scholarly journals Iron Stores, Periodic Leg Movements, and Sleepiness in Obstructive Sleep Apnea

2009 ◽  
Vol 05 (06) ◽  
pp. 525-531 ◽  
Author(s):  
Louise M. O'Brien ◽  
Julie Koo ◽  
Ludi Fan ◽  
Jocelynn T. Owusu ◽  
Wattanachai Chotinaiwattarakul ◽  
...  
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Periodic Leg Movements ◽  
Iron Stores ◽  
Obstructive Sleep ◽  
Leg Movements

scholarly journals Contactless recording of sleep apnea and periodic leg movements by nocturnal 3-D-video and subsequent visual perceptive computing

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Christian Veauthier ◽  
Juliane Ryczewski ◽  
Sebastian Mansow-Model ◽  
Karen Otte ◽  
Bastian Kayser ◽  
...  
Keyword(s):  
Sleep Apnea ◽  
Characteristic Curve ◽  
Apnea Hypopnea Index ◽  
Periodic Leg Movements ◽  
Respiratory Events ◽  
Obstructive Sleep ◽  
Machine Learning Approach ◽  
Diagnostic Benefit ◽  
Test Probability ◽  
Leg Movements

AbstractContactless measurements during the night by a 3-D-camera are less time-consuming in comparison to polysomnography because they do not require sophisticated wiring. However, it is not clear what might be the diagnostic benefit and accuracy of this technology. We investigated 59 persons simultaneously by polysomnography and 3-D-camera and visual perceptive computing (19 patients with restless legs syndrome (RLS), 21 patients with obstructive sleep apnea (OSA), and 19 healthy volunteers). There was a significant correlation between the apnea hypopnea index (AHI) measured by polysomnography and respiratory events measured with the 3-D-camera in OSA patients (r = 0.823; p < 0.001). The receiver operating characteristic curve yielded a sensitivity of 90% for OSA with a specificity of 71.4%. In RLS patients 72.8% of leg movements confirmed by polysomnography could be detected by 3-D-video and a significant moderate correlation was found between PLM measured by polysomnography and by the 3-D-camera (RLS: r = 0.654; p = 0.004). In total, 95.4% of the sleep epochs were correctly classified by the machine learning approach, but only 32.5% of awake epochs. Further studies should investigate, if this technique might be an alternative to home sleep testing in persons with an increased pre-test probability for OSA.

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