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Sleep Apnea Syndromes

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[VOTE versus ACLTE: comparison of two snoring noise classifications using machine learning methods].

HNO Sep 1, 2019
BACKGROUND: Acoustic snoring sound analysis is a noninvasive method for diagnosis of the mechanical mechanisms causing snoring that can be performed during natural sleep. The objective of this work is development and evaluation of classification sche...
Humans Sleep Apnea Syndromes Sound Spectrography Snoring Machine Learning Noise
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Tracheal Sound Analysis Using a Deep Neural Network to Detect Sleep Apnea.

Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine Aug 15, 2019
STUDY OBJECTIVES: Portable devices for home sleep apnea testing are often limited by their inability to discriminate sleep/wake status, possibly resulting in underestimations. Tracheal sound (TS), which can be visualized as a spectrogram, carries inf...
Trachea Respiratory Sounds Sleep Apnea Syndromes Polysomnography Humans Reproducibility of Results Neural Networks, Computer Sensitivity and Specificity Female Middle Aged Male Deep Learning
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Convolutional Neural Networks to Detect Pediatric Apnea-Hypopnea Events from Oximetry.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2019
Pediatric sleep apnea-hypopnea syndrome (SAHS) is a highly prevalent breathing disorder that is related to many negative consequences for the children's health and quality of life when it remains untreated. The gold standard for pediatric SAHS diagno...
Oximetry Neural Networks, Computer Quality of Life Sleep Apnea Syndromes Polysomnography Child Humans
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Sleep Apnea Severity Estimation from Respiratory Related Movements Using Deep Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2019
Sleep apnea is a common chronic respiratory disorder which occurs due to the repetitive complete or partial cessations of breathing during sleep. The gold standard assessment of sleep apnea requires full night polysomnography in a sleep laboratory wh...
Female Aged Adult Humans Deep Learning Sleep Apnea Syndromes Sleep Male Middle Aged Respiration Polysomnography
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Expert-level sleep scoring with deep neural networks.

Journal of the American Medical Informatics Association : JAMIA Dec 1, 2018
OBJECTIVES: Scoring laboratory polysomnography (PSG) data remains a manual task of visually annotating 3 primary categories: sleep stages, sleep disordered breathing, and limb movements. Attempts to automate this process have been hampered by the com...
Sleep Stages Classification Polysomnography Humans Electroencephalography Neural Networks, Computer Sleep Sleep Apnea Syndromes Models, Biological Machine Learning Datasets as Topic
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Robotics in oral and maxillofacial surgery.

Annals of the Royal College of Surgeons of England May 1, 2018
How trans-oral robotic surgery can treat cancer in the oropharyngyal space
Robotics Robotic Surgical Procedures Organ Sparing Treatments Surgery, Oral Sleep Apnea Syndromes Length of Stay Time Factors Humans Oropharyngeal Neoplasms Incidence Blood Loss, Surgical Postoperative Complications Oropharynx
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Towards a Wireless Smart Polysomnograph Using Symbolic Fusion.

Studies in health technology and informatics Jan 1, 2016
Polysomnography is the gold standard test for sleep disorders among which the Sleep Apnea Syndrome (SAS) is considered a public health issue because of the increase of the cardio-and cerebro-vascular risk it is associated with. However, the reliabili...
Equipment Design Humans Reproducibility of Results Sleep Apnea Syndromes Wireless Technology Sensitivity and Specificity Artificial Intelligence Pattern Recognition, Automated Equipment Failure Analysis Diagnosis, Computer-Assisted Polysomnography Decision Support Systems, Clinical
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[Validation of the advanced event detection in patients with sleep apnea hypopnea syndrome using auto-CPAP treatment].

Zhonghua jie he he hu xi za zhi = Zhonghua jiehe he huxi zazhi = Chinese journal of tuberculosis and respiratory diseases Nov 1, 2015
OBJECTIVE: To validate the use of the event detection capabilities in an auto-CPAP system used by patients with sleep apnea hypopnea syndrome (SAHS).
Continuous Positive Airway Pressure Sleep Humans Polysomnography Sleep Apnea Syndromes
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