Contactless recording of sleep apnea and periodic leg movements by nocturnal 3-D-video and subsequent visual perceptive computing
Jazyk angličtina Země Anglie, Velká Británie Médium electronic
Typ dokumentu časopisecké články, práce podpořená grantem
PubMed
31727918
PubMed Central
PMC6856090
DOI
10.1038/s41598-019-53050-3
PII: 10.1038/s41598-019-53050-3
Knihovny.cz E-zdroje
- MeSH
- audiovizuální záznam MeSH
- dospělí MeSH
- lidé středního věku MeSH
- lidé MeSH
- mladý dospělý MeSH
- polysomnografie metody MeSH
- prospektivní studie MeSH
- ROC křivka MeSH
- senioři MeSH
- senzitivita a specificita MeSH
- strojové učení MeSH
- studie případů a kontrol MeSH
- syndrom neklidných nohou diagnóza MeSH
- syndromy spánkové apnoe diagnóza MeSH
- zraková percepce MeSH
- Check Tag
- dospělí MeSH
- lidé středního věku MeSH
- lidé MeSH
- mladý dospělý MeSH
- mužské pohlaví MeSH
- senioři MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
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.
Department of Neurology Bundeswehr Krankenhaus 10115 Berlin Germany
Department of Neurology University of California Irvine Irvine CA United States
International Clinical Research Center St Anne's University Hospital Brno Brno Czech Republic
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