Prediction of gastrointestinal functional state based on myoelectric recordings utilizing a deep neural network architecture.

ABSTRACT: 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. Here we applied automatic feature extraction using a deep neural network architecture on GI myoelectric signals from free-moving ferrets. For each animal, we recorded during baseline control and feeding conditions lasting for 1 h. Data were trained on a 1-dimension

SUBMITTER: Elkhadrawi M 

PROVIDER: S-EPMC10374095 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature