Wavelet Analysis in a Canine Model of Gastric Electrical Uncoupling
R. J. Cintra, I. V. Tchervensky, V. S. Dimitrov, M. P. Mintchev

TL;DR
This study introduces a wavelet-based method to analyze electrogastrograms for non-invasive detection of gastric electrical uncoupling, which could aid in diagnosing gastric motility disorders in a canine model.
Contribution
It develops a novel wavelet analysis approach with an optimal wavelet selection technique to distinguish between normal and uncoupled gastric electrical activity.
Findings
Wavelet analysis successfully differentiates basal and uncoupled states.
Optimal wavelet selection improves detection accuracy.
Statistical significance achieved in distinguishing gastric states.
Abstract
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
