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Summary: Given the significant impact of sleep on overall health, radar technology offers a promising, non‐invasive, and cost‐effective avenue for the early detection of sleep disorders, even prior to relying on polysomnography (PSG)‐based classification. In this study, we employed an attention‐based bidirectional long short‐term memory (Attention Bi‐LSTM) model to accurately predict sleep stages using 60 GHz frequency‐modulated continuous‐wave (FMCW) radar. Our dataset comprised 78 participants from an ongoing obstructive sleep apnea (OSA) cohort, recruited between July 2021 and November 2022, who underwent overnight polysomnography alongside radar sensor monitoring. The dataset encompasses comprehensive polysomnography recordings, spanning both sleep and wakefulness states. The predictions achieved a Cohen's kappa coefficient of 0.746 and an overall accuracy of 85.2% in classifying wakefulness, rapid‐eye‐movement (REM) sleep, and non‐REM (NREM) sleep (N1 + N2 + N3). The results demonstrated that the models incorporating both Radar 1 and Radar 2 data consistently outperformed those using only Radar 1 data, indicating the potential benefits of utilising multiple radars for sleep stage classification. Although the performance of the models tended to decline with increasing OSA severity, the addition of Radar 2 data notably improved the classification accuracy. These findings demonstrate the potential of radar technology as a valuable screening tool for sleep stage classification. [ABSTRACT FROM AUTHOR]

Summary: Determining sleep stages accurately is an important part of the diagnostic process for numerous sleep disorders. However, as the sleep stage scoring is done manually following visual scoring rules there can be considerable variation in the sleep staging between different scorers. Thus, this study aimed to comprehensively evaluate the inter‐rater agreement in sleep staging. A total of 50 polysomnography recordings were manually scored by 10 independent scorers from seven different sleep centres. We used the 10 scorings to calculate a majority score by taking the sleep stage that was the most scored stage for each epoch. The overall agreement for sleep staging was κ = 0.71 and the mean agreement with the majority score was 0.86. The scorers were in perfect agreement in 48% of all scored epochs. The agreement was highest in rapid eye movement sleep (κ = 0.86) and lowest in N1 sleep (κ = 0.41). The agreement with the majority scoring varied between the scorers from 81% to 91%, with large variations between the scorers in sleep stage‐specific agreements. Scorers from the same sleep centres had the highest pairwise agreements at κ = 0.79, κ = 0.85, and κ = 0.78, while the lowest pairwise agreement between the scorers was κ = 0.58. We also found a moderate negative correlation between sleep staging agreement and the apnea–hypopnea index, as well as the rate of sleep stage transitions. In conclusion, although the overall agreement was high, several areas of low agreement were also found, mainly between non‐rapid eye movement stages. [ABSTRACT FROM AUTHOR]

Summary: Obstructive sleep apnea (OSA) is a highly prevalent condition, resulting in recurrent hypoxic events, sleep arousal, and daytime sleepiness. Patients with OSA are at an increased risk of cardiovascular morbidity and mortality. The mechanisms underlying the development of cardiovascular disease in OSA are multifactorial and cause a cascade of events. The primary contributing factor is sympathetic overactivity. Heart rate variability (HRV) can be used to evaluate shifts in the autonomic nervous system, during sleep and in response to treatment in patients with OSA. Newer technologies are aimed at improving HRV analysis to accelerate processing time, improve the diagnosis of OSA, and detection of cardiovascular risk. The present review will present contemporary understandings and uses for HRV, specifically in the realms of physiology, technology, and clinical management. [ABSTRACT FROM AUTHOR]

We examined the relationships between sleep and inflammatory biomarkers during late pregnancy. Seventy‐four women underwent an overnight sleep assessment by polysomnography. Blood samples were collected before bedtime and again within 1 h upon awakening to measure C‐reactive protein (CRP), interleukin (IL)‐6, and IL‐6 soluble receptor. Sleep parameters included variables characterizing sleep architecture and sleep continuity. The participants were 32.2 (SD = 4.1) years old, and the average gestational age was 32.8 (3.5) weeks. Controlling for covariates, evening CRP was negatively associated with N3 sleep (β = −0.30, P = 0.010). N3 sleep was also negatively associated with morning CRP (β = −0.26, P = 0.036), with a higher percentage of N3 sleep associated with a lower level of morning CRP. Contrarily, there was a tendency for a positive association between stage N2 sleep and morning CRP (β = 0.23, P = 0.065). Stage N1 sleep was associated with morning IL‐6 (β = 0.28, P = 0.021), with a higher percentage of N1 sleep associated with a higher morning IL‐6. No significant associations were found between morning inflammatory biomarkers and sleep continuity parameters. In conclusion, increased light sleep was associated with increased inflammatory biomarkers, whereas more deep sleep was associated with decreased inflammatory biomarkers. These findings further support the interactions between sleep and the immune system during late pregnancy. [ABSTRACT FROM AUTHOR]

Background Short sleep duration is a contributing factor for decreased insulin sensitivity and hyperglycemia. Sleep architecture represents a cyclical pattern of sleep that shifts between sleep Stages N1, N2, N3 (slow wave sleep) and Stage R (rapid eye movement sleep). The aim of the present study was to examine the association between sleep architecture and glucose and insulin metabolism in both normal weight and overweight/obese children and adolescents. Methods A total of 118 subjects participated in the study. Subjects underwent overnight polysomnography ( PSG) when the percentage of total sleep time (% TST) spent at each sleep stage was recorded and an oral glucose tolerance test together was performed the next morning. We assessed glucose tolerance, insulin sensitivity and pancreatic β-cell function using 2-h glucose levels, the Matsuda index ( ISOGTT), and insulin secretion-sensitivity index-2 ( ISSI-2), respectively. Results After adjustment for age, gender, body mass index z-score, pubertal status, and obstructive apnea hypopnea index, Stage N3 (% TST) was positively associated with ISOGTT, whereas Stage N1 (% TST) exerted an opposite effect on ISOGTT. Higher sleep efficiency and longer TST were independently associated with lower 2-h glucose levels, higher ISSI-2 and/or higher ISOGTT. Conclusions Stage N3, sleep efficiency and TST were protective factors in maintaining glucose and insulin homeostasis; however, Stage N1 functioned in the opposite direction. [ABSTRACT FROM AUTHOR]

Snoring is considered one of the hallmarks of sleep-disordered breathing, but its determinants remain obscure in both obstructive sleep apnoea (apnoeic) and non-apnoeic snorers. We aimed to document positional dependency of snoring along with its association with clinical and polysomnographic variables. Seventy-seven apnoeic and 27 non-apnoeic snorers who complained for every-night loud snoring and slept in supine and lateral positions in all sleep stages during overnight polysomnography were included. Snoring (i.e. sound intensity > 40 dB) was quantified by measuring the mean and maximum sound intensity, and snoring frequency. In apnoeic and non-apnoeic snorers, mean snoring intensity and snoring frequency were higher in supine than in lateral positions irrespective of sleep stage, and were also usually higher in N3 in comparison to rapid eye movement and/or N2 sleep stage in any given position. Positional change in snoring intensity as expressed by the ratio of mean intensity in the supine to lateral positions was independently and positively correlated with body mass index, tonsils size and age in the total of patients. Snoring is more prominent in the supine position and in N3 sleep stage in apnoeic and non-apnoeic snorers. Snoring positional dependence is determined by body mass index, tonsils size and age. [ABSTRACT FROM AUTHOR]

We examined the effects of sleep stages and sleep-disordered breathing (SDB) on autonomic modulation in 700 children. Apnea hypopnea index (AHI) during one 9 h night-time polysomnography was used to define SDB. Sleep stage-specific autonomic modulation was measured by heart rate variability (HRV) analysis of the first available 5 min RR intervals from each sleep stage. The mean [standard deviation (SD)] age was 112 (21) months (49% male and 25% non-Caucasian). The average AHI was 0.79 (SD = 1.03) h−1, while 73.0%, 25.8% and 1.2% of children had AHI <1 (no SDB), 1–5 (mild SDB) and ≥5 (moderate SDB), respectively. In the no SDB group, the high frequency (HF) and root mean square SD (RMSSD) increased significantly from wake to Stage 2 and slow wave sleep (SWS), and then decreased dramatically when shifting into rapid eye movement (REM) sleep. In the moderate SDB group, the pattern of HRV shift was similar to that of no SDB. However, the decreases in HF and RMSSD from SWS to REM were more pronounced in moderate SDB children [between-group differences in HF (−24% in moderate SDB versus −10% in no SDB) and RMSSD (−27% versus −12%) were significant ( P < 0.05)]. The REM stage HF is significantly lower in the moderate SDB group compared to the no SDB group [mean (standard error): 4.49 (0.43) versus 5.80 (0.05) ms2, respectively, P < 0.05]. Conclusions are that autonomic modulation shifts significantly towards higher parasympathetic modulation from wake to non-rapid eye movement sleep, and reverses to a less parasympathetic modulation during REM sleep. However, the autonomic modulation is impaired among children with moderate SDB in the directions of more reduction in parasympathetic modulation from SWS to REM sleep and significantly weaker parasympathetic modulation in REM sleep, which may lead to higher arrhythmia vulnerability, especially during REM sleep. [ABSTRACT FROM AUTHOR]

There have been many trials in which the waveform recognition method, which is intended to replace expert observation by computer processing, has been used to extract the features of biological signals during sleep, and to automatically score sleep stages based on feature parameters. This paper proposes a waveform recognition method which extracts the feature parameters based on the characteristics of the biological signal during sleep, and a method of automatic sleep stage scoring by decision-tree learning, which is currently considered to be one of the most successful machine learning methods in practice. As the first step in the method, the features corresponding to the state of the EEG, the EOG, and the EMG during sleep are compared to the features of characteristic waves such as the α-wave, δ-wave, sleep spindle, K-complex, and REM, and the feature parameters needed in order to judge the sleep stage are extracted. Using canonical discriminant analysis and discretization method RWS based on the random walk, the feature parameters are converted to a small number of discrete variables. Based on training instances, obtained by the bootstrap method, a set of multiple small decision trees (a committee) is formed, and the sleep stage is scored by majority decision in the classification results. The method is applied to the PSG chart digital data provided by the Japan Sleep Society, and the performance of the system is evaluated experimentally. It is verified that the proposed method is promising as a method of automatic sleep stage scoring with high accuracy, requiring little expenditure of time in learning and classification. © 2002 Wiley Periodicals, Inc. Syst Comp Jpn, 33(11): 1–13, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/scj.10248 [ABSTRACT FROM AUTHOR]

Summary Studies have shown that next-day performance and alertness are impaired by sleep fragmentation procedures even when total sleep time (TST) is unaffected. Based on these studies it has been hypothesized that both the duration and continuity of sleep determine its recuperative value. This review of the literature suggests that when sleep fragmentation procedures increase the relative amount of stage 1 sleep, next-day performance and alertness are impaired. Other studies suggest that stage 1 sleep has little or no recuperative value. Total sleep time, however, is typically defined as the sum of time spent in sleep stages 1, 2, 3, 4, and REM. In the present paper it is shown that when stage 1 sleep is excluded from TST, a stronger relationship between TST and subsequent alertness and performance emerges – and the need to invoke ‘sleep continuity’ as a variable that contributes independently to recuperative sleep processes is obviated. In the same way that partial or total sleep deprivation impairs alertness and performance, it is proposed that sleep disruption also impairs alertness and performance by reducing true recuperative sleep time. [ABSTRACT FROM AUTHOR]

Polygraph recordings were conducted with normal subjects who were not sleep-deprived to examine the association between EEG alpha dynamics and a passive behavioral index of sleep/wake status, and to assess the usefulness of that index in subjects who do not produce abundant wakeful alpha activity. The behavioral indicator of sleep onset comprised the depression of a telegraph key initiated by loss of extensor tension in the finger. Alpha abundant (high-alpha) subjects showed a strong association of alpha level (as sleep Stage Wake (W) vs. Stage 1 sleep) with behavioral level, and a strong association of alpha loss events with key closure events. Mean latencies between alpha loss and key closure varied from -1.8 to 18.4 s with a median of 1.0 s. As expected, high-alpha subjects also showed significant correlations between alpha level and changes in peripheral physiological variables in the vicinity of sustained alpha losses. Compared with Stage W, Stage 1 was associated with a greater incidence of slow eye movements, lower abdominal breathing amplitude, and a higher thoracic:abdominal breathing ratio. Similar correlations were observed in both high-alpha and low-alpha subjects between these variables and the key index, supporting its potential generality as an indicator of sleep onset. [ABSTRACT FROM AUTHOR]

Auditory average evoked responses (AERs) to clicks ranging from SO to 80 dB were studied in 9 normal adults while awake and during sleep. AER amplitude tended to increase little from 50 to 80 dB in waking subjects but increased markedly in sleeping subjects during stages 3 and 4. Rapid eye movement (REM) and stage 1 sleep had small amplitude AERs in comparison with other sleep stages. Individuals who showed decreases in amplitude at high intensities while awake slept significantly longer during the experimental nights. [ABSTRACT FROM AUTHOR]

To clarify the thermally comfortable environment for satisfied sleep, we investigated the relationship between skin temperatures and sleep stages during night-sleep in winter, summer and autumn. Four healthy young females served as the subjects. The experiments were performed in a climate chamber under three conditions: at 13°C-RH70% with futon and blanket in winter, at 20–18°C-RH60% with futon and towelket in autumn and at 29°C-RH70% with towelket in summer. The subjects’ skin temperature on the sole was lower until about 4 hours after lights off in winter than in autumn and summer, and the percentage of st.2 was lower and st.4 was higher in winter than in autumn and summer, although the bed climate both in winter and autumn was much the same. [ABSTRACT FROM AUTHOR]

A sleep electroencephalogram was analyzed by non-linear analysis. The polysomnography of a healthy male subject was analyzed and the correlation dimensions calculated. The correlation dimensions decreased from the ‘awake’ stage to sleep stages 1–3 and increased during rapid eye movement (REM) sleep. These results were seen during each sleep cycle. In each sleep cycle, the correlation dimensions decrease for slow wave sleep, and increase for REM sleep. [ABSTRACT FROM AUTHOR]

Abstract The interhemispheric differences of the correlation dimension (D2 ) in the sleep electroencephalogram (EEG) of eight healthy right-handed students was investigated. During slow wave sleep (SWS) the D2 of the central EEG and the temporal left hemisphere (LH) EEG were significantly higher than those in the right hemisphere (RH) EEG; but during rapid eye movement (REM) sleep, the D2 of the central EEG and the occipital RH EEG were significantly higher. The D2 of EEG in the left temporal site during REM sleep were significantly higher than in the right during the first and third sleep cycles, but these were significantly lower during the fourth and fifth sleep cycles. During REM sleep, temporal brain activity may shift from the LH to the RH as morning approaches. [ABSTRACT FROM AUTHOR]

Abstract The polysomnogram (PSG), blood melatonin concentration, rectal temperature, and answers to a self-evaluation questionnaire about the effects of agarwood (jinkoh in Japanese), which has been reported to have sleep-promoting effects were examined. The subjects tested were male medical students, who were free of otorhinolaryngological diseases and were non-smokers. The results of sleep stage and other sleep variables, and those of rectal temperature rhythm and blood melatonin concentrations showed no significant differences between the baseline nights, experimental nights, and recovery nights. [ABSTRACT FROM AUTHOR]

Abstract We examined the differences between the results of an automatic sleep analysis system and inspection decision. Subjects were 10 males (average age 21.6 years). One section consists of 20 s records. The sections that deviated from the algorithm could not be decided. Each sleep stage decided by automatic analysis was compared with the inspection decision. The agreement ratio of stage 3 was 91.6% in the highest, and followed by stage 2, stage 4, stage W and stage 1. The lowest was 62.5% for movement time. The total agreement ratio was 85.8%. The agreement ratios of the apnea index (AI) and the apnea hypopnea index (AHI) were relatively high, but for types of sleep apnea, agreement ratios require improvement. [ABSTRACT FROM AUTHOR]

Abstract The influence on sleep of alcohol (ethanol) was investigated in nine males using the correlation dimension (CD). Polysomnography (PSG) was recorded on a baseline night (BL-N) and on an ethanol (0.8 g/kg) night (Et-N). The mean correlation dimensions on Et-N, as well as on BL-N, significantly decreased from sleep stage ‘awake’ to sleep Stages 1, 2 and 3 and increased during rapid eye movement (REM) sleep. The mean correlation dimensions of the sleep EEG during sleep Stage 2 and those for the second sleep cycle on the Et-N were significantly higher than those on the BL-N. The changes in the correlation dimensions between sleep cycles were reduced on Et-N as compared to BL-N. [ABSTRACT FROM AUTHOR]

Abstract Effects of light intensity and sleep stages on heart rate variability (HRV) were investigated in young healthy subjects. The low-frequency (LF)/high-frequency (HF) ratio was significantly increased by exposing either to bright lights of 10 000 lx or to extreme darkness (< 0.01 lx), while HF and LF components of HRV were not changed, when compared with those under dim light (100 lx). However, LF was significantly increased at REM sleep, when compared with that at the pre-sleep wake. In contrast, HF was increased at all stages of sleep, and the LF/HF ratio was decreased at slow wave sleep during the baseline night. [ABSTRACT FROM AUTHOR]

AbstrastWe attempted to develop an automated sleep analysis system that uses a personal computer as an aid to the entire sleep research process. Analysis is based on electroencephalogram, chin muscle electromyogram and electrooculography, while other physiological phenomena can be handled arbitrarily. Major characteristics of the system include: (i) simultaneous gathering of physiological phenomena from up to three patients; (ii) high-speed waveform analysis; (iii) user-friendly operating environment through the use of a graphical user interface; and (iv) versatile utilization of analytical data in research. [ABSTRACT FROM AUTHOR]