title: Non-invasive identification of gastric contractions from surface electrogastrogram using back-propagation neural networks.
abstract: Gastric contractions play an important role in the digestive process of the stomach. The established method for the measurement of gastric contractions is invasive and involves the insertion through the nose of a manometric probe into the stomach. A non-invasive method is introduced in this paper for the identification of gastric contractions using the surface electrogastrogram. The electrogastrogram (EGG) was measured by placing surface electrodes on the abdominal skin over the stomach in ten subjects. Gastric contractions were simultaneously monitored using an intraluminal manometric probe. The back-propagation neural network was applied to identify gastric contractions from the EGG. The input of the neural network was composed of spectral data points of the EGG which was computed using the exponential distribution method. Experiments were conducted to optimize network structures and parameters. Using the EGG data in five subjects as the training set and the EGG data in another five subjects as the testing set, an overall accuracy of 92% was achieved in the identification of gastric contractions with an optimized three-layer back-propagation neural network (number of nodes for input:hidden:output layers being 64:10:2).
authors.affiliation: University of Virginia Health Science Center, Charlottesville 22908, USA.
authors.forename: J D
authors.initials: JD
authors.lastname: Chen
authors.forename: Z
authors.initials: Z
authors.lastname: Lin
authors.forename: Q
authors.initials: Q
authors.lastname: Wu
authors.forename: R W
authors.initials: RW
authors.lastname: McCallum
mesh_terms: D000328:Adult; D001698:Biomedical Engineering; D004576:Electromyography / Q000379:methods / Q000706:statistics & numerical data; D005260:Female; D005769:Gastrointestinal Motility / Q000502:physiology*; D006801:Humans; D008365:Manometry / Q000379:methods / Q000706:statistics & numerical data; D009119:Muscle Contraction / Q000502:physiology; D016571:Neural Networks, Computer; D012815:Signal Processing, Computer-Assisted