Arousal burden is highest in supine sleeping position and during light sleep. Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine STUDY OBJECTIVES:Arousal burden (AB) is defined as the cumulative duration of arousals during sleep divided by the total sleep time. However, in-depth analysis of AB related to sleep characteristics is lacking. Based on previous studies addressing the arousal index (ArI), we hypothesized that the AB would peak in the supine sleeping position and during non-rapid eye movement stage 1 (N1) and show high variability between scorers. METHODS:Nine expert scorers analyzed polysomnography recordings of 50 participants, the majority with an increased risk for obstructive sleep apnea. AB was calculated in different sleeping positions and sleep stages. A generalized estimating equation was utilized to test the association between AB and sleeping positions, sleep stages, and scorers. The correlation between AB and ArI was tested with Spearman's rank-order correlation. RESULTS:AB significantly differed between sleeping positions (<0.001). The median AB in the supine sleeping position was 47-62% higher than in the left and right position. The AB significantly differed between the sleep stages (<0.001); the median AB was more than 200% higher during N1 than during other sleep stages. In addition, the AB differed significantly between scorers (<0.001) and correlated strongly with ArI (=0.935, <0.001). CONCLUSIONS:AB depends on the sleeping position, sleep stage, and scorer as hypothesized. AB behaved similarly as the ArI, but the high variability in the ABs between scorers indicates a potential limitation caused by subjective manual scoring. Thus, the development of more accurate techniques for scoring arousals is required before AB can be reliably utilized. 10.5664/jcsm.11398

Transitioning sleeping position detection in late pregnancy using computer vision from controlled to real-world settings: an observational study. Scientific reports Sleeping on the back after 28 weeks of pregnancy has recently been associated with giving birth to a small-for-gestational-age infant and late stillbirth, but whether a causal relationship exists is currently unknown and difficult to study prospectively. This study was conducted to build a computer vision model that can automatically detect sleeping position in pregnancy under real-world conditions. Real-world overnight video recordings were collected from an ongoing, Canada-wide, prospective, four-night, home sleep apnea study and controlled-setting video recordings were used from a previous study. Images were extracted from the videos and body positions were annotated. Five-fold cross validation was used to train, validate, and test a model using state-of-the-art deep convolutional neural networks. The dataset contained 39 pregnant participants, 13 bed partners, 12,930 images, and 47,001 annotations. The model was trained to detect pillows, twelve sleeping positions, and a sitting position in both the pregnant person and their bed partner simultaneously. The model significantly outperformed a previous similar model for the three most commonly occurring natural sleeping positions in pregnant and non-pregnant adults, with an 82-to-89% average probability of correctly detecting them and a 15-to-19% chance of failing to detect them when any one of them is present. 10.1038/s41598-024-68472-x