Non-invasive identification of gastric contractions from surface electrogastrogram using back-propagation neural networks

被引：20

作者：

De Chen, Z.J. ^{[1
]}

Lin, Z. ^{[1
]}

Wu, Q. ^{[1
]}

McCallum, R.W. ^{[1
]}

机构：

[1] Medical Center, University of Virginia, Charlottesville, VA 22908, United States

来源：

Medical Engineering and Physics | 1995年 / 17卷 / 03期

关键词：

Accuracy - Back propagation neural networks - Exponential distribution method - Gastric contraction - Gastric motility - Intraluminal manometric probe - Stomach - Surface electrogastrogram;

D O I：

10.1016/1350-4533(95)95713-K

中图分类号：

学科分类号：

摘要：

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).

引用

页码：219 / 225