Automated Machine Learning Pipeline Framework for Classification of Pediatric Functional Nausea Using High-resolution Electrogastrogram-Reference-Cited by-同舟云学术

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Automated Machine Learning Pipeline Framework for Classification of Pediatric Functional Nausea Using High-resolution Electrogastrogram

Published:2021 Issue: Volume: Page:1-1
ISSN:0018-9294
Container-title:IEEE Transactions on Biomedical Engineering
language:
Short-container-title:IEEE Trans. Biomed. Eng.

Author:

Olson Joseph D,Somarajan Suseela,Muszynski Nicole,Comstock Andrew H,Hendrickson Kyra Elayne,Scott Lauren,Acra Sari A,Russell Alexandra,Bradshaw Leonard Alan

Funder

Foundation for the National Institutes of Health

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Biomedical Engineering

Link

http://xplorestaging.ieee.org/ielx7/10/4359967/09619917.pdf?arnumber=9619917

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. An autonomous and intelligent hybrid CNN-RNN-LSTM based approach for the detection and classification of abnormalities in brain;Multimedia Tools and Applications;2024-01-02

2. Prediction of gastrointestinal functional state based on myoelectric recordings utilizing a deep neural network architecture;PLOS ONE;2023-07-27

3. Principles and clinical methods of body surface gastric mapping: Technical review;Neurogastroenterology & Motility;2023-03-29

4. Multichannel magnetogastrogram: a clinical marker for pediatric chronic nausea;American Journal of Physiology-Gastrointestinal and Liver Physiology;2022-12-01

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