Project Summary
Gastrointestinal (GI) problems are the second leading cause for missing work or school, giving rise to 10
percent of reasons a patient visits their primary care physician and costing $142 billion annually in the US. A
majority of such cases are referred to GI specialists, where endoscopy, imaging, and blood tests allow for easy
diagnosis of blockages and infections. However, more than half of GI disorders involve abnormal
neuromuscular functioning of the GI tract, occurring in a majority of Parkinson’s and diabetes patients for
instance. Diagnosis of such GI disorders typically entails subjective symptom-based questionnaires or
objective but invasive procedures in specialized centers. Symptom-based diagnosis is problematic because
many GI functional disorders with different treatment regimens have overlapping symptoms. Invasive
approaches performed in specialized centers can differentiate between myopathic and neuropathic functional
disorders and can change the diagnosis/treatment of 15% to 20% of patients with upper GI symptoms.
However, they have drawbacks of cost and invasiveness: gastric scintigraphy with its radioactive imaging;
manometry, which involves a catheter inserted through the mouth or nose with fluoroscopic or endoscopic
guidance. Long wait times and intermittent monitoring associated with assessment of neuromuscular GI
disorders, coupled with a strong preference by patients for non-invasive testing instead of current approaches,
pinpoints the non-trivial challenges associated with scaling up GI assessment with specialized centers.
Altogether, the non-existence of an objective, non-invasive, way to monitor functional GI disorders and their
association with transient symptoms is a significant drawback that has vast economic, social, and healthcare
consequences. We have developed and demonstrated a procedure that uses a non-invasive multi-electrode
sensor array along with a suite of statistical signal processing methods that objectively provide wave
propagation descriptions of GI neuromuscular functions that correlate with symptoms. Additionally, using this
multi-electrode array we have developed novel Bayesian inference methods to source localize the gastric slow
wave on the stomach surface. In this project, we will advance our source localization method to reduce the
requirement of human intervention and then apply our method to an existing set of subjects for whom we have
already collected data. This project is an important step towards validation of a quantifiable non-invasive
measure for gastric health that can help modernize functional gastroenterology. It promotes an inexpensive,
non-invasive technology coupled with novel signal processing methods that may lead to transformational
clinical approaches that allow for understanding disease etiology, assessing disease progression, and
predicting treatment response.
