Polysomnography
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Last Posted: Jun 25, 2024
- OSApredictor: A tool for prediction of moderate to severe obstructive sleep apnea-hypopnea using readily available patient characteristics.
Amlan Talukder et al. Comput Biol Med 2024 178108777 - Sleep assessment using EEG-based wearables - A systematic review.
C J de Gans et al. Sleep Med Rev 2024 76101951 - A Machine Learning Prediction Model of Adult Obstructive Sleep Apnea Based on Systematically Evaluated Common Clinical Biochemical Indicators.
Jiewei Huang et al. Nat Sci Sleep 2024 16413-428 - Enhanced sleep staging with artificial intelligence: a validation study of new software for sleep scoring.
Massimiliano Grassi et al. Front Artif Intell 2023 61278593 - Belun Ring (Belun Sleep System BLS-100): Deep learning-facilitated wearable enables obstructive sleep apnea detection, apnea severity categorization, and sleep stage classification in patients suspected of obstructive sleep apnea.
Zachary Strumpf et al. Sleep Health 2023 - Benchmarking performance of an automatic polysomnography scoring system in a population with suspected sleep disorders.
Bryan Peide Choo et al. Frontiers in neurology 2023 141123935 - Design and Conceptual Proposal of an Intelligent Clinical Decision Support System for the Diagnosis of Suspicious Obstructive Sleep Apnea Patients from Health Profile.
Manuel Casal-Guisande et al. International journal of environmental research and public health 2023 20(4) - Diagnosis of obstructive sleep apnea in children based on the XGBoost algorithm using nocturnal heart rate and blood oxygen feature.
Pengfei Ye et al. American journal of otolaryngology 2023 44(2) 103714 - Prediction model of obstructive sleep apnea-related hypertension: Machine learning-based development and interpretation study.
Shi Yewen et al. Frontiers in cardiovascular medicine 2022 91042996 - A Machine Learning Approach for Detecting Idiopathic REM Sleep Behavior Disorder.
Salsone Maria et al. Diagnostics (Basel, Switzerland) 2022 12(11) - A Deep Learning Framework for Automatic Sleep Apnea Classification Based on Empirical Mode Decomposition Derived from Single-Lead Electrocardiogram.
Setiawan Febryan et al. Life (Basel, Switzerland) 2022 12(10) - Sleep classification using Consumer Sleep Technologies and AI: A review of the current landscape.
Djanian Shagen et al. Sleep medicine 2022 100390-403 - Enabling Early Obstructive Sleep Apnea Diagnosis With Machine Learning: Systematic Review.
Ferreira-Santos Daniela et al. Journal of medical Internet research 2022 24(9) e39452 - Logistic regression and artificial neural network-based simple predicting models for obstructive sleep apnea by age, sex, and body mass index.
Kuan Yi-Chun et al. Mathematical biosciences and engineering : MBE 2022 19(11) 11409-11421 - Automated sleep scoring system using multi-channel data and machine learning.
Arslan Recep Sinan et al. Computers in biology and medicine 2022 146105653 - Obstructive Sleep Apnoea Syndrome Screening Through Wrist-Worn Smartbands: A Machine-Learning Approach.
Benedetti Davide et al. Nature and science of sleep 2022 14941-956 - Barriers of artificial intelligence implementation in the diagnosis of obstructive sleep apnea.
Brennan Hannah L et al. Journal of otolaryngology - head & neck surgery = Le Journal d'oto-rhino-laryngologie et de chirurgie cervico-faciale 2022 51(1) 16 - Diagnosis of Sleep Apnoea Using a Mandibular Monitor and Machine Learning Analysis: One-Night Agreement Compared to in-Home Polysomnography.
Kelly Julia L et al. Frontiers in neuroscience 2022 16726880 - Acoustic Screening for Obstructive Sleep Apnea in Home Environments Based on Deep Neural Networks.
Romero Hector E et al. IEEE journal of biomedical and health informatics 2022 PP - Deep Learning Application to Clinical Decision Support System in Sleep Stage Classification.
Kim Dongyoung et al. Journal of personalized medicine 2022 12(2) - Detecting obstructive sleep apnea by craniofacial image-based deep learning.
He Shuai et al. Sleep & breathing = Schlaf & Atmung 2022 - Automated Scoring of Respiratory Events in Sleep with a Single Effort Belt and Deep Neural Networks.
Nassi Thijs-Enagnon et al. IEEE transactions on bio-medical engineering 2021 PP - Investigation of Machine Learning and Deep Learning Approaches for Detection of Mild Traumatic Brain Injury from Human Sleep Electroencephalogram.
Vishwanath Manoj et al. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference 2021 20216134-6137 - Towards Validating the Effectiveness of Obstructive Sleep Apnea Classification from Electronic Health Records Using Machine Learning.
Ramesh Jayroop et al. Healthcare (Basel, Switzerland) 2021 9(11) - Integrating domain knowledge with machine learning to detect obstructive sleep apnea: Snore as a significant bio-feature.
Hsu Yu-Ching et al. Journal of sleep research 2021 e13487 - RobustSleepNet: Transfer learning for automated sleep staging at scale.
Guillot Antoine et al. IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society 2021 PP - Deep Learning for Diagnosis and Classification of Obstructive Sleep Apnea: A Nasal Airflow-Based Multi-Resolution Residual Network.
Yue Huijun et al. Nature and science of sleep 2021 13361-373 - Development of digital measures for nighttime scratch and sleep using wrist-worn wearable devices.
Mahadevan Nikhil et al. NPJ digital medicine 2021 4(1) 42 - Deep Neural Network Sleep Scoring Using Combined Motion and Heart Rate Variability Data.
Haghayegh Shahab et al. Sensors (Basel, Switzerland) 2020 Dec 21(1) - A New Wearable System for Home Sleep Apnea Testing, Screening, and Classification.
Manoni Alessandro et al. Sensors (Basel, Switzerland) 2020 Dec 20(24)
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About HLBS-PopOmics
HLBS-PopOmics is an online, continuously updated, searchable database of published scientific literature, CDC and NIH resources, and other materials that address the translation of genomic and other precision health discoveries into improved health care and prevention related to Heart and Vascular Diseases(H), Lung Diseases(L), Blood Diseases(B), and Sleep Disorders(S)...more
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Site Citation:
Mensah GA, Yu W, Barfield WL, Clyne M, Engelgau MM, Khoury MJ. HLBS-PopOmics: an online knowledge base to accelerate dissemination and implementation of research advances in population genomics to reduce the burden of heart, lung, blood, and sleep disorders. Genet Med. 2018 Sep 10. doi: 10.1038/s41436-018-0118-1
Disclaimer: Articles listed in the Public Health Knowledge Base are selected by Public Health Genomics Branch to provide current awareness of the literature and news. Inclusion in the update does not necessarily represent the views of the Centers for Disease Control and Prevention nor does it imply endorsement of the article's methods or findings. CDC and DHHS assume no responsibility for the factual accuracy of the items presented. The selection, omission, or content of items does not imply any endorsement or other position taken by CDC or DHHS. Opinion, findings and conclusions expressed by the original authors of items included in the update, or persons quoted therein, are strictly their own and are in no way meant to represent the opinion or views of CDC or DHHS. References to publications, news sources, and non-CDC Websites are provided solely for informational purposes and do not imply endorsement by CDC or DHHS.
- Page last reviewed:Feb 1, 2024
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