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

• Published in: Scientific reports Vol. 9; no. 1; pp. 16812 - 11
• Main Authors: Veauthier, Christian, Ryczewski, Juliane, Mansow-Model, Sebastian, Otte, Karen, Kayser, Bastian, Glos, Martin, Schöbel, Christoph, Paul, Friedemann, Brandt, Alexander U., Penzel, Thomas
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 14.11.2019; Nature Publishing Group
• Subjects: 631/1647/48; 631/57/2266; Adult; Aged; Apnea; Cameras; Case-Control Studies; Female; Humanities and Social Sciences; Humans; Learning algorithms; Leg; Machine Learning; Male; Middle Aged; multidisciplinary; Polysomnography - methods; Prospective Studies; Restless legs syndrome; Restless Legs Syndrome - diagnosis; ROC Curve; Science; Science (multidisciplinary); Sensitivity and Specificity; Sleep; Sleep apnea; Sleep Apnea Syndromes - diagnosis; Sleep disorders; Visual Perception; Young Adult
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-019-53050-3

Contactless 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.