Standardized image-based polysomnography database and deep learning algorithm for sleep-stage classification

Differential effects of sleep position and sleep stage on the severity of obstructive sleep apnea

This study compared the effects of sleeping in the supine position and rapid eye movement sleep on the severity of obstructive sleep apnea, and investigated the effect of sleep stage on position-dependent obstructive sleep apnea, and of sleep position on rapid eye movement-dependent obstructive sleep apnea. We analysed epoch-labelled polysomnographic readouts of 3843 patients, and calculated the apnea-hypopnea index for each sleep position and sleep stage. Subgroup analyses were performed to evaluate whether the proportion of position-dependent obstructive sleep apnea patients changed during rapid eye movement and non-rapid eye movement sleep, and whether that of rapid eye movement-dependent obstructive sleep apnea patients changed during supine/lateral sleep. The apnea-hypopnea index was highest in the rapid eye movement-supine position (50.7 ± 22.6 events per hr), followed by non-rapid eye movement-supine, rapid eye movement-lateral and non-rapid eye movement-lateral (39.2 ± 25.3, 22.9 ± 24.4, 15.9 ± 21.9 events per hr, respectively; p < 0.001). Patients with position-dependent obstructive sleep apnea had a higher ratio of rapid eye movement sleep, and those with rapid eye movement-dependent obstructive sleep apnea had a higher ratio of sleep time in the supine position (p < 0.001). During rapid eye movement sleep, position-dependent obstructive sleep apnea was not observed in 21.1% of patients who otherwise had position-dependent obstructive sleep apnea. In the lateral position, 36.9% of patients with rapid eye movement-dependent obstructive sleep apnea did not retain rapid eye movement dependency. Although sleeping in the supine position and rapid eye movement sleep were both associated with more frequent respiratory events, this was the first study to demonstrate that the former had a stronger correlation with obstructive sleep apnea severity. Position dependency in patients with obstructive sleep apnea decreased during rapid eye movement sleep, and worsening of rapid eye movement dependency was alleviated in the lateral position, suggesting potential for personalized obstructive sleep apnea management.

Analysing the impact of body position shift on sleep architecture and stage transition: A comprehensive multidimensional study using event-synchronised polysomnography data

Although understanding the physiological mechanisms of obstructive sleep apnea (OSA) is important for treating OSA, limited studies have examined OSA patients' sleep architecture at the epoch-by-epoch level and analysed the impact of sleep position and stage on OSA pathogenesis. The epoch-labelled polysomnogram was analysed multidimensionally to investigate the effect of sleep position on the sleep architecture and risk factors of apnea in patients with OSA. This retrospective multicentric case-control study reviewed full-night diagnostic polysomnography of 6983 participants. The difference in the proportion of time spent supine during non-rapid eye movement (NREM) and REM stages, and the mean duration of respiratory events per body position were evaluated. The frequency of sleep stage transition per body position shift type was computed. Further subgroup analysis was performed based on OSA severity and positional dependency. Supine time in patients with OSA varied across sleep stages, with lower proportions in N3 and REM, and shorter durations with severity. Patients with OSA spent less time in supine positions during N3 and REM, and experienced longer apnea events in both positions compared to the control group. The frequency of all sleep stage transitions increased with OSA severity and was higher among non-positional OSA than positional OSA and the control group, regardless of body position shift type. The sleep stage transition from N3 and REM to wakefulness was notably heightened during position shift. Understanding the sleep architecture of patients with OSA requires analysing various sleep characteristics including sleep position simultaneously, with future studies focusing on position detection to predict sleep stages and respiratory events.

A Novel Continuous Sleep State Artificial Neural Network Model Based on Multi-Feature Fusion of Polysomnographic Data

Purpose

Sleep structure is crucial in sleep research, characterized by its dynamic nature and temporal progression. Traditional 30-second epochs falter in capturing the intricate subtleties of various micro-sleep states. This paper introduces an innovative artificial neural network model to generate continuous sleep depth value (SDV), utilizing a novel multi-feature fusion approach with EEG data, seamlessly integrating temporal consistency.

Methods

The study involved 50 normal and 100 obstructive sleep apnea-hypopnea syndrome (OSAHS) participants. After segmenting the sleep data into 3-second intervals, a diverse array of 38 feature values were meticulously extracted, including power, spectrum entropy, frequency band duration and so on. The ensemble random forest model calculated the timing fitness value for all the features, from which the top 7 time-correlated features were selected to create detailed sleep sample values ranging from 0 to 1. Subsequently, an artificial neural network (ANN) model was trained to delineate sleep continuity details, unravel concealed patterns, and far surpassed the traditional 5-stage categorization (W, N1, N2, N3, and REM).

Results

The SDV changes from wakeful stage (mean 0.7021, standard deviation 0.2702) to stage N3 (mean 0.0396, standard deviation 0.0969). During the arousal epochs, the SDV increases from the range (0.1 to 0.3) to the range around 0.7, and decreases below 0.3. When in the deep sleep (≤0.1), the probability of arousal of normal individuals is less than 10%, while the average arousal probability of OSA patients is close to 30%.

Conclusion

A sleep continuity model is proposed based on multi-feature fusion, which generates SDV ranging from 0 to 1 (representing deep sleep to wakefulness). It can capture the nuances of the traditional five stages and subtle differences in microstates of sleep, considered as a complement or even an alternative to traditional sleep analysis.