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

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 lac...

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Autores principales: Elkhadrawi, Mahmoud, Akcakaya, Murat, Fulton, Stephanie, Yates, Bill J., Fisher, Lee E., Horn, Charles C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10374095/
https://www.ncbi.nlm.nih.gov/pubmed/37498882
http://dx.doi.org/10.1371/journal.pone.0289076
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author Elkhadrawi, Mahmoud
Akcakaya, Murat
Fulton, Stephanie
Yates, Bill J.
Fisher, Lee E.
Horn, Charles C.
author_facet Elkhadrawi, Mahmoud
Akcakaya, Murat
Fulton, Stephanie
Yates, Bill J.
Fisher, Lee E.
Horn, Charles C.
author_sort Elkhadrawi, Mahmoud
collection PubMed
description 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-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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spelling pubmed-103740952023-07-28 Prediction of gastrointestinal functional state based on myoelectric recordings utilizing a deep neural network architecture Elkhadrawi, Mahmoud Akcakaya, Murat Fulton, Stephanie Yates, Bill J. Fisher, Lee E. Horn, Charles C. PLoS One Research Article 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-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. Public Library of Science 2023-07-27 /pmc/articles/PMC10374095/ /pubmed/37498882 http://dx.doi.org/10.1371/journal.pone.0289076 Text en © 2023 Elkhadrawi et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Elkhadrawi, Mahmoud
Akcakaya, Murat
Fulton, Stephanie
Yates, Bill J.
Fisher, Lee E.
Horn, Charles C.
Prediction of gastrointestinal functional state based on myoelectric recordings utilizing a deep neural network architecture
title Prediction of gastrointestinal functional state based on myoelectric recordings utilizing a deep neural network architecture
title_full Prediction of gastrointestinal functional state based on myoelectric recordings utilizing a deep neural network architecture
title_fullStr Prediction of gastrointestinal functional state based on myoelectric recordings utilizing a deep neural network architecture
title_full_unstemmed Prediction of gastrointestinal functional state based on myoelectric recordings utilizing a deep neural network architecture
title_short Prediction of gastrointestinal functional state based on myoelectric recordings utilizing a deep neural network architecture
title_sort prediction of gastrointestinal functional state based on myoelectric recordings utilizing a deep neural network architecture
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10374095/
https://www.ncbi.nlm.nih.gov/pubmed/37498882
http://dx.doi.org/10.1371/journal.pone.0289076
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