Identification of Slow Wave Propagation in the Multichannel (EGG) Electrogastrographical Signal

The aim of this research is to examine the effectiveness of combining two methods Independent Component Analysis (ICA) and adaptive filtering for identifying the slow waves propagation from cutaneous multichannel electrogastrographical signal (EGG). The 3 cycle per minute (3 cpm) gastric pacesetter potential so-called slow wave is fundamental electrical phenomenon of stomach. Slow waves determine the propagation and maximum frequency of stomach contractions. Appropriate spread of gastric contractions is a key for the correct stomach emptying whereas delay this action causes various gastric disorders, such as bloating, vomiting or unexplained nausea. Parameters depict EGG properties mostly based on spectral analysis and information about slow waves spread and coupling are totaly lost, so new methods for studying slow wave propagation are really desired.

[1]  Jie Liang,et al.  What Can Be Measured from Surface Electrogastrography , 1997 .

[2]  Xuemei Lin,et al.  Detection of gastric slow wave propagation from the cutaneous electrogastrogram. , 1999, American journal of physiology. Gastrointestinal and liver physiology.

[3]  W. C. Alvarez,et al.  The electrogastrogram and what it shows , 1922 .

[4]  Pierre Comon,et al.  Independent component analysis, A new concept? , 1994, Signal Process..

[5]  D. Levanon,et al.  Electrogastrography: its role in managing gastric disorders. , 1998, Journal of pediatric gastroenterology and nutrition.

[6]  Harvey Gould,et al.  Computer simulations , 1990 .

[7]  J. Chen,et al.  Electrogastrography: measuremnt, analysis and prospective applications , 1991, Medical and Biological Engineering and Computing.

[8]  Z. S. Wang,et al.  Blind separation of multichannel electrogastrograms using independent component analysis based on a neural network , 2006, Medical & Biological Engineering & Computing.

[9]  Aapo Hyvärinen,et al.  Survey on Independent Component Analysis , 1999 .

[10]  S. Ward,et al.  Physiology and pathophysiology of the interstitial cell of Cajal: from bench to bedside. I. Functional development and plasticity of interstitial cells of Cajal networks. , 2001, American journal of physiology. Gastrointestinal and liver physiology.

[11]  Erkki Oja,et al.  Independent component analysis: algorithms and applications , 2000, Neural Networks.

[12]  H. Parkman,et al.  Electrogastrography: a document prepared by the gastric section of the American Motility Society Clinical GI Motility Testing Task Force , 2003, Neurogastroenterology and motility : the official journal of the European Gastrointestinal Motility Society.

[13]  Aapo Hyvärinen,et al.  New Approximations of Differential Entropy for Independent Component Analysis and Projection Pursuit , 1997, NIPS.

[14]  H. Liang,et al.  Extraction of gastric slow waves from electrogastrograms: Combining independent component analysis and adaptive signal enhancement , 2005, Medical and Biological Engineering and Computing.

[15]  Jie Liang,et al.  What Can Be Measured from Surface Electrogastrography (Computer Simulations) , 1997, Digestive Diseases and Sciences.