Contactless detection of periodic leg movements during sleep: A 3D video pilot study.
Stefan SeidelHeinrich GarnMarkus GallBernhard KohnChristoph WiesmeyrMarkus WaserCarmina CoronelAndrijana StefanicMarion BöckMarkus WimmerMagdalena MandlBirgit HöglGerhard KlöschPublished in: Journal of sleep research (2020)
In clinical practice, the quality of polysomnographic recordings in children and patients with neurodegenerative diseases may be affected by sensor displacement and diminished total sleep time due to stress during the recording. In the present study, we investigated if contactless three-dimensional (3D) detection of periodic leg movements during sleep was comparable to polysomnography. We prospectively studied a sleep laboratory cohort from two Austrian sleep laboratories. Periodic leg movements during sleep were classified according to the standards of the World Association of Sleep Medicine and served as ground truth. Leg movements including respiratory-related events (A1) and excluding respiratory-related events (A2 and A3) were presented as A1, A2 and A3. Three-dimensional movement analysis was carried out using an algorithm developed by the Austrian Institute of Technology. Fifty-two patients (22 female, mean age 52.2 ± 15.1 years) were included. Periodic leg movement during sleep indexes were significantly higher with 3D detection compared to polysomnography (33.3 [8.1-97.2] vs. 30.7 [2.9-91.9]: +9.1%, p = .0055/27.8 [4.5-86.2] vs. 24.2 [0.00-88.7]: +8.2%, p = .0154/31.8 [8.1-89.5] vs. 29.6 [2.4-91.1]: +8.9%, p = .0129). Contactless automatic 3D analysis has the potential to detect restlessness mirrored by periodic leg movements during sleep reliably and may especially be suited for children and the elderly.
Keyphrases
- sleep quality
- physical activity
- clinical practice
- obstructive sleep apnea
- young adults
- machine learning
- depressive symptoms
- newly diagnosed
- end stage renal disease
- deep learning
- ejection fraction
- risk assessment
- climate change
- capillary electrophoresis
- peritoneal dialysis
- human health
- patient reported outcomes
- neural network
- heat stress
- single molecule