Utility of sleep stage transitions in assessing sleep continuity

Biological sex influences sleep phenotype in mice experiencing spontaneous opioid withdrawal

Aversive symptoms, including insomnia experienced during opioid withdrawal, are a major drive to relapse; however, withdrawal-associated sleep symptomatology has been little explored in preclinical models. We describe here a model of opioid withdrawal in mice that resembles the sleep phenotype characteristic of withdrawal in humans. Male and female C57BL/6 mice were instrumented with telemeters to record electroencephalogram, electromyogram, activity and subcutaneous temperature. All mice received two treatments separated by a 16-day washout period: (1) saline (volume: 10 ml kg-1); or (2) ascending doses of morphine (5, 10, 20, 40 and 80 mg kg-1; volume: 10 ml kg-1) for 5 days at Zeitgeber time 1 and Zeitgeber time 13. Recordings for the first 71 hr after treatment discontinuation (withdrawal days 1-3) and for 24 hr on withdrawal days 5 and 7 were scored for sleep/wake state, and sleep architecture and electroencephalogram spectral data were analysed. Morphine was acutely wake- and activity-promoting, and non-rapid eye movement and rapid eye movement sleep were increased during the dark phase on withdrawal day 2 in both sexes. While non-rapid eye movement delta power (0.5-4.0 Hz), a measure of sleep intensity, was reduced during the light phase on withdrawal day 1 and the dark phase on withdrawal day 2 in both sexes, female mice also exhibited changes in the duration and the number of bouts of sleep/wake states. These observations of fragmented sleep on withdrawal days 1-3 suggest poorer sleep consolidation and a more pronounced withdrawal-associated sleep phenotype in female than in male mice. These data may indicate a greater sensitivity to morphine, a more distinct aversive sleep phenotype and/or a faster escalation to dependence in female mice.

Overtime work is related to nonrestorative sleep independently of short sleep time among a Japanese occupational population

OBJECTIVE

The symptoms of insomnia are defined as difficulty falling asleep, difficulty staying asleep, and early awakening. Although also a symptom of insomnia, nonrestorative sleep (NRS) is clearly more associated with objective indices than other insomnia symptoms. However, the link between NRS and overtime work duration is poorly understood.

METHODS

The results of a single year's medical examinations were investigated for 26,144 Japanese office workers who were 30 to 59 years old. NRS status and lifestyle were collected through a computer-assisted medical interview. The subjects were asked about the presence or absence of NRS and their lifestyles in the most recent two to three months. The subjects were asked about their sleep times and average overtime durations per month (< 20 h/month, ≥ 20 but < 40 h/month, ≥ 40 but < 60 h/month, and ≥ 60 h/month). The relationships between NRS and overtime work duration adjusted for sleep time were also analyzed.

RESULTS

The proportion of subjects with NRS showed a stepwise increase as overtime work hours increased. A logistic regression analysis was performed using NRS as an objective variable. The multivariate analysis demonstrated that overtime work duration (OR, 1.13; 95% CI 1.10-1.17; P < 0.001; per one-category increase) was an independent determinant of NRS.

CONCLUSION

For office workers, long hours of overtime work increased the NRS prevalence at any sleep duration.

Association between electroencephalogram-based sleep characteristics and physical health in the general adult population

We examined the associations between electroencephalogram (EEG)-based sleep characteristics and physical health parameters in general adults via a cross-sectional study recruiting 100 volunteers aged 30-59 years. Sleep characteristics were measured at home using a portable multichannel electroencephalography recorder. Using the k-means +  +  clustering method, according to 10 EEG-based parameters, participants were grouped into better (n = 39), middle (n = 46), and worse (n = 15) sleep groups. Comparing 50 physical health parameters among the groups, we identified four signals of difference (P < 0.05), including systolic (sBP) and diastolic blood pressure (dBP), γ-glutamyl transpeptidase (γ-GTP), and serum creatinine, where sBP reached a Bonferroni-corrected threshold (P < 0.001). The sBP was higher by 7.9 (95% confidence interval 1.9-13.9) and 15.7 (7.3-24.0) mmHg before adjustment and 5.4 (- 0.1-10.9) and 8.7 (1.1-16.3) mmHg after adjustment for age, sex, body mass index, smoking, drinking habits, and 3% oxygen desaturation index in the middle and worse sleep groups, respectively, than in the better group. As another approach, among 500 combinations of EEG-based and physical health parameters, there were 45 signals of correlation, of which 4 (N1% and sBP, dBP, γ-GTP, and triglycerides) reached a Bonferroni-corrected threshold (P < 0.0001). Thus, EEG-based sleep characteristics are associated with several physical health parameters, particularly sBP.

Sleep Stage Transitions and Sleep-Dependent Memory Consolidation in Children with Narcolepsy-Cataplexy

Electroencephalographic sleep stage transitions and altered first REM sleep period transitions have been identified as biomarkers of type 1 narcolepsy in adults, but not in children. Studies on memory complaints in narcolepsy have not yet investigated sleep-dependent memory consolidation. We aimed to explore stage transitions; more specifically altered REM sleep transition and its relationship with sleep-dependent memory consolidation in children with narcolepsy. Twenty-one children with narcolepsy-cataplexy and twenty-three healthy control children completed overnight polysomnography and sleep-dependent memory consolidation tests. Overnight transition rates (number of transitions per hour), global relative transition frequencies (number of transitions between a stage and all other stages/total number of transitions × 100), overnight transitions to REM sleep (transition from a given stage to REM/total REM transitions × 100), and altered first REM sleep period transitions (transitions from wake or N1 to the first REM period) were computed. Narcoleptic children had a significantly higher overnight transition rate with a higher global relative transition frequencies to wake. A lower sleep-dependent memory consolidation score found in children with narcolepsy was associated with a higher overnight transition frequency. As observed in narcoleptic adults, 90.48% of narcoleptic children exhibited an altered first REM sleep transition. As in adults, the altered sleep stage transition is also present in children with narcolepsy-cataplexy, and a higher transition rate could have an impact on sleep-dependent memory consolidation. These potential biomarkers could help diagnose type 1 narcolepsy in children more quickly; however, further studies with larger cohorts, including of those with type 2 narcolepsy and hypersomnia, are needed.

Relationship between nonrestorative sleep and heartburn among a Japanese occupational population

BACKGROUND

Nonrestorative sleep (NRS) is a symptom of insomnia and is clearly more associated with objective indices than other insomnia symptoms. Gastroesophageal reflux disease (GERD) and insomnia are known to be strongly related. However, the link between NRS and heartburn, a main symptom of GERD, is poorly understood. Therefore, the relationships between them were investigated in addition to sleep duration.

METHODS

The results of a single year's medical examinations were investigated for 29,475 Japanese active office workers who were 20 to 59 years old. NRS and heartburn were investigated in a medical interview using a personal computer. The relationships between NRS and heartburn in addition to sleep duration were also analyzed.

RESULTS

The subjects were 46.6 ± 8.7 years old (mean ± SD) and 27% and 13% of them had NRS and heartburn, respectively. The presence of NRS together with a sleep duration of ≤ 5, 6, 7 or ≥ 8 hours was an independent comorbid factor for heartburn compared with the absence of NRS together with a sleep duration of seven hours (reference; odds ratio [OR], 2.38; 95% confidence interval [CI], 2.11-2.69, OR, 2.44; 95% CI, 2.16-2.76, OR, 2.33; 95% CI, 1.94-2.79; and OR, 1.72; 95% CI, 1.14-2.52). The absence of NRS together with a sleep duration of ≤ 5 hours was also an independent comorbid factor for heartburn compared with the reference (OR, 1.20; 95% CI, 1.05-1.37).

CONCLUSIONS

NRS in active workers may contribute to heartburn comorbidity despite the sleep duration as in other workers without NRS.

AI-Driven sleep staging from actigraphy and heart rate

Sleep is an important indicator of a person's health, and its accurate and cost-effective quantification is of great value in healthcare. The gold standard for sleep assessment and the clinical diagnosis of sleep disorders is polysomnography (PSG). However, PSG requires an overnight clinic visit and trained technicians to score the obtained multimodality data. Wrist-worn consumer devices, such as smartwatches, are a promising alternative to PSG because of their small form factor, continuous monitoring capability, and popularity. Unlike PSG, however, wearables-derived data are noisier and far less information-rich because of the fewer number of modalities and less accurate measurements due to their small form factor. Given these challenges, most consumer devices perform two-stage (i.e., sleep-wake) classification, which is inadequate for deep insights into a person's sleep health. The challenging multi-class (three, four, or five-class) staging of sleep using data from wrist-worn wearables remains unresolved. The difference in the data quality between consumer-grade wearables and lab-grade clinical equipment is the motivation behind this study. In this paper, we present an artificial intelligence (AI) technique termed sequence-to-sequence LSTM for automated mobile sleep staging (SLAMSS), which can perform three-class (wake, NREM, REM) and four-class (wake, light, deep, REM) sleep classification from activity (i.e., wrist-accelerometry-derived locomotion) and two coarse heart rate measures-both of which can be reliably obtained from a consumer-grade wrist-wearable device. Our method relies on raw time-series datasets and obviates the need for manual feature selection. We validated our model using actigraphy and coarse heart rate data from two independent study populations: the Multi-Ethnic Study of Atherosclerosis (MESA; N = 808) cohort and the Osteoporotic Fractures in Men (MrOS; N = 817) cohort. SLAMSS achieves an overall accuracy of 79%, weighted F1 score of 0.80, 77% sensitivity, and 89% specificity for three-class sleep staging and an overall accuracy of 70-72%, weighted F1 score of 0.72-0.73, 64-66% sensitivity, and 89-90% specificity for four-class sleep staging in the MESA cohort. It yielded an overall accuracy of 77%, weighted F1 score of 0.77, 74% sensitivity, and 88% specificity for three-class sleep staging and an overall accuracy of 68-69%, weighted F1 score of 0.68-0.69, 60-63% sensitivity, and 88-89% specificity for four-class sleep staging in the MrOS cohort. These results were achieved with feature-poor inputs with a low temporal resolution. In addition, we extended our three-class staging model to an unrelated Apple Watch dataset. Importantly, SLAMSS predicts the duration of each sleep stage with high accuracy. This is especially significant for four-class sleep staging, where deep sleep is severely underrepresented. We show that, by appropriately choosing the loss function to address the inherent class imbalance, our method can accurately estimate deep sleep time (SLAMSS/MESA: 0.61±0.69 hours, PSG/MESA ground truth: 0.60±0.60 hours; SLAMSS/MrOS: 0.53±0.66 hours, PSG/MrOS ground truth: 0.55±0.57 hours;). Deep sleep quality and quantity are vital metrics and early indicators for a number of diseases. Our method, which enables accurate deep sleep estimation from wearables-derived data, is therefore promising for a variety of clinical applications requiring long-term deep sleep monitoring.

Relationship between nonrestorative sleep with short sleep duration and diabetes mellitus comorbid among a Japanese occupational population

Objective

Nonrestorative sleep (NRS) is one of the symptoms of insomnia and is clearly more associated with objective indices, such as sleep stability, sleep fragmentation, and inflammatory responses, than other insomnia symptoms. However, the link between NRS and diabetes mellitus is poorly understood. Therefore, the prevalence of NRS in workers and the relationships between NRS and diabetes were investigated.

Methods

The results of a single year's medical examinations were investigated for 26,144 Japanese active office workers who were 30 to 59 years old. NRS was investigated using a personal computer in a medical interview. Furthermore, the relationships between NRS and diabetes comorbidity in addition to sleep duration were also analyzed.

Results

The mean age of the subjects was 47.9 ± 7.3 years old, and the proportion of subjects with NRS was 26%. The presence of NRS together with a sleep duration ≤ 5 h or a sleep duration of 6 h was an independent comorbid factor for diabetes compared with the absence of NRS together with a sleep duration of 7 h (odds ratio [OR] 1.34; 95% confidence interval [CI] 1.14-1.58; P < 0.001; and OR 1.25; 95% CI 1.04-1.48; P = 0.015).

Conclusion

NRS in active workers may contribute to the development of diabetes even if the workers have the same short sleep durations as other workers without NRS.

Sleep Staging Framework with Physiologically Harmonized Sub-Networks

Sleep screening is an important tool for both healthcare and neuroscientific research. Automatic sleep scoring is an alternative to the time-consuming gold-standard manual scoring procedure. Recently there have seen promising results on automatic stage scoring by extracting spatio-temporal features via deep neural networks from electroencephalogram (EEG). However, such methods fail to consistently yield good performance due to a missing piece in data representation: the medical criterion of the sleep scoring task on top of EEG features. We argue that capturing stage-specific features that satisfy the criterion of sleep medicine is non-trivial for automatic sleep scoring. This paper considers two criteria: Transient stage marker and Overall profile of EEG features, then we propose a physiologically meaningful framework for sleep stage scoring via mixed deep neural networks. The framework consists of two sub-networks: feature extraction networks, constructed in consideration of the physiological characteristics of sleep, and an attention-based scoring decision network. Moreover, we quantize the framework for potential use under an IoT setting. For proof-of-concept, the performance of the proposed framework is demonstrated by introducing multiple sleep datasets with the largest comprising 42,560 h recorded from 5,793 subjects. From the experiment results, the proposed method achieves a competitive stage scoring performance, especially for Wake, N2, and N3, with higher F1 scores of 0.92, 0.86, and 0.88, respectively. Moreover, the feasibility analysis of framework quantization provides a potential for future implementation in the edge computing field and clinical settings.

Instability of non-REM sleep in older women evaluated by sleep-stage transition and envelope analyses

Study objective

Traditionally, age-related deterioration of sleep architecture in older individuals has been evaluated by visual scoring of polysomnographic (PSG) recordings with regard to total sleep time and latencies. In the present study, we additionally compared the non-REM sleep (NREM) stage and delta, theta, alpha, and sigma wave stability between young and older subjects to extract features that may explain age-related changes in sleep.

Methods

Polysomnographic recordings were performed in 11 healthy older (72.6 ± 2.4 years) and 9 healthy young (23.3 ± 1.1 years) females. In addition to total sleep time, the sleep stage, delta power amplitude, and delta, theta, alpha, and sigma wave stability were evaluated by sleep stage transition analysis and a novel computational method based on a coefficient of variation of the envelope (CVE) analysis, respectively.

Results

In older subjects, total sleep time and slow-wave sleep (SWS) time were shorter whereas wake after sleep onset was longer. The number of SWS episodes was similar between age groups, however, sleep stage transition analysis revealed that SWS was less stable in older individuals. NREM sleep stages in descending order of delta power were: SWS, N2, and N1, and delta power during NREM sleep in older subjects was lower than in young subjects. The CVE of the delta-band is an index of delta wave stability and showed significant differences between age groups. When separately analyzed for each NREM stage, different CVE clusters in NREM were clearly observed between young and older subjects. A lower delta CVE and amplitude were also observed in older subjects compared with young subjects in N2 and SWS. Additionally, lower CVE values in the theta, alpha and sigma bands were also characteristic of older participants.

Conclusion

The present study shows a decrease of SWS stability in older subjects together with a decrease in delta wave amplitude. Interestingly, the decrease in SWS stability coincided with an increase in short-term delta, theta, sigma, and alpha power stability revealed by lower CVE. Loss of electroencephalograms (EEG) variability might be a useful marker of brain age.

Brain glutamate and sleep efficiency associations following a ketogenic diet intervention in individuals with Alcohol Use Disorder

Background

We previously showed that ketogenic diet (KD) was effective in curbing alcohol withdrawal and craving in individuals with alcohol use disorder (AUD). We hypothesized that the clinical benefits were due to improvements in sleep. To test this, we performed a secondary analysis on the KD trial data to (1) examine the effects of KD on total sleep time (TST) and sleep quality and (2) investigate the association between KD-induced alterations in cingulate glutamate concentration and changes in TST and sleep quality.

Methods

AUD individuals undergoing alcohol detoxification were randomized to receive KD (n=19) or standard American diet (SA; n=14) for three weeks. TST was measured weekly by self-report, GENEActive sleep accelerometer, and X4 Sleep Profiler ambulatory device. Sleep quality was assessed using subjectively ratings of sleep depth and restedness and Sleep Profiler (Sleep Efficiency [%]). Weekly ¹H magnetic resonance spectroscopy scans measured cingulate glutamate levels.

Results

TST was lower in KD than SA and increased with effect of time. Sleep depth, restedness, and Sleep Efficiency improved with time, but exhibited no effect of diet. In KD and SA combined, week 1 cingulate glutamate levels correlated positively with Sleep Efficiency, but not with TST.

Conclusions

Although cingulate glutamate levels correlated positively with Sleep Efficiency in week 1, KD-induced glutamate elevation did not produce significant sleep improvements. Rather, KD was associated with lower TST than SA. Given the well-established associations between sleep and alcohol relapse, longer follow up assessment of KD's impact on sleep in AUD is warranted.

Effect of Seat Angle when Sleeping in a Car on Quality of Sleep and Its Impact on Calculation Performance the Following Day

The number of occasions to stay in a car overnight is increasing during disasters; however, the effects on sleep and the impact on daytime functioning are not well understood. We investigated the effect of seat angle when sleeping in a car and its impact on calculation performance the following day. Fifteen healthy males participated in three trials (sleeping in a car with the front seat angled at 45° and 60° in a laboratory and sleeping at home); sleep and calculation performance the following day were compared. Increased wake after sleep onset and decreased slow-wave sleep were observed in the 60° trial, that is, near-vertical, compared with the others. Subjective sleep quality and calculation performance in the 45° and 60° trials were poorer than those in the home trial. The effect of seat angle on sleep was confirmed objectively, but not subjectively, suggesting that a large seat angle might cause sleep impairment.

Relationships between sleep efficiency and lifestyle evaluated by objective sleep assessment: SLeep Epidemiology Project at University of Tsukuba

Objectively measured sleep efficiency has recently been shown to be associated with health problems. Although several factors have previously been reported to be associated with sleep efficiency, most of these studies were conducted on older or younger adults, and the factors associated with sleep efficiency in healthy workers remain unknown. The aim of this study was to investigate the relationship between sleep efficiency and lifestyle factors using sleep measurement data recorded by an activity meter worn by workers. In total, 693 workers (male, 43.6%; mean age, 42.7 ± 11.3 years) were recruited from five offices in 2017. Sleep was measured over the period of 1 week by actigraphy. Workers' attributes, lifestyle habits, and occupational stress were identified using a questionnaire, and the association of sleep efficiency with lifestyle, occupational stress, and attributes was explored by logistic regression analysis. A logistic regression analysis using attributes and occupational stress as adjustment variables revealed that "longer sleeping hours on weekends than on weekdays" [odds ratios (OR), 0.66; 95% confidence interval (CI), 0.47-0.94], "water ingestion at bedtime" [OR, 2.09; 95% CI, 1.28-3.41], and "smartphone use at bedtime" [OR, 1.90; 95% CI, 1.28-2.83] were associated with decreased sleep efficiency. This study found that lifestyle habits were associated with sleep efficiency among workers. It is necessary to verify whether intervention in these lifestyle habits would contribute to the improvement of sleep efficiency in future studies.

Non-pharmacological interventions for sleep promotion in hospitalized children

BACKGROUND

Healthy sleep is an important component of childhood development. Changes in sleep architecture, including sleep stage composition, quantity, and quality from infancy to adolescence are a reflection of neurologic maturation. Hospital admission for acute illness introduces modifiable risk factors for sleep disruption that may negatively affect active brain development during a period of illness and recovery. Thus, it is important to examine non-pharmacologic interventions for sleep promotion in the pediatric inpatient setting.

OBJECTIVES

To evaluate the effect of non-pharmacological sleep promotion interventions in hospitalized children and adolescents on sleep quality and sleep duration, child or parent satisfaction, cost-effectiveness, delirium incidence, length of mechanical ventilation, length of stay, and mortality.

SEARCH METHODS

We searched CENTRAL, MEDLINE, Embase, CINAHL, three other databases, and three trials registers to December 2021. We searched Google Scholar, and two websites, handsearched conference abstracts, and checked reference lists of included studies.

SELECTION CRITERIA

Randomized controlled trials (RCTs) or quasi-RCTs, including cross-over trials, investigating the effects of any non-pharmacological sleep promotion intervention on the sleep quality or sleep duration (or both) of children aged 1 month to 18 years in the pediatric inpatient setting (intensive care unit [ICU] or general ward setting).

DATA COLLECTION AND ANALYSIS

Two review authors independently assessed trial eligibility, evaluated risk of bias, extracted and synthesized data, and used the GRADE approach to assess certainty of evidence. The primary outcomes were changes in both objective and subjective validated measures of sleep in children; secondary outcomes were child and parent satisfaction, cost-effectiveness ratios, delirium incidence or delirium-free days at time of hospital discharge, duration of mechanical ventilation, length of hospital stay, and mortality.

MAIN RESULTS

We included 10 trials (528 participants; aged 3 to 22 years) in inpatient pediatric settings. Seven studies were conducted in the USA, two in Canada, and one in Brazil. Eight studies were funded by government, charity, or foundation grants. Two provided no information on funding. Eight studies investigated behavioral interventions (massage, touch therapy, and bedtime stories); two investigated physical activity interventions. Duration and timing of interventions varied widely. All studies were at high risk of performance bias due to the nature of the intervention, as participants, parents, and staff could not be masked to group assignment. We were unable to perform a quantitative synthesis due to substantial clinical heterogeneity. Behavioral interventions versus usual care Five studies (145 participants) provided low-certainty evidence of no clear difference between multicomponent relaxation interventions and usual care on objective sleep measures. Overall, evidence from single studies found no clear differences in daytime or nighttime sleep measures (33 participants); any sleep parameter (48 participants); or daytime or nighttime sleep or nighttime arousals (20 participants). One study (34 participants) reported no effect of massage on nighttime sleep, sleep efficiency (SE), wake after sleep onset (WASO), or total sleep time (TST) in adolescents with cancer. Evidence from a cross-over study in 10 children with burns suggested touch therapy may increase TST (391 minutes, interquartile range [IQR] 251 to 467 versus 331 minutes, IQR 268 to 373; P = 0.02); SE (76, IQR 53 to 90 versus 66, IQR 55 to 78; P = 0.04); and the number of rapid eye movement (REM) periods (4.5, IQR 2 to 5 versus 3.5, IQR 2 to 4; P = 0.03); but not WASO, sleep latency (SL), total duration of REM, or per cent of slow wave sleep. Four studies (232 participants) provided very low-certainty evidence on subjective measures of sleep. Evidence from single studies found that sleep efficiency may increase, and the percentage of nighttime wakefulness may decrease more over a five-day period following a massage than usual care (72 participants). One study (48 participants) reported an improvement in Children's Sleep Habits Questionnaire scores after discharge in children who received a multicomponent relaxation intervention compared to usual care. In another study, mean sleep duration per sleep episode was longer (23 minutes versus 15 minutes), and time to fall asleep was shorter (22 minutes versus 27 minutes) following a bedtime story versus no story (18 participants); and children listening to a parent-recorded story had longer SL than when a parent was present (mean 57.5 versus 43.5 minutes); both groups reported longer SL than groups who had a stranger-recorded story, and those who had no story and absent parents (94 participants; P < 0.001). In one study (34 participants), 87% (13/15) of participants felt they slept better following massage, with most parents (92%; 11/12) reporting they wanted their child to receive a massage again. Another study (20 participants) reported that parents thought the music, touch, and reading components of the intervention were acceptable, feasible, and had positive effects on their children (very low-certainty evidence). Physical activity interventions versus usual care One study (29 participants) found that an enhanced physical activity intervention may result in little or no improvement in TST or SE compared to usual care (low-certainty evidence). Another study (139 participants), comparing play versus no play found inconsistent results on subjective measures of sleep across different ages (TST was 49% higher for the no play groups in 4- to 7-year olds, 10% higher in 7- to 11-year olds, and 22% higher in 11- to 14-year olds). This study also found inconsistent results between boys and girls (girls in the first two age groups in the play group slept more than the no play group). No study evaluated child or parent satisfaction for behavioral interventions, or cost-effectiveness, delirium incidence or delirium-free days at hospital discharge, length of mechanical ventilation, length of hospital stay, or mortality for either behavioral or physical activity intervention.

AUTHORS' CONCLUSIONS

The included studies were heterogeneous, so we could not quantitatively synthesize the results. Our narrative summary found inconsistent, low to very low-certainty evidence. Therefore, we are unable to determine how non-pharmacologic sleep promotion interventions affect sleep quality or sleep duration compared with usual care or other interventions. The evidence base should be strengthened through design and conduct of randomized trials, which use validated and highly reliable sleep assessment tools, including objective measures, such as polysomnography and actigraphy.

SleepSEEG: Automatic sleep scoring using intracranial EEG recordings only

OBJECTIVE

To perform automatic sleep scoring based only on intracranial EEG, without the need for scalp electroencephalography (EEG), electrooculography (EOG) and electromyography (EMG), in order to study sleep, epilepsy, and their interaction.

APPROACH

Data from 33 adult patients was used for development and training of the automatic scoring algorithm using both oscillatory and non-oscillatory spectral features. The first step consisted in unsupervised clustering of channels based on feature variability. For each cluster the classification was done in two steps, a multiclass tree followed by binary classification trees to distinguish the more challenging stage N1. The test data consisted in 11 patients, in whom the classification was done independently for each channel and then combined to get a single stage per epoch.

MAIN RESULTS

An overall agreement of 78% was observed in the test set between the sleep scoring of the algorithm and two human experts scoring based on scalp EEG, EOG and EMG. Balanced sensitivity and specificity were obtained for the different sleep stages. The performance was excellent for stages W, N2, and N3, and good for stage R, but with high variability across patients. The performance for the challenging stage N1 was poor, but at a similar level as for published algorithms based on scalp EEG. High confidence epochs in different stages (other than N1) can be identified with median per patient specificity >80%.

SIGNIFICANCE

The automatic algorithm can perform sleep scoring of long term recordings of patients with intracranial electrodes undergoing presurgical evaluation in the absence of scalp EEG, EOG and EMG, which are normally required to define sleep stages but are difficult to use in the context of intracerebral studies. It also constitutes a valuable tool to generate hypotheses regarding local aspects of sleep, and will be significant for sleep evaluation in clinical epileptology and neuroscience research.

Objective underpinnings of self-reported sleep quality in middle-aged and older adults: The importance of N2 and wakefulness

STUDY OBJECTIVES

The measurable aspects of brain function (polysomnography, PSG) that are correlated with sleep satisfaction are poorly understood. Using recent developments in automated sleep scoring, which remove the within- and between-rater error associated with human scoring, we examine whether PSG measures are associated with sleep satisfaction.

DESIGN AND SETTING

A single night of PSG data was compared to contemporaneously collected measures of sleep satisfaction with Random Forest regressions. Whole and partial night PSG data were scored using a novel machine learning algorithm.

PARTICIPANTS

Community-dwelling adults (N = 3165) who participated in the Sleep Heart Health Study.

INTERVENTIONS

None.

MEASUREMENTS AND RESULTS

Models explained 30% of sleep depth and 27% of sleep restfulness, with a similar top four predictors: minutes of N2 sleep, sleep efficiency, age, and minutes of wake after sleep onset (WASO). With increasing self-reported sleep quality, there was a progressive increase in N2 and decrease in WASO of similar magnitude, without systematic changes in N1, N3 or REM sleep. In comparing those with the best and worst self-reported sleep satisfaction, there was a range of approximately 30 min more N2, 30 min less WASO, an improvement of sleep efficiency of 7-8%, and an age span of 3-5 years. Examination of sleep most proximal to morning awakening revealed no greater explanatory power than the whole-night data set.

CONCLUSIONS

Higher N2 and concomitant lower wake is associated with improved sleep satisfaction. Interventions that specifically target these may be suitable for improving the self-reported sleep experience.

Mortality associated with nonrestorative short sleep or nonrestorative long time-in-bed in middle-aged and older adults

Associations of sleep duration with human health could differ depending on whether sleep is restorative. Using data from 5804 participants of the Sleep Heart Health Study, we examined the longitudinal association of sleep restfulness combined with polysomnography-measured total sleep time (TST) or time in bed (TIB), representing different sleeping behaviors, with all-cause mortality. Among middle-aged adults, compared with restful intermediate TST quartile, the lowest TST quartile with feeling unrested was associated with higher mortality (hazard ratio [HR], 1.54; 95% confidence interval [CI] 1.01–2.33); the highest TST quartile with feeling rested was associated with lower mortality (HR, 0.55; 95% CI 0.32–0.97). Among older adults, the highest TIB quartile with feeling unrested was associated with higher mortality, compared with restful intermediate TIB quartile (HR, 1.57; 95% CI 1.23–2.01). Results suggest a role of restorative sleep in differentiating the effects of sleep duration on health outcomes in midlife and beyond.

REM Sleep: An Unknown Indicator of Sleep Quality

Standard polysomnographic analysis of sleep has not provided evidence of an objective measure of sleep quality; however, factors such as sleep duration and sleep efficiency are those more consistently associated with the subjective perception of sleep quality. Sleep reduction as currently occurs in our 24/7 society has had a profound impact on sleep quality; the habitual sleep period should fit within what is a limited nighttime window and may not be sufficient to satisfy the whole sleep process; moreover, the use of artificial light during the evening and early night hours can delay and disturb the circadian rhythms, especially affecting REM sleep. The correct phase relationship of the sleep period with the circadian pacemaker is an important factor to guarantee adequate restorative sleep duration and sleep continuity, thus providing the necessary background for a good night's sleep. Due to the fact that REM sleep is controlled by the circadian clock, it can provide a window-like mechanism that defines the termination of the sleep period when there is still the necessity to complete the sleep process (not only wake-related homeostasis) and to meet the circadian end of sleep timing. An adequate amount of REM sleep appears necessary to guarantee sleep continuity, while periodically activating the brain and preparing it for the return to consciousness.

Nighttime melatonin secretion and sleep architecture: different associations in perimenopausal and postmenopausal women

BACKGROUND

Sleep quality typically decreases after menopause, but the underlying mechanisms are poorly understood. Concentrations of melatonin are lower and its secretion profiles different before and after menopause. However, whether and how melatonin and sleep architecture are associated in women of different reproductive states have not been examined to date.

METHODS

Overnight serum melatonin samples were taken from 17 perimenopausal and 18 postmenopausal healthy women. Sleep quality was measured with all-night polysomnography recordings.

RESULTS

Melatonin concentrations tended to be the lowest during NREM sleep, and were associated with higher odds of transitions from wake to NREM sleep. The curves of predicted overnight melatonin values from linear mixed models varied according to sleep phases (NREM, REM, Wake) in perimenopausal, but not in postmenopausal women. In perimenopause higher melatonin area under curve (AUC) correlated with higher slow-wave activity (p = 0.043), and higher minimum concentrations with shorter slow-wave sleep (SWS) latency (p = 0.029). In postmenopause higher mean and maximum melatonin concentrations and AUC correlated with lower SWS percentage (p = 0.044, p = 0.029, p = 0.032), and higher mean (p = 0.032), maximum (p = 0.032) and minimum (p = 0.037) concentrations with more awakenings from REM sleep. In the age- and BMI- adjusted regression models, the association between higher maximum (p = 0.046) melatonin concentration and lower SWS percentage remained.

CONCLUSIONS

The relationship between melatonin and sleep architecture differed in perimenopausal and postmenopausal women. After menopause, high melatonin concentrations were associated with worse sleep. Whether these different patterns are related to aging of the reproductive system, and to decrease in menopausal sleep quality, remains to be elucidated.

Subjective sleep quality is poorly associated with actigraphy and heart rate measures in community-dwelling older men

OBJECTIVES

There has been a proliferation in the use of commercially-available accelerometry- and heart rate-based wearable devices to monitor sleep. While the underlying technology is reasonable at detecting sleep quantity, the ability of these devices to predict subjective sleep quality is currently unknown. We tested whether the fundamental signals from such devices are useful in determining subjective sleep quality.

METHODS

Older, community-dwelling men (76.5 ± 5.77 years) enrolled in the Osteoporotic Fractures in Men Study (MrOS) participated in an overnight sleep study during which sleep was monitored with actigraphy (wrist-worn accelerometry) and polysomnography (PSG), including electrocardiography (N = 1141). Subjective sleep quality was determined the next morning using 5-point Likert-type scales of sleep depth and restfulness. Lasso and random forest regression models analyzed the relationship between actigraph-determined sleep variables, the shape of the activity patterns during sleep (functional principal component analysis), average heart rate, heart rate variability (HRV), demographics, and self-reported depression, anxiety, habitual sleep, and daytime sleepiness measures.

RESULTS

Actigraphy data, in combination with heart rate, HRV, demographic, and psychological variables, do not predict well subjective sleep quality (R² = 0.025 to 0.162).

CONCLUSIONS

Findings are consistent with previous studies that objective sleep measures are not well correlated with subjective sleep quality. Developing validated biomarkers of subjective sleep quality could improve both existing and novel treatment modalities and advance sleep medicine towards precision healthcare standards.

Validity of an under-mattress sensor for objective sleep measurement in critically ill patients: a prospective observational study

Background

Considering the adverse effects of sleep disturbance in critical care settings, accurate assessment could aid therapy; however, methodological inadequacies mean that no viable option is currently available. Research in healthy population has recently shown that a non-wearable sleep measurement device placed under the mattress of the bed could be beneficial in intensive care settings. Therefore, we aimed to validate this device compared with polysomnography (PSG) and to assess how it related to subjective sleep evaluations.

Methods

This observational study measured the sleep of critically ill adult patients. The primary goal was to validate the Nemuri SCAN (NSCAN; Paramount Bed Co., Ltd., Tokyo, Japan) against the reference standard PSG for 24 h. The secondary goal was to evaluate the association between the objective parameters obtained from NSCAN and PSG and the subjective report data obtained using the Richards–Campbell Sleep Questionnaire (RCSQ) for the nighttime.

Results

Eleven participants were evaluated. The median of the total sleep time scored by PSG was 456.0 (353.0–517.5) min during the nighttime and 305.0 (186.2–542.5) min during the daytime. PSG over 24 h revealed significant decreases in restorative sleep, with excessive daytime sleep, but with a normal quantity of nighttime sleep. The agreement, sensitivity, and specificity rates (with 95% confidence intervals) for the NSCAN compared with PSG were 68.4% (67.9–69.0%), 90.1% (89.7–90.6%), and 38.7% (37.9–39.7%), respectively. The median RCSQ value when subjectively evaluating nighttime sleep was 68.0 (26.3–83.5); this showed no correlation with the NSCAN sleep parameters, despite a positive correlation with the ratio of the stage N2 isolated or combined with restorative sleep in the PSG assessment.

Conclusions

NSCAN had moderate agreement, high sensitivity, and poor specificity in intensive care settings, which is most likely due to its inability to identify immobile wakefulness often observed in critically ill patients or sleep depth. This remains a barrier to its use in the assessment of subjective sleep quality.

Trial registration

This investigation was part of an interventional trial registered with the University Hospital Medical Information Network Individual Clinical Trials Registry (UMIN000026350, http://www.umin.ac.jp/icdr/index-j.html) on March 1, 2017.

Sleep continuity, stability and organization in good and bad sleepers

The relationship between objective and subjective sleep quality is still debated. Here, we investigate differences in objective sleep parameters in habitual subjective good sleepers and bad sleepers with the aim of evaluating sleep continuity, stability and organization as possible determinants of subjective sleep quality. In total, 38 subjects (good sleepers, N = 18; bad sleepers, N = 20) underwent two nights of sleep recording. Traditional sleep parameters displayed no between-groups differences. Conversely, bad sleepers showed lower sleep continuity (awakenings frequency), stability (e.g. arousals and state transitions frequency) and organization (e.g. number of sleep cycles and time spent in cycles). Our findings point to the involvement of these measures in determining habitual sleep quality perception and suggest the possibility to include them in standard sleep assessments.

The Effect of Cognitive Activity on Sleep Maintenance in a Subsequent Daytime Nap

Background/Objective: The aim of this study is to assess the effects of a learning task on the characteristics of a subsequent daytime nap. Participants and Methods: Thirty-eight subjects were administered a control nap (C) and one preceded by a cognitive training session (TR). Results: Relative to C, TR naps showed significantly increased sleep duration with decreased sleep latency, as well as significantly increased sleep efficiency due to reduced awakening frequency. Meaningful trends were also found toward an increase of Stage 2 sleep proportion and a reduction of Stage 1 sleep, percentage of wake after sleep onset (WASO), and frequency of state transitions. Conclusions: Our results indicate that presleep learning favors sleep propensity and maintenance, offering the possibility to explore planned cognitive training as a low-cost treatment for sleep impairments.

Unique sleep-stage transitions determined by obstructive sleep apnea severity, age and gender

In obstructive sleep apnea, patients' sleep is fragmented leading to excessive daytime sleepiness and co-morbidities like arterial hypertension. However, traditional metrics are not always directly correlated with daytime sleepiness, and the association between traditional sleep quality metrics like sleep duration and arterial hypertension is still ambiguous. In a development cohort, we analysed hypnograms from mild (n = 209), moderate (n = 222) and severe (n = 272) obstructive sleep apnea patients as well as healthy controls (n = 105) from the European Sleep Apnea Database. We assessed sleep by the analysis of two-step transitions depending on obstructive sleep apnea severity and anthropometric factors. Two-step transition patterns were examined for an association to arterial hypertension or daytime sleepiness. We also tested cumulative distributions of wake as well as sleep-states for power-laws (exponent α) and exponential distributions (decay time τ) in dependency on obstructive sleep apnea severity and potential confounders. Independent of obstructive sleep apnea severity and potential confounders, wake-state durations followed a power-law distribution, while sleep-state durations were characterized by an exponential distribution. Sleep-stage transitions are influenced by obstructive sleep apnea severity, age and gender. N2 → N3 → wake transitions were associated with high diastolic blood pressure. We observed higher frequencies of alternating (symmetric) patterns (e.g. N2 → N1 → N2, N2 → wake → N2) in sleepy patients both in the development cohort and in a validation cohort (n = 425). In conclusion, effects of obstructive sleep apnea severity and potential confounders on sleep architecture are small, but transition patterns still link sleep fragmentation directly to obstructive sleep apnea-related clinical outcomes like arterial hypertension and daytime sleepiness.

Better sleep quality could reinforce the relationship between sleep posture and kidney stone laterality in obstructive sleep apnea patients

Urolithiasis is a common urologic problem among adults worldwide. It is interesting that urinary stones tend to recur on the same side and the pathophysiology of unilateral stone formation is not clearly understood. Researchers found that sleep posture could alter renal perfusion, and subsequent vascular injury may lead to urolithiasis formation. The aim of this study is to retrospectively evaluate the correlations between the specific sleep postures recorded in polysomnography (PSG) and the stone laterality in unilateral urinary stone formers with obstructive sleep apnea (OSA). We retrospectively reviewed patients receiving PSG for the diagnosis of OSA in our institutes between January 1st 2002 and September 30th 2016. Those had a history of unilateral urinary stones which surgical intervention was warranted were included. Supine, prone, right decubitus, and left decubitus sleep postures were determined as patients remained specific postures for more than 50% of their sleep time. The laterality of stones with sleep posture was analyzed with Chi-square test. A student t test was used to assess factors that influenced the ipsilateral stone formation. IBM SPSS Statistics 21 software was adopted to analyze the data, and p value less than 0.05 was considered statistically significant. 117 patients were enrolled in this study, among which 105 (89.7%) were male and 12 were female. The mean age at PSG examination was 53.5 ± 11.5 years. 66 patients were diagnosed as left-side urolithiasis, and 51 patients had urinary stones at right side. 58 (49.6%) patients slept predominantly in supine posture, while 53 (45.3%) patients, in decubitus posture, and no patient, in prone posture. A total of 51 patients' stones appeared at the same side as their decubitus posture (p < 0.0001). Shorter sleep stage I ratio (24.3 ± 15.5% versus 52.4 ± 24.7%, p = 0.017), longer sleep stage II + III + IV ratio (66.2 ± 21.9% versus 30.9 ± 2.8, p = 0.049), and less sleep stage change (176.6 ± 69.6 times versus 282.5 ± 91.2 times, p = 0.041) were correlated with stone formation on the same side. Age, gender, body mass index, and other polysomnographic parameters failed to demonstrate associations between the sides of urinary calculi and sleep postures. We discovered that sleep posture is associated with unilateral urolithiasis formation. Favorable sleep quality and less sleep stage changes could consolidate the correlations. Further prospective study is warranted to evaluate the effect of sleep posture and quality on non-OSA patients as well as the preventive effect of sleep posture modification on repeated unilateral stone formers.

Sleep Is a Refreshing Process: An fNIRS Study

Sleep is a very critical process that constitutes up to one third of daytime of a healthy adult. It is known to be an active period where body and brain is refreshed for the next day. It is both part of a larger cycle, i.e., circadian rhythm, and has subcsycles in it, i.e., sleep stages. Although hemodynamics of these stages have been investigated especially in the last two decades, there are still points in the hemodynamics to be illuminated especially in terms of refreshment. This study aims to investigate refreshing property of sleep in terms of sleep stages using functional near-infrared spectroscopy (fNIRS) for measuring prefrontal cortex (PFC) hemodynamics. Nine healthy subjects slept in sleep laboratories, monitored by polysomnography and fNIRS before, during, and after night sleep. REM stage had lower oxyhemoglobin (HbO) and total hemoglobin (HbT) than the other sleep stages and wakefulness. Deoxyhemoglobin (HbR) did not differ between any stages. All sleep stages and wakefulness stage at the end of the sleep had higher HbO and lower HbR than the beginning of the sleep. HbT levels did not differ between the beginning and the end of the sleep for any stages. During REM sleep, PFC seems to get lower blood supply, possibly due to increased demand in other brain regions. Regardless of the stage, PFC has higher oxygenation toward the end of sleep, indicating refreshment. Overall, our brain seems to be on duty during sleep throughout the night for "cleaning" and "refreshing" itself. Hemodynamic changes from the beginning to end of sleep might be the indicator of this work. Thus, accordingly REM stage seems to be at a central point for this work.

A Two Stage Approach for the Automatic Detection of Insomnia

Chronic insomnia can significantly impair an individual's quality of life leading to a high societal cost. Unfortunately, limited automated tools exist that can assist clinicians in the timely detection of insomnia. In this paper, we propose a two stage approach to automatically detect insomnia from an overnight EEG recording. In the first stage we trained a sleep stage scoring model and an epoch-level insomnia detection model. Both models are deep neural network (DNN)- based which are fed by a set of temporal and spectral features derived from 2 EEG channels. In the second stage we computed two subject-level feature sets. One is computed using the output of the sleep stage scoring model and consists of the sleep stage ratios, the stage pair ratios and the stage transition ratios. The second feature set is derived from the output of the epoch-level insomnia detection model and represents the ratio of detected insomniac epochs in each stage and their average posterior probability. These features are then used to train a final binary classifier to classify each subject as control, i.e., with no sleep complaints, or insomniac. We compared 5 different binary classifiers, namely the linear discriminant analysis (LDA), the classification and regression trees (CART) and the support vector machine (SVM) with linear, Gaussian and sigmoid kernels. The system was evaluated against data collected from 115 participants, 61 control and 54 with insomnia, and achieved $F1$ score, sensitivity and specificity of 0.88, 84% and 91% respectively.

Sleep devices: wearables and nearables, informational and interventional, consumer and clinical

The field of sleep is in many ways ideally positioned to take full advantage of advancements in technology and analytics that is fueling the mobile health movement. Combining hardware and software advances with increasingly available big datasets that contain scored data obtained under gold standard sleep laboratory conditions completes the trifecta of this perfect storm. This review highlights recent developments in consumer and clinical devices for sleep, emphasizing the need for validation at multiple levels, with the ultimate goal of using personalized data and advanced algorithms to provide actionable information that will improve sleep health.

Physical activity measured with wrist and ankle accelerometers: Age, gender, and BMI effects

Physical activity (PA) is associated with various aspects of physical and mental health and varies by age and BMI. We aimed to compare PA measures obtained with wrist and ankle accelerometers and characterize their associations with age and BMI. We assessed PA mean and PA variability (indexed by coefficient of variation (CV)) at daytime and nighttime periods for seven consecutive days (M = 152.90 h) in 47 healthy participants (18–73 years old, 21 females). Diurnally, mean PA for both ankle and wrist and CV of PA for ankle decreased from the first to the second half of daytime (p < 0.05). There were no differences in mean PA between wrist and ankle at any time-period (p > 0.2). CV of ankle PA at daytime was significantly higher than wrist PA (p < .0001). The opposite pattern was observed at nighttime (p < .0001). Pearson correlation analyses were performed to assess the associations between wrist (or ankle) PA and age and BMI. Mean daytime (but not nighttime) activity for wrist and ankle decreased significantly with age (p < .05). PA variability also decreased with age for wrist and ankle during daytime and for ankle during nighttime (p < .05). BMI was negatively associated with wrist daytime PA variability (p < .05). There were no gender effects on activity measures. These findings indicate that wrist and ankle mean PA measures were not significantly different but were significantly different (p < 0.5) for PA variability in both daytime and nighttime. Age-related decreases of PA-mean and variability were observed during daytime in wrist and ankle, whereas higher wrist daytime variability was inversely associated with BMI. These findings provide new insights into PA features in free-living environment, which are relevant for public health and may have implications for clinical assessment of neurodegenerative disorders impacting PA and their interaction with demographics.

Sleep Stage Transition Dynamics Reveal Specific Stage 2 Vulnerability in Insomnia

Study Objectives

Objective sleep impairments in insomnia disorder (ID) are insufficiently understood. The present study evaluated whether whole-night sleep stage dynamics derived from polysomnography (PSG) differ between people with ID and matched controls and whether sleep stage dynamic features discriminate them better than conventional sleep parameters.

Methods

Eighty-eight participants aged 21-70 years, including 46 with ID and 42 age- and sex-matched controls without sleep complaints, were recruited through www.sleepregistry.nl and completed two nights of laboratory PSG. Data of 100 people with ID and 100 age- and sex-matched controls from a previously reported study were used to validate the generalizability of findings. The second night was used to obtain, in addition to conventional sleep parameters, probabilities of transitions between stages and bout duration distributions of each stage. Group differences were evaluated with nonparametric tests.

Results

People with ID showed higher empirical probabilities to transition from stage N2 to the lighter sleep stage N1 or wakefulness and a faster decaying stage N2 bout survival function. The increased transition probability from stage N2 to stage N1 discriminated people with ID better than any of their deviations in conventional sleep parameters, including less total sleep time, less sleep efficiency, more stage N1, and more wake after sleep onset. Moreover, adding this transition probability significantly improved the discriminating power of a multiple logistic regression model based on conventional sleep parameters.

Conclusions

Quantification of sleep stage dynamics revealed a particular vulnerability of stage N2 in insomnia. The feature characterizes insomnia better than-and independently of-any conventional sleep parameter.

Comparing two insomnia detection models of clinical diagnosis techniques

Sleep disorders are becoming increasingly prevalent in society. However most of the burgeoning research on automated sleep analysis has been in the realm of sleep stage classification with limited focus on accurately diagnosing these disorders. In this paper, we explore two different models to discriminate between control and insomnia patients using support vector machine (SVM) classifiers. We validated the models using data collected from 124 participants, 70 control and 54 with insomnia. The first model uses 57 features derived from two channels of EEG data and achieved an accuracy of 81%. The second model uses 15 features from each participant's hypnogram and achieved an accuracy of 74%. The impetus behind using these two models is to follow the clinician's diagnostic decision-making process where both the EEG signals and the hypnograms are used. These results demonstrate that there is potential for further experimentation and improvement of the predictive capability of the models to help in diagnosing sleep disorders like insomnia.

Correlates of sleep quality in midlife and beyond: a machine learning analysis

OBJECTIVES

In older adults, traditional metrics derived from polysomnography (PSG) are not well correlated with subjective sleep quality. Little is known about whether the association between PSG and subjective sleep quality changes with age, or whether quantitative electroencephalography (qEEG) is associated with sleep quality. Therefore, we examined the relationship between subjective sleep quality and objective sleep characteristics (standard PSG and qEEG) across middle to older adulthood.

METHODS

Using cross-sectional analyses of 3173 community-dwelling men and women aged between 39 and 90 participating in the Sleep Heart Health Study, we examined the relationship between a morning rating of the prior night's sleep quality (sleep depth and restfulness) and polysomnographic, and qEEG descriptors of that single night of sleep, along with clinical and demographic measures. Multivariable models were constructed using two machine learning methods, namely lasso penalized regressions and random forests.

RESULTS

Little variance was explained across models. Greater objective sleep efficiency, reduced wake after sleep onset, and fewer sleep-to-wake stage transitions were each associated with higher sleep quality; qEEG variables contributed little explanatory power. The oldest adults reported the highest sleep quality even as objective sleep deteriorated such that they would rate their sleep better, given the same level of sleep efficiency. Despite this, there were no major differences in the predictors of subjective sleep across the age span.

CONCLUSION

Standard metrics derived from PSG, including qEEG, contribute little to explaining subjective sleep quality in middle-aged to older adults. The objective correlates of subjective sleep quality do not appear to systematically change with age despite a change in the relationship between subjective sleep quality and objective sleep efficiency.

Big data in sleep medicine: prospects and pitfalls in phenotyping

Clinical polysomnography (PSG) databases are a rich resource in the era of "big data" analytics. We explore the uses and potential pitfalls of clinical data mining of PSG using statistical principles and analysis of clinical data from our sleep center. We performed retrospective analysis of self-reported and objective PSG data from adults who underwent overnight PSG (diagnostic tests, n=1835). Self-reported symptoms overlapped markedly between the two most common categories, insomnia and sleep apnea, with the majority reporting symptoms of both disorders. Standard clinical metrics routinely reported on objective data were analyzed for basic properties (missing values, distributions), pairwise correlations, and descriptive phenotyping. Of 41 continuous variables, including clinical and PSG derived, none passed testing for normality. Objective findings of sleep apnea and periodic limb movements were common, with 51% having an apnea-hypopnea index (AHI) >5 per hour and 25% having a leg movement index >15 per hour. Different visualization methods are shown for common variables to explore population distributions. Phenotyping methods based on clinical databases are discussed for sleep architecture, sleep apnea, and insomnia. Inferential pitfalls are discussed using the current dataset and case examples from the literature. The increasing availability of clinical databases for large-scale analytics holds important promise in sleep medicine, especially as it becomes increasingly important to demonstrate the utility of clinical testing methods in management of sleep disorders. Awareness of the strengths, as well as caution regarding the limitations, will maximize the productive use of big data analytics in sleep medicine.

When a gold standard isn't so golden: Lack of prediction of subjective sleep quality from sleep polysomnography

BACKGROUND

Reports of subjective sleep quality are frequently collected in research and clinical practice. It is unclear, however, how well polysomnographic measures of sleep correlate with subjective reports of prior-night sleep quality in elderly men and women. Furthermore, the relative importance of various polysomnographic, demographic and clinical characteristics in predicting subjective sleep quality is not known. We sought to determine the correlates of subjective sleep quality in older adults using more recently developed machine learning algorithms that are suitable for selecting and ranking important variables.

METHODS

Community-dwelling older men (n=1024) and women (n=459), a subset of those participating in the Osteoporotic Fractures in Men study and the Study of Osteoporotic Fractures study, respectively, completed a single night of at-home polysomnographic recording of sleep followed by a set of morning questions concerning the prior night's sleep quality. Questionnaires concerning demographics and psychological characteristics were also collected prior to the overnight recording and entered into multivariable models. Two machine learning algorithms, lasso penalized regression and random forests, determined variable selection and the ordering of variable importance separately for men and women.

RESULTS

Thirty-eight sleep, demographic and clinical correlates of sleep quality were considered. Together, these multivariable models explained only 11-17% of the variance in predicting subjective sleep quality. Objective sleep efficiency emerged as the strongest correlate of subjective sleep quality across all models, and across both sexes. Greater total sleep time and sleep stage transitions were also significant objective correlates of subjective sleep quality. The amount of slow wave sleep obtained was not determined to be important.

CONCLUSIONS

Overall, the commonly obtained measures of polysomnographically-defined sleep contributed little to subjective ratings of prior-night sleep quality. Though they explained relatively little of the variance, sleep efficiency, total sleep time and sleep stage transitions were among the most important objective correlates.

Retrospective, nonrandomized controlled study on autoadjusting, dual-pressure positive airway pressure therapy for a consecutive series of complex insomnia disorder patients

PURPOSE

Emerging evidence shows that positive airway pressure (PAP) treatment of obstructive sleep apnea (OSA) and upper airway resistance syndrome (UARS) in chronic insomnia patients (proposed "complex insomnia" disorder) leads to substantial decreases in insomnia severity. Although continuous PAP (CPAP) is the pressure mode most widely researched, intolerance to fixed pressurized air is rarely investigated or described in comorbidity patients. This retrospective study examined dual pressure, autoadjusting PAP modes in chronic, complex insomnia disorder patients.

PATIENTS AND METHODS

Chronic insomnia disorder patients (mean [SD] insomnia severity index [ISI] =19.11 [3.34]) objectively diagnosed with OSA or UARS and using either autobilevel PAP device or adaptive servoventilation (ASV) device after failing CPAP therapy (frequently due to intolerance to pressurized air, poor outcomes, or emergence of CSA) were divided into PAP users (≥20 h/wk) and partial users (<20 h/wk) for comparison. Subjective and objective baseline and follow-up measures were analyzed.

RESULTS

Of the 302 complex insomnia patients, PAP users (n=246) averaged 6.10 (1.78) nightly hours and 42.71 (12.48) weekly hours and partial users (n=56) averaged 1.67 (0.76) nightly hours and 11.70 (5.31) weekly hours. For mean (SD) decreases in total ISI scores, a significant (group × time) interaction was observed (F[1,300]=13.566; P<0.0001) with PAP users (-7.59 [5.92]; d=1.63) showing superior results to partial users (-4.34 [6.13]; d=0.81). Anecdotally, patients reported better tolerability with advanced PAP compared to previous experience with CPAP. Both adaptive servoventilation and autobilevel PAP showed similar ISI score improvement without statistical differences between devices. Total weekly hours of PAP use correlated inversely with change in insomnia symptoms (r=-0.256, P<0.01).

CONCLUSION

Insomnia severity significantly decreased in patients using autoadjusting PAP devices, but the study design restricts interpretation to an association. Future research must elucidate the interaction between insomnia and OSA/UARS as well as the adverse influence of pressure intolerance on PAP adaptation in complex insomnia patients. Randomized controlled studies must determine whether advanced PAP modes provide benefits over standard CPAP modes in these comorbidity patients.

Development of the National Healthy Sleep Awareness Project Sleep Health Surveillance Questions

OBJECTIVES

For the first time ever, as emphasized by inclusion in the Healthy People 2020 goals, sleep health is an emphasis of national health aims. The National Healthy Sleep Awareness Project (NHSAP) was tasked to propose questions for inclusion in the next Behavioral Risk Factor Surveillance System (BRFSS), a survey that includes a number of questions that target behaviors thought to impact health, as a means to measure community sleep health. The total number of questions could not exceed five, and had to include an assessment of the risk for obstructive sleep apnea (OSA).

METHODS

An appointed workgroup met via teleconference and face-to-face venues to develop an inventory of published survey questions being used to identify sleep health, to develop a framework on which to analyze the strengths and weaknesses of current survey questions concerning sleep, and to develop recommendations for sleep health and disease surveillance questions going forward.

RESULTS

The recommendation was to focus on certain existing BRFSS questions pertaining to sleep duration, quality, satisfaction, daytime alertness, and to add to these other BRFSS existing questions to make a modified STOP-BANG questionnaire (minus the N for neck circumference) to assess for risk of OSA.

CONCLUSIONS

Sleep health is an important dimension of health that has previously received less attention in national health surveys. We believe that 5 questions recommended for the upcoming BRFSS question banks will assist as important measures of sleep health, and may help to evaluate the effectiveness of interventions to improve sleep health in our nation.

Labile sleep promotes awareness of abstract knowledge in a serial reaction time task

Sleep has been identified as a critical brain state enhancing the probability of gaining insight into covert task regularities. Both non-rapid eye movement (NREM) and rapid eye movement (REM) sleep have been implicated with offline re-activation and reorganization of memories supporting explicit knowledge generation. According to two-stage models of sleep function, offline processing of information during sleep is sequential requiring multiple cycles of NREM and REM sleep stages. However, the role of overnight dynamic sleep macrostructure for insightfulness has not been studied so far. In the present study, we test the hypothesis that the frequency of interactions between NREM and REM sleep stages might be critical for awareness after sleep. For that aim, the rate of sleep stage transitions was evaluated in 53 participants who learned implicitly a serial reaction time task (SRTT) in which a determined sequence was inserted. The amount of explicit knowledge about the sequence was established by verbal recall after a night of sleep following SRTT learning. Polysomnography was recorded in this night and in a control night before and was analyzed to compare the rate of sleep-stage transitions between participants who did or did not gain awareness of task regularity after sleep. Indeed, individual ability of explicit knowledge generation was strongly associated with increased rate of transitions between NREM and REM sleep stages and between light sleep stages and slow wave sleep. However, the rate of NREM-REM transitions specifically predicted the amount of explicit knowledge after sleep in a trait-dependent way. These results demonstrate that enhanced lability of sleep goes along with individual ability of knowledge awareness. Observations suggest that facilitated dynamic interactions between sleep stages, particularly between NREM and REM sleep stages play a role for offline processing which promotes rule extraction and awareness.

Nocturnal Sleep Dynamics Identify Narcolepsy Type 1

STUDY OBJECTIVES

To evaluate the reliability of nocturnal sleep dynamics in the differential diagnosis of central disorders of hypersomnolence.

DESIGN

Cross-sectional.

SETTING

Sleep laboratory.

PATIENTS

One hundred seventy-five patients with hypocretin-deficient narcolepsy type 1 (NT1, n = 79), narcolepsy type 2 (NT2, n = 22), idiopathic hypersomnia (IH, n = 22), and "subjective" hypersomnolence (sHS, n = 52).

INTERVENTIONS

None.

METHODS

Polysomnographic (PSG) work-up included 48 h of continuous PSG recording. From nocturnal PSG conventional sleep macrostructure, occurrence of sleep onset rapid eye movement period (SOREMP), sleep stages distribution, and sleep stage transitions were calculated. Patient groups were compared, and receiver operating characteristic (ROC) curve analysis was used to test the diagnostic utility of nocturnal PSG data to identify NT1.

RESULTS

Sleep macrostructure was substantially stable in the 2 nights of each diagnostic group. NT1 and NT2 patients had lower latency to rapid eye movement (REM) sleep, and NT1 patients showed the highest number of awakenings, sleep stage transitions, and more time spent in N1 sleep, as well as most SOREMPs at daytime PSG and at multiple sleep latency test (MSLT) than all other groups. ROC curve analysis showed that nocturnal SOREMP (area under the curve of 0.724 ± 0.041, P < 0.0001), percent of total sleep time spent in N1 (0.896 ± 0.023, P < 0.0001), and the wakefulness-sleep transition index (0.796 ± 0.034, P < 0.0001) had a good sensitivity and specificity profile to identify NT1 sleep, especially when used in combination (0.903 ± 0.023, P < 0.0001), similarly to SOREMP number at continuous daytime PSG (0.899 ± 0.026, P < 0.0001) and at MSLT (0.956 ± 0.015, P < 0.0001).

CONCLUSIONS

Sleep macrostructure (i.e. SOREMP, N1 timing) including stage transitions reliably identifies hypocretin-deficient narcolepsy type 1 among central disorders of hypersomnolence.

Sleep continuity: a new metric to quantify disrupted hypnograms in non-sedated intensive care unit patients

INTRODUCTION

Sleep in intensive care unit (ICU) patients is severely altered. In a large proportion of critically ill patients, conventional sleep electroencephalogram (EEG) patterns are replaced by atypical sleep. On the other hand, some non-sedated patients can display usual sleep EEG patterns. In the latter, sleep is highly fragmented and disrupted and conventional rules may not be optimal. We sought to determine whether sleep continuity could be a useful metric to quantify the amount of sleep with recuperative function in critically ill patients with usual sleep EEG features.

METHODS

We retrospectively reanalyzed polysomnographies recorded in non-sedated critically ill patients requiring non-invasive ventilation (NIV) for acute hypercapnic respiratory failure. Using conventional rules, we built two-state hypnograms (sleep and wake) and identified all sleep episodes. The percentage of time spent in sleep bouts (<10 minutes), short naps (>10 and <30 minutes) and long naps (>30 minutes) was used to describe sleep continuity. In a first study, we compared these measures regarding good (NIV success) or poor outcome (NIV failure). In a second study performed on a different patient group, we compared these measurements during NIV and during spontaneous breathing.

RESULTS

While fragmentation indices were similar in the two groups, the percentage of total sleep time spent in short naps was higher and the percentage of sleep time spent in sleep bouts was lower in patients with successful NIV. The percentage of total sleep time spent in long naps was higher and the percentage of sleep time spent in sleep bouts was lower during NIV than during spontaneous breathing; the level of reproducibility of sleep continuity measures between scorers was high.

CONCLUSIONS

Sleep continuity measurements could constitute a clinically relevant and reproducible assessment of sleep disruption in non-sedated ICU patients with usual sleep EEG.

Combining hidden Markov models for comparing the dynamics of multiple sleep electroencephalograms

In this manuscript, we consider methods for the analysis of populations of electroencephalogram signals during sleep for the study of sleep disorders using hidden Markov models (HMMs). Notably, we propose an easily implemented method for simultaneously modeling multiple time series that involve large amounts of data. We apply these methods to study sleep-disordered breathing (SDB) in the Sleep Heart Health Study (SHHS), a landmark study of SDB and cardiovascular consequences. We use the entire, longitudinally collected, SHHS cohort to develop HMM population parameters, which we then apply to obtain subject-specific Markovian predictions. From these predictions, we create several indices of interest, such as transition frequencies between latent states. Our HMM analysis of electroencephalogram signals uncovers interesting findings regarding differences in brain activity during sleep between those with and without SDB. These findings include stability of the percent time spent in HMM latent states across matched diseased and non-diseased groups and differences in the rate of transitioning.

Nocturnal awakening and sleep efficiency estimation using unobtrusively measured ballistocardiogram

Fragmented sleep due to frequent awakenings represents a major cause of impaired daytime performance and adverse health outcomes. Currently, the gold standard for studying and assessing sleep fragmentation is polysomnography (PSG). Here, we propose an alternative method for real-time detection of nocturnal awakening via ballistocardiography using an unobtrusive polyvinylidene fluoride (PVDF) film sensor on a bed mattress. From ballistocardiogram, heart rate and body movement information were extracted to develop an algorithm for classifying sleeping and awakening epochs. In total, ten normal subjects (mean age 38.7 ± 14.6 years) and ten patients with obstructive sleep apnea (OSA) (mean age 44.2 ± 16.5 years) of varying symptom severity participated in this study. Our study detected awakening epochs with an average sensitivity of 85.3% and 85.2%, specificity of 98.4% and 97.7%, accuracy of 97.4% and 96.5%, and Cohen's kappa coefficient of 0.83 and 0.81 for normal subjects and OSA patients, respectively. Also, sleep efficiency was estimated using detected awakening epochs and then compared with PSG results. Mean absolute errors in sleep efficiency were 1.08% and 1.44% for normal subjects and OSA patients, respectively. The results presented here indicate that our suggested method could be reliably applied to real-time nocturnal awakening detection and sleep efficiency estimation. Furthermore, our method may ultimately be an effective tool for long-term, home monitoring of sleep-wake behavior.

Enveloping Spectral Surfaces: Covariate Dependent Spectral Analysis of Categorical Time Series

Motivated by problems in Sleep Medicine and Circadian Biology, we present a method for the analysis of cross-sectional categorical time series collected from multiple subjects where the effect of static continuous-valued covariates is of interest. Toward this goal, we extend the spectral envelope methodology for the frequency domain analysis of a single categorical process to cross-sectional categorical processes that are possibly covariate dependent. The analysis introduces an enveloping spectral surface for describing the association between the frequency domain properties of qualitative time series and covariates. The resulting surface offers an intuitively interpretable measure of association between covariates and a qualitative time series by finding the maximum possible conditional power at a given frequency from scalings of the qualitative time series conditional on the covariates. The optimal scalings that maximize the power provide scientific insight by identifying the aspects of the qualitative series which have the most pronounced periodic features at a given frequency conditional on the value of the covariates. To facilitate the assessment of the dependence of the enveloping spectral surface on the covariates, we include a theory for analyzing the partial derivatives of the surface. Our approach is entirely nonparametric, and we present estimation and asymptotics in the setting of local polynomial smoothing.

The AASM Scoring Manual four years later

PURPOSE OF REVIEW

Review published studies and critiques which evaluate the impact and effects of the American Academy of Sleep Medicine (AASM) Sleep Scoring Manual in the four years since its publication.

FINDINGS

USING THE AASM MANUAL RULES TO SCORE SLEEP AND EVENTS IN A POLYSOMNOGRAM (PSG) RESULTS IN: (1) very large differences in apnea-hypopnea indexes (AHI) when using the recommended and alternative rule for scoring hypopneas in adults; (2) increases in NREM 1 and sleep stage shifts with compensatory decreases in NREM 2 in children and adults when following rule 5.C.b. for ending NREM 2 sleep; (3) increases in NREM 3 in adults scoring slow wave activity in the frontal EEG derivations; (4) improved interscorer reliability; and (5) successfully identified fragmented sleep in children with obstructive sleep apnea (OSA) from primary snorers or normal controls because they had more NREM 1 and stage shifts using rule 5.C.b. Criticism of the Manual most often cited: (1) two rules for scoring hypopneas; (2) alternative EEG montage cancellation effects; (3) scoring stages 3 and 4 as NREM 3; and (4) too few rules for scoring arousals and REM sleep without atonia.

SUMMARY

Four years have passed since the AASM Scoring Manual was published with far less criticism than those who developed it feared. The AASM Manual provides a foundation upon which we all can build rules and methods which identify the complexity of sleep and its disorders.

Early-life trauma is associated with rapid eye movement sleep fragmentation among military veterans

The role of sleep in the relations between early-life trauma and the development of adverse psychological trajectories is relatively unknown and was the primary aim of the present study. Military veterans were evaluated for posttraumatic stress disorder, combat exposure, trauma history, sleep quality, disruptive nocturnal behaviors, and a subsample completed overnight polysomnography that yielded objectively measured sleep parameters. When relevant variables were controlled, increased earlier-life traumatic event exposure was associated with increased rapid-eye-movement sleep (REMs) fragmentation, and increased REMs fragmentation was associated with increased later-life disruptive nocturnal behaviors. REMs fragmentation carried an indirect relation between earlier-life trauma and later-life disruptive nocturnal behaviors. Objectively measured sleep parameters were used to describe REMs fragmentation physiology. The current findings elucidate the important role that earlier-life trauma exposure may have in the development of REM sleep physiology, and how this altered sleep physiology may have dynamic influences on subsequent posttraumatic stress symptoms in adulthood.

Mixed effect Poisson log-linear models for clinical and epidemiological sleep hypnogram data

Bayesian Poisson log-linear multilevel models scalable to epidemiological studies are proposed to investigate population variability in sleep state transition rates. Hierarchical random effects are used to account for pairings of subjects and repeated measures within those subjects, as comparing diseased with non-diseased subjects while minimizing bias is of importance. Essentially, non-parametric piecewise constant hazards are estimated and smoothed, allowing for time-varying covariates and segment of the night comparisons. The Bayesian Poisson regression is justified through a re-derivation of a classical algebraic likelihood equivalence of Poisson regression with a log(time) offset and survival regression assuming exponentially distributed survival times. Such re-derivation allows synthesis of two methods currently used to analyze sleep transition phenomena: stratified multi-state proportional hazards models and log-linear generalized estimating equations (GEE) models for transition counts. An example data set from the Sleep Heart Health Study is analyzed. Supplementary material includes the analyzed data set as well as the code for a reproducible analysis.

Sleep-stage dynamics in patients with chronic fatigue syndrome with or without fibromyalgia

STUDY OBJECTIVES

Chronic fatigue syndrome (CFS) and fibromyalgia (FM) are medically unexplained conditions that often have overlapping symptoms, including sleep-related complaints. However, differences between the 2 conditions have been reported, and we hypothesized that dynamic aspects of sleep would be different in the 2 groups of patients.

PARTICIPANTS

Subjects were 26 healthy control subjects, 14 patients with CFS but without FM (CFS alone), and 12 patients with CFS and FM (CFS+FM)-all women.

MEASUREMENTS AND RESULTS

We studied transition probabilities and rates between sleep stages (waking, rapid eye movement [REM] sleep, stage 1 [S1], stage 2 [S2], and slow-wave sleep [SWS]) and duration distributions of each sleep stage. We found that the probability of transition from REM sleep to waking was significantly greater in subjects with CFS alone than in control subjects, which may be the specific sleep problem for people with CFS alone. Probabilities of (a) transitions from waking, REM sleep, and S1 to S2 and (b) those from SWS to waking and S1 were significantly greater in subjects with CFS+FM than in control subjects; in addition, rates of these transitions were also significantly increased in subjects with CFS+FM. Result (a) might indicate increased sleep pressure in subjects with CFS+FM whereas result (b) may be the specific sleep problem of subjects with CFS+FM. We also found that shorter durations of S2 sleep are specific to patients with CFS+FM, not to CFS alone.

CONCLUSIONS

These results suggest that CFS and FM may be different illnesses associated with different problems of sleep regulation.