Quantifying sleep architecture dynamics and individual differences using big data and Bayesian networks

Gender differences in the relationship between sleep and age in a Brazilian cohort: the Baependi Heart Study

Gender and age are well-established determinants of health and sleep health that influence overall health, which also often varies by gender and age. Sleep architecture is an important component of sleep health. The goal of this analysis was to examine whether associations between age and sleep stages differ by gender in the absence of moderate-severe obstructive sleep apnea (OSA) in a rural setting in Brazil. This study conducted polysomnography recordings in the Baependi Heart Study, a cohort of Brazilian adults. Our sample included 584 women and 309 men whose apnea-hypopnea index was ≤15 events/h. We used splines to distinguish non-linear associations between age, total sleep time, wake after sleep onset (WASO), N2, N3, and rapid-eye-movement sleep. The mean (standard deviation; range) age was 47 (14; 18-89) years. All sleep outcomes were associated with age. Compared to men, women had more N3 sleep and less WASO after adjusting for age. Model-based comparisons between genders at specific ages showed statistically higher mean WASO for men at ages 60 (+13.6 min) and 70 years (+19.5 min) and less N3 for men at ages 50 (-13.2 min), 60 (-19.0 min), and 70 years (-19.5 min) but no differences at 20, 30, 40 or 80 years. The other sleep measures did not differ by gender at any age. Thus, even in the absence of moderate-severe OSA, sleep architecture was associated with age across adulthood, and there were gender differences in WASO and N3 at older ages in this rural community.

Effects of smoking on sleep architecture and ventilatory parameters including apneas: Results of the Tab-OSA study

Background

The interaction between smoking and sleep seems appears to be bidirectional, but few studies evaluated the impact of smoking and its cessation on objective sleep parameters. In this context, this new study aimed to assess the impact of smoking and its cessation on sleep architecture and on ventilatory sleep parameters, particularly the presence of sleep apnea syndrome (apnea-hypopnea index (AHI)≥15). Methods: Patients hospitalized for polysomnographic sleep exploration were compared according to their smoking status: active smokers (AS), former smokers (FS), non-smokers (NoNi). Psychiatric and non-psychiatric co-morbidities and treatment or substance use were taken into account in the analyses.

Results

A total of 170 participants were included (N = 37 FS, 39 AS, 86 NoNi). A significant decrease in the mean nocturnal O2 saturation was observed for FS and AS compared to NoNi. No differences were found regarding AHI. Regarding sleep architecture, we observed a significant decrease in the slow wave sleep duration for AS compared to NoNi, and interestingly not between FS and NoNi.

Conclusion

This study suggests that current smokers suffer from alterations in both sleep architecture and ventilatory parameters, the later appears to persist even after smoking cessation.

Modeling the Impact of Inter-Rater Disagreement on Sleep Statistics Using Deep Generative Learning

Sleep staging is the process by which an overnight polysomnographic measurement is segmented into epochs of 30 seconds, each of which is annotated as belonging to one of five discrete sleep stages. The resulting scoring is graphically depicted as a hypnogram, and several overnight sleep statistics are derived, such as total sleep time and sleep onset latency. Gold standard sleep staging as performed by human technicians is time-consuming, costly, and comes with imperfect inter-scorer agreement, which also results in inter-scorer disagreement about the overnight statistics. Deep learning algorithms have shown promise in automating sleep scoring, but struggle to model inter-scorer disagreement in sleep statistics. To that end, we introduce a novel technique using conditional generative models based on Normalizing Flows that permits the modeling of the inter-rater disagreement of overnight sleep statistics, termed U-Flow. We compare U-Flow to other automatic scoring methods on a hold-out test set of 70 subjects, each scored by six independent scorers. The proposed method achieves similar sleep staging performance in terms of accuracy and Cohen's kappa on the majority-voted hypnograms. At the same time, U-Flow outperforms the other methods in terms of modeling the inter-rater disagreement of overnight sleep statistics. The consequences of inter-rater disagreement about overnight sleep statistics may be great, and the disagreement potentially carries diagnostic and scientifically relevant information about sleep structure. U-Flow is able to model this disagreement efficiently and can support further investigations into the impact inter-rater disagreement has on sleep medicine and basic sleep research.

Conscious entry into sleep: Yoga Nidra and accessing subtler states of consciousness

Human sleep is a dynamic and complex process comprising sleep stages with REM and NREM sleep characteristics that come in cycles. During sleep, there is a loss of responsiveness or a perceptual loss of conscious awareness with increasing thresholds for wakefulness as sleep progresses. There are brief bursts of wakefulness or Wake After Sleep Onset (WASO) throughout a nocturnal sleep. Conscious experience during nocturnal sleep is known to occur during lucid dreaming when one is aware during dreams when the dream is occurring. Most cultures have known lucid dreaming since antiquity. However, conscious experience during dreamless sleep is relatively lesser known. Nevertheless, selected Indo-Tibetan meditation literature has documented it since antiquity. Minimal Phenomenal Experience (MPE) research describes lucid dreamless sleep as its target phenomenology. "Conscious entry into sleep" posits tonic alertness is maintained post sleep onset through the sleep stages for sustained durations of time until an eventual loss of conscious awareness. Entering sleep consciously and being aware during dreamless sleep, including Slow Wave Activity, is plausibly to be in the state of "Yoga Nidra" or Yogic sleep. An attentive sleepful state provides access to subtler states of consciousness and significantly deepens the levels of silence. It is phenomenologically distinct from hypnagogic hallucinations and lucid dreaming. Unfortunately, sleep studies validating this phenomenology are yet to be done. Therefore, an experimental methodology akin to those used in lucid dreaming experiments is described.

Sex differences in predictors and regional patterns of brain age gap estimates

The brain-age-gap estimate (brainAGE) quantifies the difference between chronological age and age predicted by applying machine-learning models to neuroimaging data and is considered a biomarker of brain health. Understanding sex differences in brainAGE is a significant step toward precision medicine. Global and local brainAGE (G-brainAGE and L-brainAGE, respectively) were computed by applying machine learning algorithms to brain structural magnetic resonance imaging data from 1113 healthy young adults (54.45% females; age range: 22-37 years) participating in the Human Connectome Project. Sex differences were determined in G-brainAGE and L-brainAGE. Random forest regression was used to determine sex-specific associations between G-brainAGE and non-imaging measures pertaining to sociodemographic characteristics and mental, physical, and cognitive functions. L-brainAGE showed sex-specific differences; in females, compared to males, L-brainAGE was higher in the cerebellum and brainstem and lower in the prefrontal cortex and insula. Although sex differences in G-brainAGE were minimal, associations between G-brainAGE and non-imaging measures differed between sexes with the exception of poor sleep quality, which was common to both. While univariate relationships were small, the most important predictor of higher G-brainAGE was self-identification as non-white in males and systolic blood pressure in females. The results demonstrate the value of applying sex-specific analyses and machine learning methods to advance our understanding of sex-related differences in factors that influence the rate of brain aging and provide a foundation for targeted interventions.

Adaptive Solutions to the Problem of Vulnerability During Sleep

Sleep is a behavioral state whose quantity and quality represent a trade-off between the costs and benefits this state provides versus the costs and benefits of wakefulness. Like many species, we humans are particularly vulnerable during sleep because of our reduced ability to monitor the external environment for nighttime predators and other environmental dangers. A number of variations in sleep characteristics may have evolved over the course of human history to reduce this vulnerability, at both the individual and group level. The goals of this interdisciplinary review paper are (1) to explore a number of biological/instinctual features of sleep that may have adaptive utility in terms of enhancing the detection of external threats, and (2) to consider relatively recent cultural developments that improve vigilance and reduce vulnerability during sleep and the nighttime. This paper will also discuss possible benefits of the proposed adaptations beyond vigilance, as well as the potential costs associated with each of these proposed adaptations. Finally, testable hypotheses will be presented to evaluate the validity of these proposed adaptations.

Sleep apnea plays a more important role on sleep N3 stage than chronic tinnitus in adults

Sleep apnea is negatively associated with N3 sleep in children. However, the association between tinnitus and sleep N3 stage was still inconclusive. We aimed to clarify the relationship between sleep apnea, chronic tinnitus, and sleep N3 stage in adults. Clinical and overnight polysomnography data of 2847 adults were collected retrospectively. Univariate and multivariate linear regression was used to test the impacts of sleep apnea indices and chronic tinnitus on the percentage of sleep N3 stage in all adults. Univariate linear regression analysis showed that sleep apnea indices, chronic tinnitus, age, sex, hypertension, diabetes mellitus, dyslipidemia, subjective insomnia, sleep efficiency, and rapid eye movement sleep were significantly associated with sleep N3 stage. However, multivariate linear regression showed that apnea-hyponea index, but not chronic tinnitus, has a significant negative association with the percentage of sleep N3 stage. Sleep apnea plays a more important role on sleep N3 stage than chronic tinnitus in adults.

[Arousal characteristics in different OSAHS populations]

Objective:This study sought to explore the effect of age on arousal index in patients with OSAHS, and the significance of arousal index among different phenotypes identified through cluster analysis according to clinical symptoms and complications. Methods:A total of 607 adult patients with OSAHS who received polysomnography in the Second Affiliated Hospital of Xi'an Jiaotong University from July 2020 to July 2021 were selected. All patients registered basic human data, symptoms, complaints and complications, completed the Epworth Sleepiness Scale. We explored the distribution of several PSG parameters in different age groups, and included typical symptoms and complications into cluster analysis to explore the parameter differences in patients with different phenotypes. Results:Young patients had the lowest arousal index and arousal frequency in NREM stage, in middle-aged patients, the arousal index was relatively higher, the arousal times during NREM with oxygen desaturation were the highest. Among elderly patients, the wakefulness after sleep onset（WASO） was the longest, the arousal times in REM sleep was the lowest, and the spontaneous arousal times in NREM sleep were the highest（P<0.05）. Among the 3 types according to the cluster analysis, phenotype Ⅰwas characterised by maximally complications, excessive daytime sleepiness and obesity, while memory and attention impairment were obvious in phenotype Ⅱ. Phenotype Ⅲhad minimal complications, relatively better mental state with shorter time-course of snoring and apnea. Phenotype I differed significantly by higher severity, more severe hypoxemia, higher arousal index and longer WASO time（P<0.05）. Conclusion:the arousal index distribution varies among OSAHS patients with different age, and cluster analysis shows that patients with severe symptoms and more complications tend to have higher arousal index.

Nighttime Sleep Characteristics and White Matter Integrity in Young Adults

PURPOSE

Sleep is essential for life and plays a key role for optimal physiology, brain functioning, and health. Evidence suggests a relation between sleep and cerebral white matter integrity. Human studies report that sleep duration shows a U-shaped association with brain functioning. We hypothesized that participants with longer or shorter sleep time in the nighttime period show altered microstructural white matter integrity.

PARTICIPANTS AND METHODS

Seventy-three young adult participants were evaluated. Sleep-wake cycle parameters were assessed objectively using actigraphy. Diffusion tensor imaging studies were performed to assess white matter integrity using fractional anisotropy and mean, axial, and radial diffusivities. Relations between white matter microstructure indexes and sleep parameters were investigated through tract-based spatial statistics. Participants were grouped according to their nocturnal total sleep time: 27 in the Reference sleep group (6.5-8.0 h), 23 in the Short sleep group (<6.5 h) and 23 in the Long sleep group (>8.0 h).

RESULTS

Compared with the Reference sleep group, participants in the Long sleep group showed lower fractional anisotropy (p < 0.05) and higher radial diffusivity (p < 0.05) values in white matter tracts linked to sleep regulation (corona radiata, body of the corpus callosum, superior longitudinal fasciculus, and anterior thalamic radiation).

CONCLUSION

This pattern of reduced fractional anisotropy and increased radial diffusivity in the Long sleep group indicates an association between sleep duration and lower integrity of myelin sheaths. Because myelin is continuously remodeled in the brain, nighttime sleep characteristics appear to be a key player for its quality and maintenance.

Influence of sleep fragmentation and fatigue on turnover of female nurses working rotating shifts

AIMS AND OBJECTIVES

To investigate whether the sleep quality and fatigue of female nurses working rotating shifts could be used to predict future turnover status.

BACKGROUND

Female nurses working rotating shifts often suffer from sleep problems and fatigue, and the turnover rates of nurses are generally higher than those of other hospital personnel.

DESIGN

A prospective study.

METHODS

We recruited a total of 198 female nurses working rotating shifts from December 2017 to March 2019. The nurses completed the Checklist Individual Strength (CIS) scale and wore an actigraph for seven consecutive days in order to collect their sleep parameters. Their turnover status was tracked until 31 May 2021 at which time 55 participants (27.8%) had resigned. The Cox proportional hazard model was used to analyse the factors influencing turnover. In addition, the study adhered to the STROBE checklist.

RESULTS

The results revealed significant differences between the nurses in the still-working group and the resigned group in terms of the sleep quality parameters sleep efficiency (SE) and wake after sleep onset (WASO) as well as CIS scores. WASO was significantly correlated with intensity of fatigue, and fatigue was common among all of the nurses working rotating shifts. As time progressed, the sleep quality parameter WASO and CIS scores could be used to predict turnover status after 2.4 years.

CONCLUSION

The results of this study indicated more sleep fragmentation and poor sleep efficiency in the resigned group. Sleep fragmentation was highly correlated with fatigue, and sleep fragmentation and fatigue could be used to predict turnover status.

RELEVANCE TO CLINICAL PRACTICE

We suggest that relevant hospital management pay more attention to the sleep conditions and fatigue of female nurses working rotating shifts when arranging personnel and schedules and offer them more understanding.

Sleep Measurements in Women

Sleep in women and men have been studied in several studies with higher prevalence of sleep complaints in women compared with men. Several factors can affect sleep and could be argued to contribute to sex and gender differences in general sleep. There are no differences in guidelines when measuring sleep in women but several sleep assessment tools have been validated or compared between sexes. Because there is still a lack of knowledge on sleep measurements in women, the present review aimed to produce an overview of the current knowledge of objective and subjective sleep measurements in women.

Nocturnal Brain Activity Differs with Age and Sex: Comparisons of Sleep EEG Power Spectra Between Young and Elderly Men, and Between 60-80-Year-Old Men and Women

PURPOSE

Quantification of nocturnal EEG activity has emerged as a promising extension to the conventional sleep evaluation approach. To date, studies focusing on quantitative sleep EEG data in relation to age and sex have revealed considerable variation across lifespan and differences between men and women. However, sleep EEG power values from elderly individuals are still rare. The present secondary analysis aimed to fill this gap.

PARTICIPANTS AND METHODS

Sleep EEG data of 30 healthy elderly males (mean age ± SD: 69.1 ± 5.5 years), 30 healthy elderly females (67.8 ± 5.7 years), and of 30 healthy young males (25.6 ± 2.4 years) have been collected in three different studies with the same experimental design. Each individual contributed three polysomnographic recordings without any intervention to the analysis. Sleep recordings were performed and evaluated according to the standard of the American Academy of Sleep Medicine. Sleep EEG signals were derived from 19 electrode sites. Sleep-stage specific global and regional EEG power were compared between samples using a permutation-based statistic in combination with the threshold-free cluster enhancement method.

RESULTS

The present results showed pronounced differences in sleep EEG power between older men and women. The nocturnal EEG activity of older women was generally larger than that of older men, confirming previously reported variations with sex in younger individuals. Aging was reflected by differences in EEG power between young and elderly men for lower frequencies and for the sleep spindle frequency range, again consistent with prior studies.

CONCLUSION

The findings of this investigation complement those of earlier studies. They add to the understanding of nocturnal brain activity manifestation in senior adulthood and show how it differs with age in males. Unfortunately, the lack of information on young women prevents a similar insight for females.

A Multi-Level Classification Approach for Sleep Stage Prediction With Processed Data Derived From Consumer Wearable Activity Trackers

Consumer wearable activity trackers, such as Fitbit are widely used in ubiquitous and longitudinal sleep monitoring in free-living environments. However, these devices are known to be inaccurate for measuring sleep stages. In this study, we develop and validate a novel approach that leverages the processed data readily available from consumer activity trackers (i.e., steps, heart rate, and sleep metrics) to predict sleep stages. The proposed approach adopts a selective correction strategy and consists of two levels of classifiers. The level-I classifier judges whether a Fitbit labeled sleep epoch is misclassified, and the level-II classifier re-classifies misclassified epochs into one of the four sleep stages (i.e., light sleep, deep sleep, REM sleep, and wakefulness). Best epoch-wise performance was achieved when support vector machine and gradient boosting decision tree (XGBoost) with up sampling were used, respectively at the level-I and level-II classification. The model achieved an overall per-epoch accuracy of 0.731 ± 0.119, Cohen's Kappa of 0.433 ± 0.212, and multi-class Matthew's correlation coefficient (MMCC) of 0.451 ± 0.214. Regarding the total duration of individual sleep stage, the mean normalized absolute bias (MAB) of this model was 0.469, which is a 23.9% reduction against the proprietary Fitbit algorithm. The model that combines support vector machine and XGBoost with down sampling achieved sub-optimal per-epoch accuracy of 0.704 ± 0.097, Cohen's Kappa of 0.427 ± 0.178, and MMCC of 0.439 ± 0.180. The sub-optimal model obtained a MAB of 0.179, a significantly reduction of 71.0% compared to the proprietary Fitbit algorithm. We highlight the challenges in machine learning based sleep stage prediction with consumer wearables, and suggest directions for future research.

A comprehensive scoping review of Bayesian networks in healthcare: Past, present and future

No comprehensive review of Bayesian networks (BNs) in healthcare has been published in the past, making it difficult to organize the research contributions in the present and identify challenges and neglected areas that need to be addressed in the future. This unique and novel scoping review of BNs in healthcare provides an analytical framework for comprehensively characterizing the domain and its current state. A literature search of health and health informatics literature databases using relevant keywords found 3810 articles that were reduced to 123. This was after screening out those presenting Bayesian statistics, meta-analysis or neural networks, as opposed to BNs and those describing the predictive performance of multiple machine learning algorithms, of which BNs were simply one type. Using the novel analytical framework, we show that: (1) BNs in healthcare are not used to their full potential; (2) a generic BN development process is lacking; (3) limitations exist in the way BNs in healthcare are presented in the literature, which impacts understanding, consensus towards systematic methodologies, practice and adoption; and (4) a gap exists between having an accurate BN and a useful BN that impacts clinical practice. This review highlights several neglected issues, such as restricted aims of BNs, ad hoc BN development methods, and the lack of BN adoption in practice and reveals to researchers and clinicians the need to address these problems. To map the way forward, the paper proposes future research directions and makes recommendations regarding BN development methods and adoption in practice.

Sleep structure and electroencephalographic spectral power of middle-aged or older adults: Normative values by age and sex in the Korean population

The fine structure of sleep electrocortical activity reflects health and disease. The current study provides normative data for sleep structure and electroencephalography (EEG) spectral power measures derived from overnight polysomnography (PSG) and examines the effect of age and sex among Korean middle-aged and older adults with or without obstructive sleep apnea (OSA). We analysed home PSG data from 1,153 adult participants of an ongoing population-based cohort study, the Korean Genome and Epidemiology Study. Sleep stages were visually scored and spectral power was measured on a single-channel EEG (C4-A1). We computed spectral power for five frequency ranges. The EEG power was reported in relative (%) and log-transformed absolute values (µV² ). With ageing, the proportion of N1 sleep increased, whereas N3 decreased, which is more noticeable in men than in women. The amount of N3 was relatively low in this cohort. With ageing, relative delta power decreased and alpha and sigma power increased for the whole sleep period, which was more pronounced during REM sleep in non-OSA. For men compared with women, relative theta power was lower during REM and sigma and beta were higher during N1 sleep. The differences of relative powers by age and sex in OSA were comparable to those in non-OSA. In a community-based Korean population, we present normative data of sleep structure and spectral power for middle-aged or older adults of a non-Caucasian ethnicity. The values varied with age and sex and were not influenced by sleep apnea.

Ultradian modulation of cortical arousals during sleep: effects of age and exposure to nighttime transportation noise

STUDY OBJECTIVES

The present study aimed at assessing the temporal non-rapid eye movement (NREM) EEG arousal distribution within and across sleep cycles and its modifications with aging and nighttime transportation noise exposure, factors that typically increase the incidence of EEG arousals.

METHODS

Twenty-six young (19-33 years, 12 women) and 16 older (52-70 years, 8 women) healthy volunteers underwent a 6-day polysomnographic laboratory study. Participants spent two noise-free nights and four transportation noise exposure nights, two with continuous and two characterized by eventful noise (average sound levels of 45 dB, maximum sound levels between 50 and 62 dB for eventful noise). Generalized mixed models were used to model the time course of EEG arousal rates during NREM sleep and included cycle, age, and noise as independent variables.

RESULTS

Arousal rate variation within NREM sleep cycles was best described by a u-shaped course with variations across cycles. Older participants had higher overall arousal rates than the younger individuals with differences for the first and the fourth cycle depending on the age group. During eventful noise nights, overall arousal rates were increased compared to noise-free nights. Additional analyses suggested that the arousal rate time course was partially mediated by slow wave sleep (SWS).

CONCLUSIONS

The characteristic u-shaped arousal rate time course indicates phases of reduced physiological sleep stability both at the beginning and end of NREM cycles. Small effects on the overall arousal rate by eventful noise exposure suggest a preserved physiological within- and across-cycle arousal evolution with noise exposure, while aging affected the shape depending on the cycle.

Increased prevalence of obstructive sleep apnea in women diagnosed with endometrial or breast cancer

Background

Epidemiological studies demonstrate associations between obstructive sleep apnea (OSA) and cancer incidence and mortality. The aim of this study was to measure OSA in women with breast (BC) or endometrial cancer (EC) and associations with clinico-pathological tumor variables.

Methods and findings

In a cross sectional study, women with BC (12 months) or EC (3 months) post-diagnosis were recruited from cancer clinics. We collected demographic, anthropometric data, cancer stage, grade, histopathology and history of cancer treatment and all subjects had in-laboratory polysomnography. Sleepiness was assessed with the Epworth Sleepiness Scale (ESS). We compared anthropometric and polysomnographic data between cancer groups (unpaired t-tests), and assessed relationships between cancer characteristics and OSA variables (Fishers exact test). There were no significant differences between average age (BC:59.6±8.7 years(n = 50); EC:60.3±7.7 years(n = 37)), or ESS score (BC:6.4±4.4; EC 6.8±4.7; mean±SD; all p>0.2), however, BMI was higher in EC (BC: 29.7±7.9kgm^-2; EC: 34.2±8.0 kgm^-2; p<0.05). BC had longer sleep latency (BC:31.8±32minutes; EC:19.3±17.9 minutes), less Stage 3 sleep (BC:20.0±5.2%; EC:23.6±8.2%) and more REM sleep (BC:21.1±6.9%; EC: 16.6±5.7%), all p<0.05. EC had lower average awake and asleep oxygen saturation levels (BC: 95.6±1.3%; EC: 94.6±1.9% [awake]: BC: 94.8±2.1%; EC: 93.3±2.4% [asleep]; both p<0.05). Apnea-Hypopnea Index (AHI) (BC: 21.2(7.3–36.9) events/hr; EC: 15.7 (10–33.5) events/hour (median (interquartile range)) was not different p = 0.7), however, 58% and 57% of women with BC and EC respectively, had an AHI>15 events/hour. In this small sample size group, no significant associations (all p>0.1) were detected between OSA metrics and clinico-pathological tumor variables.

Conclusion

In postmenopausal women with breast or endometrial cancer there is high prevalence of OSA, with no association with specific tumor characteristics detected. Recognition of the high prevalence of OSA in women with cancer is important to recognise as it may impact on surgical risk and quality of life.

Gender differences of clinical and polysomnographic findings with obstructive sleep apnea syndrome

Obstructive sleep apnea syndrome (OSAS) is underdiagnosed in females and gender differences in clinical and polysomnographic findings have not been widely investigated in China. We examined clinical and polysomnographic differences between males and females with OSAS in order to determine the influence of gender on clinical presentation and polysomnographic features. Data were collected from 303 adult patients diagnosed with OSAS (237 males and 66 females) from 2017 to 2019. All the patients completed physical examination, Epworth sleepiness scale, and whole night polysomnography. AVONA, univariate and multivariate logistic regression analyses were conducted to assess gender differences of clinical and polysomnographic findings with OSAS. P < 0.05 was statistically significant. The average age was 48.4 ± 12.6 years for females and 43.4 ± 12.4 years for males. Compared with female patients with OSAS, male patients were taller and heavier, had higher systolic blood pressure in the morning, shorter duration of slow wave sleep, more micro-arousal events, greater AHI, and more complex sleep apnea events. There are obvious gender differences of clinical and polysomnographic characteristics with OSAS. Understanding gender differences will contribute to better clinical recognition of OSAS in females as well as the provision of proper health care and therapeutic practice.

Is moderate depression associated with sleep stage architecture in adolescence? Testing the stage type associations using network and transition probability approaches

BACKGROUND

Depression even at the subclinical level is often accompanied by sleep disturbances, but little is known about the dynamics of the sleep stages in relation to depressive symptoms. We examined whether the amount, associations, and transition probabilities of various sleep stages were associated with depressive symptoms in a community sample of adolescents.

METHODS

The participants (N = 172, 59% girls, mean age 16.9 years) underwent overnight polysomnography and provided data on depressive symptoms (Beck Depression Inventory II). The association between depression status and total duration of each stage type was analyzed using ANOVA and survival analyses. The associations between the number of different sleep stage types were analyzed using graphical Gaussian models, mixed graphical models, and relative importance networks. A Markov chain algorithm was used to estimate the transition probabilities between each state and these probabilities were further compared between depression status groups.

RESULTS

The associations between N1 and N3 were significantly stronger in both directions of the association (p-values for interactions 0.012 and 0.006) in those with more depressive symptoms. Similarly, a stronger association was observed from N1 to wake stage in those with more depressive symptoms (p-value for interaction 0.002). In those with more depressive symptoms, it was more likely to transition from N2 to N3 and from REM to N2 compared to others.

CONCLUSIONS

These findings indicate that changes in sleep architecture are not limited to clinical depression and that the transitional dynamics of sleep stages are an important marker of subclinical depression.

RF-EMF exposure effects on sleep - Age doesn't matter in men!

BACKGROUND

Although there are several human experimental studies on short-term effects of radiofrequency electromagnetic fields (RF-EMF) on sleep, the role of effect modification by sex or age in this context has not yet been considered. In an earlier study, we observed sex differences in RF-EMF effects in elderly subjects. The present study investigated possible RF-EMF effect modifications by age in men.

METHODS

Data available for the present analysis come from three double-blind, randomized cross-over studies, in which effects of different RF-EMF exposure signals on sleep were investigated in young [sample 1: 25.3 (mean) ± 2.6 (SD) years; sample 2: 25.4 ± 2.6 years; n = 30, respectively] and older (69.1 ± 5.5 years; n = 30) healthy male volunteers. Studies comprised a screening/adaptation night followed by nine experimental nights at two-week intervals. RF-EMF exposure effect modifications by age were analysed for two different exposure signals (GSM900 at 2 W/kg, TETRA at 6 W/kg), each compared to a sham exposure. Polysomnography, during which the exposure signals were delivered by a head worn antenna, as well as sleep staging were performed according to the AASM standard. Four subjective and 30 objective sleep parameters were statistically analysed related to possible RF-EMF effects.

RESULTS

Comparisons of sleep parameters observed under sham exposure revealed highly pronounced physiological differences between young and elderly men. A consistent exposure effect in both age groups was found for a shorter latency to persistent sleep under TETRA exposure reflecting a sleep-promoting effect. Exposure effect modifications by age were observed for two of the four self-reported sleep parameters following GSM900 exposure and for arousals during REM sleep under TETRA exposure.

CONCLUSIONS

As effects of a short-term all-night RF-EMF exposure on sleep occurred only sporadically in young and elderly men, it seems that age doesn't matter in this respect. However, as long as there are no corresponding data from young healthy women that would allow a comparison with the data from elderly women, this assumption cannot be conclusively verified. Nevertheless, the present results are not indicative of any adverse health effects.

An experimental study on effects of radiofrequency electromagnetic fields on sleep in healthy elderly males and females: Gender matters!

BACKGROUND

Results from human experimental studies investigating possible effects of radiofrequency electromagnetic fields (RF-EMF) on sleep are heterogeneous. So far, there is no study on possible sex-differences in RF-EMF effects.

OBJECTIVES

The present study aimed at analyzing differences in RF-EMF effects on the macrostructure of sleep between healthy elderly males and females.

METHODS

With a double-blind, randomized, sham-controlled cross-over design effects of two RF-EMF exposures (GSM900 and TETRA) on sleep were investigated in samples of 30 elderly healthy male and 30 healthy elderly female volunteers. Participants underwent each of the three exposure conditions on three occassions following an individually randomized order resulting in a total of nine study nights per participant. Exposure was delivered for 30 min prior to sleep and for the whole night (7.5 h) by a head worn antenna specifically designed for the projects. The peak spatial absorption rate averaged over time in head tissues (psSAR10g) was 6 W/kg for TETRA and 2 W/kg for GSM900. Thirty variables characterising the macrostructure of sleep and arousals as well as four subjective sleep variables were considered for statistical analyses.

RESULTS

Multivariate analyses revealed that exposure to GSM900 and/or TETRA resulted in a significant reduction in arousals, a shorter latency to sleep stage N3, and a shorter self-reported time awake after sleep in both males and females. Exposure effects depending on sex (significant interactions) were observed. Latency to sleep stage R was shorter in females and tended to be longer in males under both exposures. Latency to stage N3 was shorter in females under TETRA exposure and almost not affected in males. The time awake within the sleep period under TETRA exposure was shorter in females and only slightly longer in males. Under GSM exposure, the self-rated total sleep time tended to be longer in females and to be shorter in males. Finally, the number of awakenings was lower only in females and tended to be higher in males under GSM exposure.

DISCUSSION

With regard to RF-EMF effects on human sleep it seems that gender matters since GSM900 and TETRA led to significantly more exposure effects in females. Regardless of gender, none of the observed changes is indicative of a sleep disturbing effect of RF-EMF exposure. Observed effects might be mediated by skin related thermoregulatory mechanisms.

Understanding the interplay of sleep and aging: Methodological challenges

In quest of new avenues to explain, predict, and treat pathophysiological conditions during aging, research on sleep and aging has flourished. Despite the great scientific potential to pinpoint mechanistic pathways between sleep, aging, and pathology, only little attention has been paid to the suitability of analytic procedures applied to study these interrelations. On the basis of electrophysiological sleep and structural brain data of healthy younger and older adults, we identify, illustrate, and resolve methodological core challenges in the study of sleep and aging. We demonstrate potential biases in common analytic approaches when applied to older populations. We argue that uncovering age-dependent alterations in the physiology of sleep requires the development of adjusted and individualized analytic procedures that filter out age-independent interindividual differences. Age-adapted methodological approaches are thus required to foster the development of valid and reliable biomarkers of age-associated cognitive pathologies.

Parallelism Strategies for Big Data Delayed Transfer Entropy Evaluation

Generated and collected data have been rising with the popularization of technologies such as Internet of Things, social media, and smartphone, leading big data term creation. One class of big data hidden information is causality. Among the tools to infer causal relationships, there is Delay Transfer Entropy (DTE); however, it has a high demanding processing power. Many approaches were proposed to overcome DTE performance issues such as GPU and FPGA implementations. Our study compared different parallel strategies to calculate DTE from big data series using a heterogeneous Beowulf cluster. Task Parallelism was significantly faster in comparison to Data Parallelism. With big data trend in sight, these results may enable bigger datasets analysis or better statistical evidence.

Sleep quality prediction in caregivers using physiological signals

Most caregivers of people with dementia (CPWD) experience a high degree of stress due to the demands of providing care, especially when addressing unpredictable behavioral and psychological symptoms of dementia. Such challenging responsibilities make caregivers susceptible to poor sleep quality with detrimental effects on their overall health. Hence, monitoring caregivers' sleep quality can provide important CPWD stress assessment. Most current sleep studies are based on polysomnography, which is expensive and potentially disrupts the caregiving routine. To address these issues, we propose a clinical decision support system to predict sleep quality based on trends of physiological signals in the deep sleep stage. This system utilizes four raw physiological signals using a wearable device (E4 wristband): heart rate variability, electrodermal activity, body movement, and skin temperature. To evaluate the performance of the proposed method, analyses were conducted on a two-week period of sleep monitored on eight CPWD. The best performance is achieved using the random forest classifier with an accuracy of 75% for sleep quality, and 73% for restfulness, respectively. We found that the most important features to detect these measures are sleep efficiency (ratio of amount of time asleep to the amount of time in bed) and skin temperature. The results from our sleep analysis system demonstrate the capability of using wearable sensors to measure sleep quality and restfulness in CPWD.

Short Duration Repetitive Transcranial Electrical Stimulation During Sleep Enhances Declarative Memory of Facts

Transcranial electrical stimulation (tES) during sleep has been shown to successfully modulate memory consolidation. Here, we tested the effect of short duration repetitive tES (SDR-tES) during a daytime nap on the consolidation of declarative memory of facts in healthy individuals. We use a previously described approach to deliver the stimulation at regular intervals during non-rapid eye movement (NREM) sleep, specifically stage NREM2 and NREM3. Similar to previous studies using tES, we find enhanced memory performance compared to sham both after sleep and 48 h later. We also observed an increase in the proportion of time spent in NREM3 sleep and SDR-tES boosted the overall rate of slow oscillations (SOs) during NREM2/NREM3 sleep. Retrospective investigation of brain activity immediately preceding stimulation suggests that increases in the SO rate are more likely when stimulation is delivered during quiescent and asynchronous periods of activity in contrast to other closed-loop approaches which target phasic stimulation during ongoing SOs.

SleepOMICS: How Big Data Can Revolutionize Sleep Science

Sleep disorders have reached epidemic proportions worldwide, affecting the youth as well as the elderly, crossing the entire lifespan in both developed and developing countries. "Real-life" behavioral (sensor-based), molecular, digital, and epidemiological big data represent a source of an impressive wealth of information that can be exploited in order to advance the field of sleep research. It can be anticipated that big data will have a profound impact, potentially enabling the dissection of differences and oscillations in sleep dynamics and architecture at the individual level ("sleepOMICS"), thus paving the way for a targeted, "one-size-does-not-fit-all" management of sleep disorders ("precision sleep medicine").

Does working memory improvement benefit from sleep in older adults?

Working Memory (WM), is an important factor influencing many higher-order cognitive functions that decline with age. Repetitive training appears to increase WM, yet the mechanisms underlying this improvement are not understood. Sleep has been shown to benefit long-term memory formation and may also play a role in WM enhancement in young adults. However, considering age-related decline in sleep, it is uninvestigated whether sleep will facilitate WM in older adults. In the present work, we investigated the impact of a nap, quiet wakefulness (QW) and active wakefulness (AW) on within-day training on the Operation Span (OSPAN) task in older adults. Improvement in WM was found following a nap and QW, but not active wake. Furthermore, better WM was associated with shared electrophysiological features, including slow oscillation (SO, 0.5-1 Hz) power in both the nap and QW, and greater coupling between SO and sigma (12-15 Hz) in the nap. In summary, our data suggest that WM improvement in older adults occurs opportunistically during offline periods that afford enhancement in slow oscillation power, and that further benefits may come with cross-frequency coupling of neural oscillations during sleep.