UC San Diego

Rates of diagnosis for gastrointestinal (GI) medical conditions have increased markedly in recent years. However, many such diagnoses are in broad, idiopathic categories. Current measures to assess GI function are mostly invasive, which leads to a relative scarcity of expertise and limited clinical visits with rarefied specialists. There is a need for a noninvasive and easily administered technique that provides robust and actionable information to clinicians. Even though the electrogastrogram (EGG) has existed for decades, its use as a diagnostic and research tool has been limited due to inconsistent results that are difficult to interpret.

Recent studies have characterized the occurrence of spatial gastric myoelectric abnormalities that are ignored by typical approaches relying on time-frequency analysis of single channels. Here, we present the high-resolution EGG, which utilizes an array of cutaneous electrodes to estimate the direction and speed of gastric slow-waves. We verified the approach using a forward electrophysiology model of the stomach, demonstrating that an accurate assessment of slow-wave propagation can be made. Also, our experimental results of propagation direction and speed were consistent with serosal recordings of slow-waves described in the literature.

Next, we introduce an approach for artifact removal along with a system for robustly recording the EGG signal in an ambulatory setting for continuous 24-hour intervals. We validated the signal processing techniques with simultaneous EGG and invasive manometry recordings. The 24-hour recordings in free-living subjects revealed daily patterns in gastric motility and apparent gastric emptying after meals.

Finally, we aim to determine the clinical utility of our developed methods. We show that the HR-EGG can detect spatial abnormalities in a patient population with well-characterized gastroparesis. We also provide a case study demonstrating the ability of the ambulatory EGG in identifying gastric outlet obstruction.

The approaches described in this dissertation are non-invasive, full-automated, and inexpensive, enabling use in the development of finer diagnoses and continuous symptom monitoring for gastrointestinal research and clinical applications. By overcoming the limitations of current methods, these tools may unveil new classes of abnormalities, which could lead to a better diagnosis of diseases and inspire novel drugs and therapies, ultimately improving clinical outcomes.