AIMC Topic: Polysomnography

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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
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...

Towards a Flexible Deep Learning Method for Automatic Detection of Clinically Relevant Multi-Modal Events in the Polysomnogram.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Much attention has been given to automatic sleep staging algorithms in past years, but the detection of discrete events in sleep studies is also crucial for precise characterization of sleep patterns and possible diagnosis of sleep disorders. We prop...

[A Domestic Diagnosis System for Early Restless Legs Syndrome Based on Deep Learning].

Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation
Restless legs syndrome,as a common sleep disorder,has nowadays long been diagnosed by self-rating scale and polysomnography.In this paper,a domestic diagnosis system for early restless legs syndrome based on deep learning is proposed,which is suitabl...

Expert-level sleep scoring with deep neural networks.

Journal of the American Medical Informatics Association : JAMIA
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...

Interactive Sleep Stage Labelling Tool For Diagnosing Sleep Disorder 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
Traditional manual scoring of the entire sleep for diagnosis of sleep disorders is highly time-consuming and dependent to experts experience. Thus, automatic methods based on electrooculography (EOG) analysis have been increasingly attracted attentio...

A Deep Learning Architecture for Temporal Sleep Stage Classification Using Multivariate and Multimodal Time Series.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Sleep stage classification constitutes an important preliminary exam in the diagnosis of sleep disorders. It is traditionally performed by a sleep expert who assigns to each 30 s of the signal of a sleep stage, based on the visual inspection of signa...

Assessment of support vector machines and convolutional neural networks to detect snoring using Emfit mattress.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Snoring (SN) is an essential feature of sleep breathing disorders, such as obstructive sleep apnea (OSA). In this study, we evaluate epoch-based snoring detection methods using an unobtrusive electromechanical film transducer (Emfit) mattress sensor ...

[Study on Sleep Staging Methods Based on Heart Rate Variability Analysis].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
In order to realize sleep staging automatically and conveniently,we used support vector machine(SVM)to analyze the correlation between heart rate variability and sleep stage experimentally.R-R intervals(RRIs)from 33 cases of sleep clinical data of Ti...

Towards a Wireless Smart Polysomnograph Using Symbolic Fusion.

Studies in health technology and informatics
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...

[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
OBJECTIVE: To validate the use of the event detection capabilities in an auto-CPAP system used by patients with sleep apnea hypopnea syndrome (SAHS).