Wavelet analysis in a canine model of gastric electrical uncoupling

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 16 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 min EGG segments were submitted to wavelet analysis. Quantitative methodology to choose an optimal wavelet was derived. This 'matching' wavelet was determined using the Pollen parametrization 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 us to significantly (p < 0.05) distinguish basal state from mild and severe gastric electrical uncoupling groups in particular EGG channels.