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Elkhadrawi M, Akcakaya M, Fulton S, Yates BJ, Fisher LE, Horn CC. Prediction of gastrointestinal functional state based on myoelectric recordings utilizing a deep neural network architecture. PLoS One 2023; 18:e0289076. [PMID: 37498882 PMCID: PMC10374095 DOI: 10.1371/journal.pone.0289076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Accepted: 07/10/2023] [Indexed: 07/29/2023] Open
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-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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Affiliation(s)
- Mahmoud Elkhadrawi
- Department of Electrical and Computer Engineering, University of Pittsburgh School of Engineering, Pittsburgh, PA, United States of America
| | - Murat Akcakaya
- Department of Electrical and Computer Engineering, University of Pittsburgh School of Engineering, Pittsburgh, PA, United States of America
| | - Stephanie Fulton
- UPMC Hillman Cancer Center, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States of America
| | - Bill J. Yates
- Department of Otolaryngology, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States of America
- Center for Neuroscience, University of Pittsburgh, Pittsburgh, PA, United States of America
| | - Lee E. Fisher
- Rehab Neural Engineering Labs, University of Pittsburgh, Pittsburgh, PA, United States of America
- Department of Physical Medicine & Rehabilitation, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States of America
- Department of Bioengineering, University of Pittsburgh School of Engineering, Pittsburgh, PA, United States of America
| | - Charles C. Horn
- UPMC Hillman Cancer Center, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States of America
- Center for Neuroscience, University of Pittsburgh, Pittsburgh, PA, United States of America
- Division of Hematology/Oncology, Department of Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States of America
- Department of Anesthesiology and Perioperative Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA, United States of America
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Wheeler S, Elkhadrawi M, Stevens B, Wheeler B, Akcakaya M. Machine learning classification of false-positive human immunodeficiency virus screening results. J Pathol Inform 2021; 12:46. [PMID: 34934521 PMCID: PMC8652341 DOI: 10.4103/jpi.jpi_7_21] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Revised: 06/29/2021] [Accepted: 07/13/2021] [Indexed: 11/04/2022] Open
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