separate the gastric signal from noisy multichannel EGGs without any information on the interference, using independent component analysis. A neural-network model is proposed, and corresponding unsupervised learning algorithms are developed to achieve the separation. The performance of the proposed method is investigated using artificial data simulating real EGG signals. Experimental EGG data are obtained from humans and dogs. The processed results of both simulated and real EGG data show the following: first, the proposed method is able to separate normal gastric slow waves from respiratory artefacts and random noises. It is also able to extract gastric slow waves, even when the EGG is contaminated by severe respiratory and ECG artefacts. Secondly, when the stomach contains various gastric electric signals with different frequencies, the proposed method is able to separate these different signals, as illustrated by simulations. These data suggest that the proposed method can be used to separate gastric slow waves, respiratory and motion artefacts, and intestinal myo-electric interference that are mixed in the EGG. It can also be used to detect gastric slow-wave uncoupling, during which the stomach has multiple gastric signals with different frequencies. It is believed that the proposed method may also be applicable to other biomedical signals.