Robust Regression and Optimal Transport Methods to Predict Gastrointestinal Disease Etiology From High Resolution EGG and Symptom Severity.

OBJECTIVE: Gastric functional and motility disorders are highly prevalent, with gastroparesis (GP) and functional dyspepsia (FD), affecting 1.5-3% and 10% of the population, respectively. Multiple disease etiologies with overlapping symptoms, such as antral hypomotility, pylorospasm, autonomic dysfunction, and gastric myoelectric dysfunction underlie GP and FD. There is an unmet need to differentiate these etiologies non-invasively to tailor treatment strategies and predict treatment response. METHODS: We performed cutaneous high-resolution electrogastrogram (HR-EGG) recordings on 32 human subjects (controls, GP, and FD) and computed gastric slow wave propagation patterns. We implemented robust regression and clustering methods to identify one group of patients with symptoms well explained by spatial slow wave features and another with symptom severity significantly exceeding predictions from spatial slow wave features. Five patients were re-assessed with validated symptom questionnaires after pyloric and prokinetic interventions. RESULTS: A group of seven patients was identified whose spatial slow wave features lie within the same range as control subjects but whose symptom severity significantly exceeded what is predicted from spatial slow wave features. We hypothesize that gastric myoelectric dysfunction is not a prominent disease etiology in this group. A highly accurate regression holds in the other group of patients (r=0.8). Of the patients with repeat questionnaires, patients with symptom severity exceeding the regression line reported symptom improvement, whereas patients with symptoms in close proximity to the regression line experienced no improvement. CONCLUSION: These findings suggest that patients with symptom severity significantly exceeding the robust regression line have symptoms that cannot be explained by gastric myoelectric dysfunction alone, and vice versa. SIGNIFICANCE: This methodology may provide clinicians with an opportunity to screen patients to determine when existing interventions will be effective, and on the flipside, when slow wave restoration interventions, such as gastric neuromodulation, may be most effective in improving symptoms and quality of life.

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.

A Deep Convolutional Neural Network Approach to Classify Normal and Abnormal Gastric Slow Wave Initiation From the High Resolution Electrogastrogram.

OBJECTIVE: Gastric slow wave abnormalities have been associated with gastric motility disorders. Invasive studies in humans have described normal and abnormal propagation of the slow wave. This study aims to disambiguate the abnormally functioning wave from one of normalcy using multi-electrode abdominal waveforms of the electrogastrogram (EGG). METHODS: Human stomach and abdominal models are extracted from computed tomography scans. Normal and abnormal slow waves are simulated along stomach surfaces. Current dipoles at the stomachs surface are propagated to virtual electrodes on the abdomen with a forward model. We establish a deep convolutional neural network (CNN) framework to classify normal and abnormal slow waves from the multi-electrode waveforms. We investigate the effects of non-idealized measurements on performance, including shifted electrode array positioning, smaller array sizes, high body mass index (BMI), and low signal-to-noise ratio (SNR). We compare the performance of our deep CNN to a linear discriminant classifier using wave propagation spatial features. RESULTS: A deep CNN framework demonstrated robust classification, with accuracy above 90% for all SNR above 0 dB, horizontal shifts within 3 cm, vertical shifts within 6 cm, and abdominal tissue depth within 6 cm. The linear discriminant classifier was much more vulnerable to SNR, electrode placement, and BMI. CONCLUSION: This is the first study to attempt and, moreover, succeed in using a deep CNN to disambiguate normal and abnormal gastric slow wave patterns from high-resolution EGG data. SIGNIFICANCE: These findings suggest that multi-electrode cutaneous abdominal recordings have the potential to serve as widely deployable clinical screening tools for gastrointestinal foregut disorders.