Non-invasive machine learning estimation of effort differentiates sleep-disordered breathing pathology-Reference-Cited by-同舟云学术

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Non-invasive machine learning estimation of effort differentiates sleep-disordered breathing pathology

Published:2019-02-26 Issue:2 Volume:40 Page:025008
ISSN:1361-6579
Container-title:Physiological Measurement
language:
Short-container-title:Physiol. Meas.

Author:

Hanif Umaer,Schneider Logan D^ORCID,Trap Lotte,Leary Eileen B,Moore Hyatt,Guilleminault Christian,Jennum Poul,Sorensen Helge B D,Mignot Emmanuel J M

Publisher

IOP Publishing

Subject

Physiology (medical),Biomedical Engineering,Physiology,Biophysics

Link

http://iopscience.iop.org/article/10.1088/1361-6579/ab0559/pdf

Reference25 articles.

1. The Treatment of Central Sleep Apnea Syndromes in Adults: Practice Parameters with an Evidence-Based Literature Review and Meta-Analyses

2. Use of Chest Wall Electromyography to Detect Respiratory Effort during Polysomnography

3. Sleep-Disordered Breathing and Cardiovascular Risk

4. Effects of Esophageal Pressure Monitoring on Sleep Architecture

Cited by 3 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. Estimation of Apnea-Hypopnea Index Using Deep Learning On 3-D Craniofacial Scans;IEEE Journal of Biomedical and Health Informatics;2021-11

2. Artificial intelligence and sleep: Advancing sleep medicine;Sleep Medicine Reviews;2021-10

3. Troubles respiratoires obstructifs du sommeil et orthodontie. Un entretien avec Christian Guilleminault, Michèle Hervy-Auboiron, Yu-Shu Huang et Kasey Li;L'Orthodontie Française;2019-09

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