Researchers from Across UPMC Publish “Prediction of Gastrointestinal Functional State Based on Myoelectric Recordings Utilizing a Deep Neural Network Architecture”
October 7, 2023
Lee Fisher, PhD, is an associate professor in the UPMC/Pitt Department of Physical Medicine and Rehabilitation and researcher in the Pitt Rehab Neural Engineering Labs. Dr. Fisher, along with colleagues from several other UPMC and Pitt departments, published “Prediction of gastrointestinal functional state based on myoelectric recordings utilizing a deep neural network architecture” in PLOS ONE.
Functional and motility-related gastrointestinal (GI) disorders affect nearly 40% percent of the population. Disturbances of GI myoelectric activity have been proposed to play a significant role in these disorders.
A significant barrier to usage of these signals in diagnosis and treatment is the lack of consistent relationships between GI myoelectric features and function. A potential cause of this issue is the use of arbitrary classification criteria, such as percentage of power in tachygastric and bradygastric frequency bands.
In this study, the team applied automatic feature extraction using a deep neural network architecture on GI myoelectric signals from free-moving ferrets. For each animal, they recorded during baseline control and feeding conditions lasting for 1 h. Data were trained on a 1-dimensional residual convolutional network, followed by a fully connected layer, with a decision based on a sigmoidal output.
For this 2-class problem, accuracy was 90%, sensitivity (feeding detection) was 90%, and specificity (baseline detection) was 89%. By comparison, approaches using hand-crafted features (e.g., SVM, random forest, and logistic regression) produced an accuracy from 54% to 82%, sensitivity from 46% to 84% and specificity from 66% to 80%.
These results suggest that automatic feature extraction and deep neural networks could be useful to assess GI function for comparing baseline to an active functional GI state, such as feeding. In future testing, the current approach could be applied to determine normal and disease-related GI myoelectric patterns to diagnosis and assess patients with GI disease.
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All study contributors
Mahmoud Elkhadrawi
Murat Akcakaya
Stephanie Fulton
Bill J. Yates
Lee E. Fisher
Charles C. Horn
