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.