  Abnormal gastric motility function could be related to gastric electrical
uncoupling, the lack of electrical, and respectively mechanical,
synchronization in different regions of the stomach. Therefore, non-invasive
detection of the onset of gastric electrical uncoupling can be important for
diagnosing associated gastric motility disorders. The aim of this study is to
provide a wavelet-based analysis of electrogastrograms (EGG, the cutaneous
recordings of gastric electric activity), to detect gastric electric
uncoupling. Eight-channel EGG recordings were acquired from sixteen dogs in
basal state and after each of two circular gastric myotomies. These myotomies
simulated mild and severe gastric electrical uncoupling, while keeping the
separated gastric sections electrophysiologically active by preserving their
blood supply. After visual inspection, manually selected 10-minute EGG segments
were submitted to wavelet analysis. Quantitative methodology to choose an
optimal wavelet was derived. This "matching" wavelet was determined using the
Pollen parameterization for 6-tap wavelet filters and error minimization
criteria. After a wavelet-based compression, the distortion of the approximated
EGG signals was computed. Statistical analysis on the distortion values allowed
to significantly ($p<0.05$) distinguish basal state from mild and severe
gastric electrical uncoupling groups in particular EGG channels.
