Changes of sleep-stage transitions due to ageing and sleep disorder

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.

The Effect of Improving Preoperative Sleep Quality on Perioperative Pain by Zolpidem in Patients Undergoing Laparoscopic Colorectal Surgery: A Prospective, Randomized Study

Methods

A prospective, randomized study was conducted with 88 patients undergoing laparoscopic colorectal surgery. The experimental group (S group, n = 44) was given 10 mg of zolpidem tartrate one night before the surgical procedure, while no medication was given to the control group (C group, n = 44). The primary outcome was the intraoperative remifentanil consumption. Sufentanil consumption, average patient-controlled analgesia (PCA) effective press times, the visual analog scale (VAS) scores, and incidences of postoperative nausea and vomiting (PONV) were recorded at 6 h (T1), 12 h (T2), and 24 h (T3) postoperatively.

Results

The intraoperative remifentanil consumption was significantly lower in the S group than that in the C group (p < 0.01). Sufentanil consumption at 6 h and 12 h postoperatively was significantly lower in the S group than that in the C group (p < 0.05); average PCA effective press times and VAS scores, at 6 h and 12 h postoperatively, were significantly lower in the S group than those in the C group (p < 0.01); differences between groups 24 h postoperatively were not significant. No significant between-group difference was noted in the incidence of nausea and vomiting.

Conclusion

Improving patients' sleep quality the night before surgical procedure by zolpidem can decrease the usage of intraoperative analgesics and reduce postoperative pain.

Sleep in Normal Aging, Homeostatic and Circadian Regulation and Vulnerability to Sleep Deprivation

In the context of geriatric research, a growing body of evidence links normal age-related changes in sleep with many adverse health outcomes, especially a decline in cognition in older adults. The most important sleep alterations that continue to worsen after 60 years involve sleep timing, (especially early wake time, phase advance), sleep maintenance (continuity of sleep interrupted by numerous awakenings) and reduced amount of sigma activity (during non-rapid eye movement (NREM) sleep) associated with modifications of sleep spindle characteristics (density, amplitude, frequency) and spindle-Slow Wave coupling. After 60 years, there is a very clear gender-dependent deterioration in sleep. Even if there are degradations of sleep after 60 years, daytime wake level and especially daytime sleepiness is not modified with age. On the other hand, under sleep deprivation condition, older adults show smaller cognitive impairments than younger adults, suggesting an age-related lower vulnerability to extended wakefulness. These sleep and cognitive age-related modifications would be due to a reduced homeostatic drive and consequently a reduced sleep need, an attenuation of circadian drive (reduction of sleep forbidden zone in late afternoon and wake forbidden zone in early morning), a modification of the interaction of the circadian and homeostatic processes and/or an alteration of subcortical structures involved in generation of circadian and homeostatic drive, or connections to the cerebral cortex with age. The modifications and interactions of these two processes with age are still uncertain, and still require further investigation. The understanding of the respective contribution of circadian and homeostatic processes in the regulation of neurobehavioral function with aging present a challenge for improving health, management of cognitive decline and potential early chronobiological or sleep-wake interventions.

Estimation of Sleep Stages Analyzing Respiratory and Movement Signals

The scoring of sleep stages is an essential part of sleep studies. The main objective of this research is to provide an algorithm for the automatic classification of sleep stages using signals that may be obtained in a non-obtrusive way. After reviewing the relevant research, the authors selected a multinomial logistic regression as the basis for their approach. Several parameters were derived from movement and breathing signals, and their combinations were investigated to develop an accurate and stable algorithm. The algorithm was implemented to produce successful results: the accuracy of the recognition of Wake/NREM/REM stages is equal to 73%, with Cohen's kappa of 0.44 for the analyzed 19324 sleep epochs of 30 seconds each. This approach has the advantage of using the only movement and breathing signals, which can be recorded with less effort than heart or brainwave signals, and requiring only four derived parameters for the calculations. Therefore, the new system is a significant improvement for non-obtrusive sleep stage identification compared to existing approaches.

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.

Sleep stage dynamics in young patients with sleep bruxism

Study Objectives

We hypothesized that sleep stage dynamics are different in patients with sleep bruxism (SB) and that these changes are associated with the occurrence of rhythmic masticatory muscle activity (RMMA).

Methods

Fifteen healthy controls and 15 patients with SB underwent overnight polysomnography. Sleep variables and survival curves of continuous runs of each sleep stage were compared between the groups. Stage transition dynamics and the probability of stage fragmentation were analyzed for three epochs before and after the epoch with RMMA. Survival curves of continuous runs of each sleep stage, terminated with or without RMMA, were also compared.

Results

There were no significant differences in sleep variables between the groups, except for shorter sleep latency, shorter rapid eye movement (REM) latency, and longer total N1 duration in SB patients than in controls. REM sleep and N2 were significantly less continuous in SB patients than in controls. In the SB group, stage fragmentation probability was significantly increased for the epoch with RMMA compared with the baseline for all stages. Meanwhile, the occurrence of RMMA did not affect the continuity of N2 or REM; however, the occurrence of RMMA was preceded by more continuous N3 runs.

Conclusions

Sleep stage dynamics differed between SB patients and controls. RMMA does not result in sleep disruption but is likely associated with dissipation of sleep pressure. Less continuity of REM sleep in SB may provide insights into the underlying pathophysiological mechanisms of SB, which may be related to REM sleep processes such as cortical desynchronized states or brainstem activation.

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.

Accuracy of Fitbit Wristbands in Measuring Sleep Stage Transitions and the Effect of User-Specific Factors

BACKGROUND

It has become possible for the new generation of consumer wristbands to classify sleep stages based on multisensory data. Several studies have validated the accuracy of one of the latest models, that is, Fitbit Charge 2, in measuring polysomnographic parameters, including total sleep time, wake time, sleep efficiency (SE), and the ratio of each sleep stage. Nevertheless, its accuracy in measuring sleep stage transitions remains unknown.

OBJECTIVE

This study aimed to examine the accuracy of Fitbit Charge 2 in measuring transition probabilities among wake, light sleep, deep sleep, and rapid eye movement (REM) sleep under free-living conditions. The secondary goal was to investigate the effect of user-specific factors, including demographic information and sleep pattern on measurement accuracy.

METHODS

A Fitbit Charge 2 and a medical device were used concurrently to measure a whole night's sleep in participants' homes. Sleep stage transition probabilities were derived from sleep hypnograms. Measurement errors were obtained by comparing the data obtained by Fitbit with those obtained by the medical device. Paired 2-tailed t test and Bland-Altman plots were used to examine the agreement of Fitbit to the medical device. Wilcoxon signed-rank test was performed to investigate the effect of user-specific factors.

RESULTS

Sleep data were collected from 23 participants. Sleep stage transition probabilities measured by Fitbit Charge 2 significantly deviated from those measured by the medical device, except for the transition probability from deep sleep to wake, from light sleep to REM sleep, and the probability of staying in REM sleep. Bland-Altman plots demonstrated that systematic bias ranged from 0% to 60%. Fitbit had the tendency of overestimating the probability of staying in a sleep stage while underestimating the probability of transiting to another stage. SE>90% (P=.047) was associated with significant increase in measurement error. Pittsburgh sleep quality index (PSQI)<5 and wake after sleep onset (WASO)<30 min could be associated to significantly decreased or increased errors, depending on the outcome sleep metrics.

CONCLUSIONS

Our analysis shows that Fitbit Charge 2 underestimated sleep stage transition dynamics compared with the medical device. Device accuracy may be significantly affected by perceived sleep quality (PSQI), WASO, and SE.

Sleep staging from single-channel EEG with multi-scale feature and contextual information

PURPOSE

Portable sleep monitoring devices with less-attached sensors and high-accuracy sleep staging methods can expedite sleep disorder diagnosis. The aim of this study was to propose a single-channel EEG sleep staging model, SleepStageNet, which extracts sleep EEG features by multi-scale convolutional neural networks (CNN) and then infers the type of sleep stages by capturing the contextual information between adjacent epochs using recurrent neural networks (RNN) and conditional random field (CRF).

METHODS

To verify the feasibility of our model, two datasets, one composed by two different single-channel EEGs (Fpz-Cz and Pz-Oz) on 20 healthy people and one composed by a single-channel EEG (F4-M1) on 104 obstructive sleep apnea (OSA) patients with different severities, were examined. The corresponding sleep stages were scored as four states (wake, REM, light sleep, and deep sleep). The accuracy measures were obtained from epoch-by-epoch comparison between the model and PSG scorer, and the agreement between them was quantified with Cohen's kappa (ҡ).

RESULTS

Our model achieved superior performance with average accuracy (Fpz-Cz, 0.88; Pz-Oz, 0.85) and ҡ (Fpz-Cz, 0.82; Pz-Oz, 0.77) on the healthy people. Furthermore, we validated this model on the OSA patients with average accuracy (F4-M1, 0.80) and ҡ (F4-M1, 0.67). Our model significantly improved the accuracy and ҡ compared to previous methods.

CONCLUSIONS

The proposed SleepStageNet has proved feasible for assessment of sleep architecture among OSA patients using single-channel EEG. We suggest that this technological advancement could augment the current use of home sleep apnea testing.

Markov modeling of sleep stage transitions and ultradian REM sleep rhythm

OBJECTIVE

One of the highly characteristic features of sleep is the cyclic occurrence of non-rapid eye movement (NREM) and REM sleep, which is referred to as the ultradian rhythm of sleep. Even though REM sleep was discovered over half a century ago, surprisingly, the mechanism of the ultradian REM sleep rhythm has not yet been fully elucidated. In the present study, we aim to provide a mechanistic insight into the generation of the ultradian REM sleep rhythm. Approach and Main results: By simulating hypnograms with the dynamic features of sleep stage transitions, i.e. stage transition probabilities and stage-specific survival time functions, we show that the second-order Markov transition probabilities and the stage-specific survival time functions can reproduce the central position (∼90 min) of the REM-onset intervals (ROIs), but with a larger variance in distribution. In addition, we demonstrate the direct effect of the increased probability of the transitions from light to deep sleep within NREM sleep on the prolongation of the ROIs in a dose-response manner.

SIGNIFICANCE

These results suggest that dynamic sleep stage transitions constitute the basis of the formation of the ultradian rhythm of sleep; however, further elaboration of the model would be required to reduce the variability in rhythmicity.

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

The pattern of sleep stages across a night (sleep architecture) is influenced by biological, behavioral, and clinical variables. However, traditional measures of sleep architecture such as stage proportions, fail to capture sleep dynamics. Here we quantify the impact of individual differences on the dynamics of sleep architecture and determine which factors or set of factors best predict the next sleep stage from current stage information. We investigated the influence of age, sex, body mass index, time of day, and sleep time on static (e.g. minutes in stage, sleep efficiency) and dynamic measures of sleep architecture (e.g. transition probabilities and stage duration distributions) using a large dataset of 3202 nights from a non-clinical population. Multi-level regressions show that sex effects duration of all Non-Rapid Eye Movement (NREM) stages, and age has a curvilinear relationship for Wake After Sleep Onset (WASO) and slow wave sleep (SWS) minutes. Bayesian network modeling reveals sleep architecture depends on time of day, total sleep time, age and sex, but not BMI. Older adults, and particularly males, have shorter bouts (more fragmentation) of Stage 2, SWS, and they transition less frequently to these stages. Additionally, we showed that the next sleep stage and its duration can be optimally predicted by the prior 2 stages and age. Our results demonstrate the potential benefit of big data and Bayesian network approaches in quantifying static and dynamic architecture of normal sleep.