Gastric Slow Wave Modelling Based on Stomach Morphology and Neuronal Firings

TL;DR

This study develops a non-invasive gastric activity model based on EGG signals and stomach morphology in rabbits, aiming to estimate stomach content pH and mass, potentially applicable to humans.

Contribution

The paper introduces a novel EGG-based gastric slow wave model incorporating stomach morphology and neuronal activity, enabling non-invasive assessment of gastric content.

Findings

EGG waveform segments identified using signal processing techniques.

Model correlates EGG features with stomach content pH and mass.

Potential for non-invasive gastric content assessment in humans.

Abstract

Gastric content's mass and pH commonly assessed invasively using endoscopic biopsy, or semi-invasively using swallowable transducer. EGG (electrogastrography) is a technique for observing gastric myoelectrical activity non-invasively, that could be designed as mobile device. In this research, 72 EGG recordings were obtained from 13 local white rabbit (Oryctolagus cuniculus). Recorded EGG processed using SCILAB 5.5.1 package. Signal processing consists of waveform identification altogether with recognition of resting, depolarization, ECA plateau, and repolarization segments of each EGG in the time domain based on amplitude and temporal filter. All rabbits were sacrificed after the recording in order to obtain its stomach content's mass and pH data. EGG waveform generator based on gastric morphological neuron assembly modeled using those data. If this model proved to be accurate, the mass and pH from rabbit (Oryctolagus cuniculus)'s stomach content could be assessed non-invasively, and could be a basis for human (Homo sapiens) trial.