Chaotic behavior of gastric migrating myoelectrical complex

Nonlinear feedback induces oscillation, whereas dynamic equilibrium between positive and negative nonlinear feedback generates rhythm. Physiological rhythms are central to life. No absolutely stable or periodic rhythm exists in living tissues. It has been extensively reported that many rhythms in human and animal organs, such as the heart and brain, are, in fact chaotic. The aim of this paper is to investigate whether the migrating myoelectrical complex (MMC) of the stomach was chaotic. The study was performed in eight healthy female hound dogs (15-22 kg), implanted with four pairs of bipolar electrodes on the serosa of the stomach along the greater curvature. After the dogs were completely recovered from the surgery, one complete cycle of gastric MMC was recorded from the serosal electrodes. Using Takens' embedding theorem, two parameters reflecting chaotic behavior, the attractor and the Lyapunov exponent of the myoelectrical recording, were reconstructed and computed, respectively. Statistical analysis was performed to investigate the difference in the Lyapunov exponents among different phases of the MMC. The results show that the MMC of the stomach is chaotic. Different phases of the MMC are characterized with different shapes of the attractors and different values of Lyapunov exponents. The characteristic chaotic behavior of the gastric MMC may be utilized for the identification of different phases.

[1]  Zhishun Wang,et al.  Blind separation of slow waves and spikes from gastrointestinal myoelectrical recordings , 2001, IEEE Transactions on Information Technology in Biomedicine.

[2]  Zhishun Wang,et al.  Rhythms and Chaos in the Stomach , 2000 .

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

[4]  Zhishun Wang,et al.  Filter Banks and Neural Network-based Feature Extraction and Automatic Classification of Electrogastrogram , 2004, Annals of Biomedical Engineering.

[5]  A. Bortoff,et al.  Propagation and electrical entrainment of intestinal slow waves , 1972, The American Journal of Digestive Diseases.

[6]  Zhishun Wang,et al.  Optimized Overcomplete Signal Representation and its Applications to Time-frequency Analysis of Electrogastrogram , 1998, Annals of Biomedical Engineering.

[7]  Floris Takens,et al.  On the numerical determination of the dimension of an attractor , 1985 .

[8]  Simon Haykin,et al.  Detection of signals in chaos , 1995, Proc. IEEE.

[9]  N. Britton.  Reaction-diffusion equations and their applications to biology. , 1989 .

[10]  Bruce J West,et al.  Patterns, Information and Chaos in Neuronal Systems , 1993 .

[11]  J. Chen,et al.  Clinical significance of gastric myoelectrical dysrhythmias. , 1995, Digestive diseases.

[12]  A. Wolf,et al.  Determining Lyapunov exponents from a time series , 1985 .

[13]  A. Akin,et al.  Time-frequency methods for detecting spike activity of stomach , 1999, Medical & Biological Engineering & Computing.

[14]  S. Sarna,et al.  Gastric pacemakers. , 1976, Gastroenterology.

[15]  From Clocks to Chaos: The Rhythms of Life , 1988 .