Dynamics of snoring sounds and its connection with obstructive sleep apnea

Snoring is extremely common in the general population and when irregular may indicate the presence of obstructive sleep apnea. We analyze the overnight sequence of wave packets — the snore sound — recorded during full polysomnography in patients referred to the Sleep Laboratory due to suspected obstructive sleep apnea. We hypothesize that irregular snore, with duration in the range between 10 and 100 s, correlates with respiratory obstructive events. We find that the number of irregular snores — easily accessible, and quantified by what we call the snore time interval index (STII) — is in good agreement with the well-known apnea–hypopnea index, which expresses the severity of obstructive sleep apnea and is extracted only from polysomnography. In addition, the Hurst analysis of the snore sound itself, which calculates the fluctuations in the signal as a function of time interval, is used to build a classifier that is able to distinguish between patients with no or mild apnea and patients with moderate or severe apnea.     

Sleep disturbances in highly stress reactive mice: Modeling endophenotypes of major depression

Background  

Neuronal mechanisms underlying affective disorders such as major depression (MD) are still poorly understood. By selectively breeding mice for high (HR), intermediate (IR), or low (LR) reactivity of the hypothalamic-pituitary-adrenocortical (HPA) axis, we recently established a new genetic animal model of extremes in stress reactivity (SR). Studies characterizing this SR mouse model on the behavioral, endocrine, and neurobiological levels revealed several similarities with key endophenotypes observed in MD patients. HR mice were shown to have changes in rhythmicity and sleep measures such as rapid eye movement sleep (REMS) and non-REM sleep (NREMS) as well as in slow wave activity, indicative of reduced sleep efficacy and increased REMS. In the present study we were interested in how far a detailed spectral analysis of several electroencephalogram (EEG) parameters, including relevant frequency bands, could reveal further alterations of sleep architecture in this animal model. Eight adult males of each of the three breeding lines were equipped with epidural EEG and intramuscular electromyogram (EMG) electrodes. After recovery, EEG and EMG recordings were performed for two days.     

Long term sleep disturbance due to traffic noise

This contribution to the evaluation of the effects of traffic noise on sleep disturbance is focused on the responses of people living near a main road. Experiments were carried out in the homes of subjects who had habitually been exposed to noise for periods of more than four years. The chronic changes in overall sleep patterns and the temporary sleep responses to particular noise events caused by traffic are demonstrated. Young people show mainly stage 3 and 4 deficits whilst older people show REM sleep deficits. The cardiac response to noise during sleep was also examined. These results highlight that both long term average and peak levels are important in assessing sleep disturbance. The threshold levels, measured inside the bedroom and above which sleep quality starts to become impaired, are 37 L_eq(A) and 45 dB (A)L_{p max}, respectively. For the type of traffic studied these two levels are coherent and it is therefore possible that a single noise index, L_eq(A), is sufficient to scale sleep disturbance.     

Large-amplitude, spatially correlated fluctuations in BOLD fMRI signals during extended rest and early sleep stages

A number of recent studies of human brain activity using blood-oxygen-level-dependent (BOLD) fMRI and EEG have reported the presence of spatiotemporal patterns of correlated activity in the absence of external stimuli. Although these patterns have been hypothesized to contain important information about brain architecture, little is known about their origin or about their relationship to active cognitive processes such as conscious awareness and monitoring of the environment. In this study, we have investigated the amplitude and spatiotemporal characteristics of resting-state activity patterns and their dependence on the subjects' alertness. For this purpose, BOLD fMRI was performed at 3.0 T on 12 normal subjects using a visual stimulation protocol, followed by a 27 min rest period, during which subjects were allowed to fall asleep. In subjects who were asleep at the end of the scan, we found (a) a higher amplitude of BOLD signal fluctuation during rest compared with subjects who were awake at the end of the scan; (b) spatially independent patterns of correlated activity that involve all of gray matter, including deep brain nuclei; (c) many patterns that were consistent across subjects; (d) that average percentage levels of fluctuation in visual cortex (VC) and whole brain were higher in subjects who were asleep (up to 1.71% and 1.16%, respectively) than in those who were awake (up to 1.15% and 0.96%) at the end of the scan and were comparable with those levels evoked by intense visual stimulation (up to 1.85% and 0.76% for two subject groups); (e) no confirmation of correlation, positive or negative, between thalamus and VC found in earlier studies. These findings suggest that resting-state activity continues during sleep and does not require active cognitive processes or conscious awareness.     

The effect of traffic noise on sleep of young adults in their homes

The disturbance of sleep by traffic noise is a major problem area in noise pollution. Extensive laboratory tests using multichannel electroencephalograms (EEG) have been carried out by many workers to determine the general response of people when exposed to noise during sleep. An experimental technique for obtaining results in people's homes has been developed using a simplified one-channel EEG. The responses of six people in the age range 19-24 were measured. A significant correlation was found between sleep disturbance and traffic noise when the noise is expressed in EPNdB. In the home experiments the subjects appeared to be approximately 10 dB less sensitive to noises than laboratory subjects for similar noise exposure. There also appeared to be some adaptation to the noise exposure; however, only one subject was tested for a period of 20 days and was insufficient to give definite results on adaptation.     

Dynamic changes in GABA<Subscript>A</Subscript>receptors on basal forebrain cholinergic neurons following sleep deprivation and recovery

Background  

The basal forebrain (BF) cholinergic neurons play an important role in cortical activation and arousal and are active in association with cortical activation of waking and inactive in association with cortical slow wave activity of sleep. In view of findings that GABA_A receptors (Rs) and inhibitory transmission undergo dynamic changes as a function of prior activity, we investigated whether the GABA_ARs on cholinergic cells might undergo such changes as a function of their prior activity during waking vs. sleep.     

脑电信号的标度分析及其在睡眠状态区分中的应用

脑电信号具有长程幂律相关性及多重分形的标度特性,并随生理病理状态改变.本文首次针对睡眠脑电信号应用单重分形去趋势波动分析(detrended fluctuation analysis,简记为DFA)方法与多重分形奇异谱对睡眠脑电信号的标度特征进行系统的对比研究.发现DFA标度指数α对于不同导联和样本组间的差异较为敏感,随睡眠状态的变化不规律;而多重分形奇异强度区间Δα随睡眠状态的变化更为规律,睡眠Ⅰ期至Ⅳ期不断增大,并且导联间差异和样本组间差异均较小.多重分形Δα参数更适合作为判定睡眠状态的定量参数.     

THE EFFECT OF DIFFERENT KINDS OF NOISE ON THE QUALITY OF SLEEP UNDER THE CONTROLLED CONDITIONS

Many laboratory and field studies of the effect of noise on sleep have been performed where subjects sleep whole nights. It was suggested from our former studies that the most serious effect of noise on sleep is disturbance in falling asleep and that people have to make efforts to try to sleep in noisy situations. In this study, the effort to fall asleep was used as an index of sleep and the effect of various physical properties of sounds was examined. Subjects were asked to try to sleep listening to sounds presented with a mini-disk and they were allowed to switch off the sound after 1 h if they could not sleep. The results suggest that (1) whether subjects can sleep within 1 h after they start to try to sleep is a good index of the effect of noise on sleep and that L_Aeqis a good index of the effect of noise on sleep except for the sounds which have meanings such as songs and people's talk.     

Sleep disturbance caused by meaningful sounds and the effect of background noise

To study noise-induced sleep disturbance, a new procedure called “noise interrupted method”has been developed. The experiment is conducted in the bedroom of the house of each subject. The sounds are reproduced with a mini-disk player which has an automatic reverse function. If the sound is disturbing and subjects cannot sleep, they are allowed to switch off the sound 1 h after they start to try to sleep. This switch off (noise interrupted behavior) is an important index of sleep disturbance. Next morning they fill in a questionnaire in which quality of sleep, disturbance of sounds, the time when they switched off the sound, etc. are asked. The results showed a good relationship between L_Aeq and the percentages of the subjects who could not sleep in an hour and between L_Aeq and the disturbance reported in the questionnaire. This suggests that this method is a useful tool to measure the sleep disturbance caused by noise under well-controlled conditions.     

An accurate algorithm to calculate the Hurst exponent of self-similar processes

In this paper, we introduce a new approach which generalizes the GM2 algorithm (introduced in Sánchez-Granero et al. (2008) [52]) as well as fractal dimension algorithms (FD1, FD2 and FD3) (first appeared in Sánchez-Granero et al. (2012) [51]), providing an accurate algorithm to calculate the Hurst exponent of self-similar processes. We prove that this algorithm performs properly in the case of short time series when fractional Brownian motions and Lévy stable motions are considered.We conclude the paper with a dynamic study of the Hurst exponent evolution in the S&P500 index stocks.     

基于网络连接度指标的脑梗死患者脑电信号相同步分析

基于图论的脑功能网络分析是近年来的一个研究热点,而相同步分析已被证实为揭示多导联脑电信号之间功能连接的有效工具.针对当脑电采集系统中导联数目较少而不适用于采用图论分析的情况,提出使用基于导联间相同步分析的网络连接度指标研究脑功能网络的关联特性和整体特性.采用新的频带划分方法,将0.5—30 Hz带宽内的脑电信号划分到5个子带上,计算了不同数据长度下各子带分量的网络连接度指标,并对比分析了各子带分量的相对功率.结果表明:在对脑梗死患者的脑电图和正常人的脑电图进行分析时,需要合理的数据长度量化不同动力学系统之间的差异;在合理的数据长度下,在网络连接度指标的区分效果方面,19—24 Hz分量信号优于其他分量,而且仅在19—24 Hz频带上,脑梗死患者组的所有导联出现了与对照组的所有导联相同趋势的变化.研究表明19—24 Hz频带是脑梗死最佳的脑电图诊断频段,可将该频段下的网络连接度指标作为脑梗死辅助诊断的新指标.     

Practical guidance for risk assessment of traffic noise effects on sleep

Environmental noise disturbs sleep and may impair well-being, performance and health. The European Union Directive 2002/49/EC (END) requires member states to generate noise maps and action plans to mitigate traffic noise effects on the population. However, practical guidance for the generation of action plans, i.e. for assessing the effects of traffic noise on sleep, is missing. Based on the current literature, we provide guidance on hazard identification, exposure assessment, exposure-response relationships and risk estimation: there is currently no consensus on both exposure and outcome variables that describe traffic noise effects on sleep most adequately. END suggests the equivalent noise level L_night as the primary exposure variable, and our own simulations of single nights with up to 200 noise events based on a field study on the effects of aircraft noise on sleep support using expert consensus L_night ranges (<30, 30-40, 40-55, >55 dB) for risk assessment. However, the precision of risk assessment may be considerably improved by adding information on the number of noise events contributing to L_night. The calculation of L_night should be extended to the shoulder hours of the day if traffic is busy during these periods. More data are needed on the combined effects of different traffic modes.     

The effect of the number of aircraft noise events on sleep quality

BackgroundBoth the WHO and the EC recommend the use of L_night as the primary indicator for sleep disturbance. Still, a key question for noise policy is whether the prediction of sleep quality could be improved by taking the number of events into account in addition to L_night.ObjectivesThe current paper investigates the association between sleep quality and the number of aircraft noise events. The first aim of this study was to investigate whether, for the purpose of predicting sleep quality measured by motility, the nummer of events is adequately represented in L_night for the purpose of predicting sleep quality measured by motility. The second aim was to investigate whether the number of events at a given L_night has an additional predictive value. In addition, it was explored whether the total number of events should be taken into account for the production of sleep quality, or only the number of events exceeding a certain sound pressure level.MethodsThis study is based on data of a field study among 418 people living within a range of 20 km from Amsterdam Airport Schiphol. The data from this study are well suited for this purpose, since for every subject both the number and the exposure level of events are available. Sleep quality was measured by motility, derived from actimeters worn on the wrist, and by self-reported sleep quality scored on a 11-point scale. Mixed linear regression models were built in a stepwise manner to predict sleep quality during a sleep period time.ResultsThe results show that, given a certain equivalent noise level, additional information on the overall number of events does not improve the prediction of sleep quality. However, the number of events above L_Amax of 60 dB was related to an increase in mean motility, indicating lower sleep quality. No effect of number of events was found on self-reported sleep quality.ConclusionsThis study suggests that the number of events is more or less adequately represented in L_night and only the number of high noise level events may have additional effects on sleep quality as measured by motility. This may be viewed as an indication that, in addition to L_night, the number of events with a relatively high L_Amax could be used as a basis for protection against noise-induced sleep disturbance.     

DIFFERENT EFFECTS OF ROAD TRAFFIC NOISE AND FROGS' CROAKING ON NIGHT SLEEP

This study was designed to assess the effects of road traffic noise and frogs' croaking on the objective and subjective quality of sleep in a laboratory. The subjects were seven male students aged 19-21 years. They were exposed to recorded road traffic noise and frogs' croaking, with 49·6 and 49·5 dB(A)L_Aeq , and 71·2 and 56·1 dB(A) L_Amax, respectively. The background noise in the experimental room was 31·0 dB(A) L_Aeq. The sleep EEG was recorded according to standard methods. The sleep polygraphic parameters examined were the percentage of sleep stage relative to the total sleep time (%S1, %S2, %S(3+4), %SREM, %MT), total sleep time, sleep onset latency, and awakening during sleep in minutes and sleep efficiency. A structured sleep rating questionnaire (OSA), was administered to the subjects after they awakened. The %S2 increased and the %SREM decreased during exposure to road traffic noise. However, no significant effect of exposure to frogs' croaking was observed on any of the polygraphic sleep parameters. The subjective quality of sleep was degraded more by exposure to road traffic noise than that to frogs' croaking.     

Nocturnal road traffic noise assessment and sleep research: The usefulness of different timeframes and in- and outdoor noise measurements

In this paper, we investigated the procedure of noise assessment in the study of nocturnal noise exposure during sleep in the home situation. The use of two different timeframes (fixed from 11 PM to 07 AM versus personal “Time in Bed” period) was explored as well as the relation between indoor and outdoor noise levels and between the actual and estimated noise levels. Noise recordings were performed inside and outside the bedroom of 24 subjects living in high density road traffic noise areas in the Brussels’ Capital Region during seven consecutive days. Indoor and outdoor noise indicators L_Aeq, L_Amax and individual noise events were analysed. Subjects completed a daily sleep log in which the Time in Bed period was assessed. The results indicate that, for outdoor noise assessment, the use of an average L_Aeq might not be sufficient to reflect well the noise levels during the sleep period. For indoor measurements, significant differences were found in the comparison between both timeframes (L_Aeq: T = 16; p < .001). Considering the relation between indoor and outdoor measurements, low correlations (r = .49; p < .001) were found even when the location of the bedroom as a mediating factor was accounted for (street side; r = .52; p < .001). Therefore, from our study, caution is needed when relying on outdoor noise measurements for the evaluation of sleep disturbances. Furthermore, one needs to be aware of the weak correspondence between indoor and outdoor noise levels in the discussion of what a harmonized noise indicator for the evaluation of noise exposure and sleep disturbances should consist of.     

Time-series analysis of sleep-wake stage of rat EEG using time-dependent pattern entropy

We performed electroencephalography (EEG) for six male Wistar rats to clarify temporal behaviors at different levels of consciousness. Levels were identified both by conventional sleep analysis methods and by our novel entropy method. In our method, time-dependent pattern entropy is introduced, by which EEG is reduced to binary symbolic dynamics and the pattern of symbols in a sliding temporal window is considered. A high correlation was obtained between level of consciousness as measured by the conventional method and mean entropy in our entropy method. Mean entropy was maximal while awake (stage W) and decreased as sleep deepened. These results suggest that time-dependent pattern entropy may offer a promising method for future sleep research.     

Effect of traffic noise on the cyclical nature of sleep

Sleep changes from shallow to deep and back again in a cyclical manner with a period of around 90 min. The sleep of 12 subjects, each sleeping for 24 nights, was monitored by EEG. The results indicate that the cyclical nature may be somewhat disturbed by continuous free-flowing traffic noise, at a level of 60 dB, if, for instance, waking is always considered as the end of one cycle. However, if a cycle is judged from a bird's-eye view of the sleep record, then it appears that the average subject persists in his normal cycle and the effect of noise is negligible. But individual differences are great and may even be in opposite directions [G. J. Thiessen and A.C. Lapointe, J. Acoust. Soc. Am. 64, 1078-1080 (1978)], which may result in obscuring real effects when data are averaged over a number of individuals. Defining a "sleep cycle" is of importance in view of reports [M. Herbert and R.T. Wilkinson, Proc. of Congress on Biological Effects on Noise, Dubrovnik, Yugoslavia (1973)] that the disturbance of the sleep rhythm has an effect on performance during the following day.     

Statistical analysis of fractal-based brain tumor detection algorithms

Fractals are geometric objects that have a noninteger fractal dimension (FD). The FD has been exploited for various biomedical recognition applications such as breast tumor and lung tumor detection. Our previous work shows that the FD is useful in the detection of brain tumors when a reference nontumor image is available. In this work, we extend our previous work by statistically validating the results of FD analysis on a set of 80 real MR and CT images. Our half-image technique requires that the tumor is located in one half of the brain whereas our whole-image technique does not. Furthermore, we alleviate the need for a reference (control) nontumor image to compute the tumor FD, which was necessary in our previous work. We also compare the brain tumor detection performance of our algorithms with other fractal-based algorithms in the literature and statistically validate our results against manually segmented tumor images. We find that the tumor region offers a statistically significant lower FD compared with that of the nontumor area for most of the FD algorithms studied in this work. Thus, our statistical analysis suggests that these FD algorithms may be exploited successfully to determine the possible presence and location of brain tumors in MR and CT images.     

Everyday episodic memory in amnestic mild cognitive impairment: a preliminary investigation

Background

In humans, rapid eye movements (REM) density during REM sleep plays a prominent role in psychiatric diseases. Especially in depression, an increased REM density is a vulnerability marker for depression. In clinical practice and research measurement of REM density is highly standardized. In basic animal research, almost no tools are available to obtain and systematically evaluate eye movement data, although, this would create increased comparability between human and animal sleep studies.

Methods

We obtained standardized electroencephalographic (EEG), electromyographic (EMG) and electrooculographic (EOG) signals from freely behaving mice. EOG electrodes were bilaterally and chronically implanted with placement of the electrodes directly between the musculus rectus superior and musculus rectus lateralis. After recovery, EEG, EMG and EOG signals were obtained for four days. Subsequent to the implantation process, we developed and validated an Eye Movement scoring in Mice Algorithm (EMMA) to detect REM as singularities of the EOG signal, based on wavelet methodology.

Results

The distribution of wakefulness, non-REM (NREM) sleep and rapid eye movement (REM) sleep was typical of nocturnal rodents with small amounts of wakefulness and large amounts of NREM sleep during the light period and reversed proportions during the dark period. REM sleep was distributed correspondingly. REM density was significantly higher during REM sleep than NREM sleep. REM bursts were detected more often at the end of the dark period than the beginning of the light period. During REM sleep REM density showed an ultradian course, and during NREM sleep REM density peaked at the beginning of the dark period. Concerning individual eye movements, REM duration was longer and amplitude was lower during REM sleep than NREM sleep. The majority of single REM and REM bursts were associated with micro-arousals during NREM sleep, but not during REM sleep.

Conclusions

Sleep-stage specific distributions of REM in mice correspond to human REM density during sleep. REM density, now also assessable in animal models through our approach, is increased in humans after acute stress, during PTSD and in depression. This relationship can now be exploited to match animal models more closely to clinical situations, especially in animal models of depression.