Carmina Coronel

Analyses of sleep-related movement disorders have gained importance due to an increase in life expectancy. The present approaches for measuring movements are based on electromyography or accelerometry and provide only local or specific results from muscles/limbs to which sensors have been attached. The motivation of this work was to investigate the detection of a more complete spectrum of sleep-related movements using a three-dimensional (3D) camera instead of the current conventional methods. In contrast to most of the previously published literature, this method allows for the detection of movements even when patients are covered with a blanket. This is the first work to evaluate movement detection with a clinical dataset and replicate the clinical environment in a laboratory setup. The laboratory setup allowed for the characterization of detectable movements through the determination of speed and amplitude limits. We used the Kinect One time-of-flight sensor to record 3D videos. Movements were quantified based on the temporal depth change in these 3D videos. A computer-controlled lifting table allowed for the controlled simulation of movements. Our algorithm detected movements with amplitude values >3.0 mm and velocity values >3.5 mm/s with an F1 score ≥95%. The shortest reliably detected duration of movement was 350 ms. In an ethically approved clinical study including 44 patients, 93.1% of electromyography-detected leg movements were also found in 3D. A significant correlation ($\rho = 0.86$ ) was found between movements detected by the 3D system and polysomnography. The 3D system detected 31.2% more movements than electromyography. In addition to obtaining a broader spectrum of movements not limited to local and muscle/limb-specific movements, the usage of a contactless 3D camera simplifies the recording setup and preserves natural sleeping behavior. The presented 3D system may become useful for diagnostic purposes during sleep studies.

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.