Robust Methods to Detect Abnormal Initiation in the Gastric Slow Wave from Cutaneous Recordings.

Upper gastrointestinal (GI) disorders are highly prevalent, with gastroparesis (GP) and functional dyspepsia (FD) affecting 3% and 10% of the US population, respectively. Despite overlapping symptoms, differing etiologies of GP and FD have distinct optimal treatments, thus making their management a challenge. One such cause, that of gastric slow wave abnormalities, affects the electromechanical coordination of pacemaker cells and smooth muscle cells in propelling food through the GI tract. Abnormalities in gastric slow wave initiation location and propagation patterns can be treated with novel pacing technologies but are challenging to identify with traditional spectral analyses from cutaneous recordings due to their occurrence at the normal slow wave frequency. This work advances our previous work in developing a 3D convolutional neural network to process multi-electrode cutaneous recordings and successfully classify, in silico, normal versus abnormal slow wave location and propagation patterns. Here, we use transfer learning to build a method that is robust to heterogeneity in both the location of the abnormal initiation on the stomach surface as well as the recording start times with respect to slow wave cycles. We find that by starting with training lowest-complexity models and building complexity in training sets, transfer learning one model to the next, the final network exhibits, on average, 80% classification accuracy in all but the most challenging spatial abnormality location, and below 5% Type-I error probabilities across all locations.

Bayesian inverse methods for spatiotemporal characterization of gastric electrical activity from cutaneous multi-electrode recordings.

Gastrointestinal (GI) problems give rise to 10 percent of initial patient visits to their physician. Although blockages and infections are easy to diagnose, more than half of GI disorders involve abnormal functioning of the GI tract, where diagnosis entails subjective symptom-based questionnaires or objective but invasive, intermittent procedures in specialized centers. Although common procedures capture motor aspects of gastric function, which do not correlate with symptoms or treatment response, recent findings with invasive electrical recordings show that spatiotemporal patterns of the gastric slow wave are associated with diagnosis, symptoms, and treatment response. We here consider developing non-invasive approaches to extract this information. Using CT scans from human subjects, we simulate normative and disordered gastric surface electrical activity along with associated abdominal activity. We employ Bayesian inference to solve the ill-posed inverse problem of estimating gastric surface activity from cutaneous recordings. We utilize a prior distribution on the spatiotemporal activity pertaining to sparsity in the number of wavefronts on the stomach surface, and smooth evolution of these wavefronts across time. We implement an efficient procedure to construct the Bayes optimal estimate and demonstrate its superiority compared to other commonly used inverse methods, for both normal and disordered gastric activity. Region-specific wave direction information is calculated and consistent with the simulated normative and disordered cases. We apply these methods to cutaneous multi-electrode recordings of two human subjects with the same clinical description of motor function, but different diagnosis of underlying cause. Our method finds statistically significant wave propagation in all stomach regions for both subjects, anterograde activity throughout for the subject with diabetic gastroparesis, and retrograde activity in some regions for the subject with idiopathic gastroparesis. These findings provide a further step towards towards non-invasive phenotyping of gastric function and indicate the long-term potential for enabling population health opportunities with objective GI assessment.