End-to-end Deep Learning of Polysomnograms for Classification of REM Sleep Behavior Disorder-Reference-Cited by-同舟云学术

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End-to-end Deep Learning of Polysomnograms for Classification of REM Sleep Behavior Disorder

Published:2022-07-11 Issue: Volume: Page:
ISSN:
Container-title:2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
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Author:

Brink-Kjaer Andreas¹,Gunter Katarina Mary¹,Mignot Emmanuel²,During Emmanuel³,Jennum Poul⁴,Sorensen Helge B.D.¹

Affiliation:

1. Technical University of Denmark,Department of Health Technology,Kongens Lyngby,Denmark

2. Center for Sleep Sciences and Medicine, Stanford University,CA,USA

3. Stanford University,Department of Psychiatry and Behavioral Sciences,CA,USA

4. Danish Center for Sleep Medicine, Glostrup University Hospital,Glostrup,Denmark

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9870821/9870822/09871576.pdf?arnumber=9871576

Reference17 articles.

1. Detection of REM sleep behaviour disorder by automated polysomnography analysis

2. Video-polysomnography procedures for diagnosis of rapid eye movement sleep behavior disorder (RBD) and the identification of its prodromal stages: guidelines from the International RBD Study Group

3. Predicting Age with Deep Neural Networks from Polysomnograms

4. Normative EMG Values during REM Sleep for the Diagnosis of REM Sleep Behavior Disorder

5. Numerical Optimization

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2. Too little or too much nocturnal movements in Parkinson’s disease: A practical guide to managing the unseen;Clinical Parkinsonism & Related Disorders;2024

3. The treatment efficacy of pharmacotherapies for rapid eye movement sleep behavior disorder with polysomnography evaluation: A systematic review and meta-analysis;Heliyon;2022-11

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