Sleep Classification With Artificial Synthetic Imaging Data Using Convolutional Neural Networks-Reference-Cited by-同舟云学术

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Sleep Classification With Artificial Synthetic Imaging Data Using Convolutional Neural Networks

Published:2023-01 Issue:1 Volume:27 Page:421-432
ISSN:2168-2194
Container-title:IEEE Journal of Biomedical and Health Informatics
language:
Short-container-title:IEEE J. Biomed. Health Inform.

Author:

Shi Lan¹^ORCID,Wank Marianthie²^ORCID,Chen Yan³,Wang Yibo²^ORCID,Liu Yachuan⁴,Hector Emily C.⁵^ORCID,Song Peter X.K.²^ORCID

Affiliation:

1. Department of Biostatistics, Vanderbilt University, Nashville, TN, USA

2. Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA

3. Department of Statistics, University of Michigan, Ann Arbor, MI, USA

4. School of Information, University of Michigan, Ann Arbor, MI, USA

5. Department of Statistics, North Carolina State University, Raleigh, NC, USA

Funder

University of Michigan

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Health Information Management,Electrical and Electronic Engineering,Computer Science Applications,Health Informatics

Link

http://xplorestaging.ieee.org/ielx7/6221020/10006388/09905731.pdf?arnumber=9905731

Reference20 articles.

1. Using convolutional neural networks for image recognition;hijazi;Cadence Des Syst Inc San Jose CA USA,2015

2. E4 Wristband User's Manual,2020

3. Arrhythmia detection using deep convolutional neural network with long duration ECG signals;y?ld?r?m;Comput Biol Med,2018

4. A deep convolutional neural network model to classify heartbeats

5. Sleep quality prediction in caregivers using physiological signals

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