Exploiting labels from multiple experts in automated sleep scoring-Reference-Cited by-同舟云学术

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Exploiting labels from multiple experts in automated sleep scoring

Published:2023-02-16 Issue:5 Volume:46 Page:
ISSN:0161-8105
Container-title:SLEEP
language:en
Short-container-title:

Author:

Nasiri Samaneh¹²³⁴,Ganglberger Wolfgang²³⁴^ORCID,Sun Haoqi²³⁴⁵,Thomas Robert J²⁴^ORCID,Westover M Brandon¹²³⁴⁵

Affiliation:

1. Department of Neurology, Massachusetts General Hospital (MGH) , Boston, MA , USA

2. Harvard Medical School , Boston, MA , USA

3. Clinical Data Animation Center (CDAC) , Boston, MA , USA

4. McCance Center for Brain Health, MGH , Boston, MA , USA

5. Department of Medicine, Division of Pulmonary, Critical Care & Sleep, Beth Israel Deaconess Medical Center , Boston, MA , USA

Funder

National Institutes of Health

National Science Foundation

American Academy of Sleep Medicine Foundation

Publisher

Oxford University Press (OUP)

Subject

Physiology (medical),Neurology (clinical)

Link

https://academic.oup.com/sleep/advance-article-pdf/doi/10.1093/sleep/zsad034/49355168/zsad034.pdf

Reference16 articles.

1. Sleep modelled as a continuous and dynamic process predicts healthy ageing better than traditional sleep scoring;Cesari;Sleep Med.,2021

2. Inter-rater agreement in sleep stage classification between centers with different backgrounds;Basner;Somnologie.,2008

3. Sleep staging from electrocardiography and respiration with deep learning;Sun;Sleep.,2020

4. Expert-level sleep scoring with deep neural networks;Biswal;J Am Med Inform Assoc.,2018

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1. Multimodal Mental Health Digital Biomarker Analysis From Remote Interviews Using Facial, Vocal, Linguistic, and Cardiovascular Patterns;IEEE Journal of Biomedical and Health Informatics;2024-03

2. Smart sleep: what to consider when adopting AI-enabled solutions in clinical practice of sleep medicine;Journal of Clinical Sleep Medicine;2023-10

3. Looking for a balance between visual and automatic sleep scoring;npj Digital Medicine;2023-09-05

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