Sleep and sleepiness following a behaviourally ‘active’ day

During 4 day periods in which a total of nine subjects expected to spend each daytime under relatively stereotyped environmental conditions, a substitution was made for 1 day (experimental day) on a random basis. Here, subjects spent the time in a variety of novel, interesting and perceptually stimulating environments. Sleep EEG recordings were made each night following an adaptation night. Self-assessment sleepiness ratings were made night and morning. Following the experimental day subjects felt significantly more sleepy at bedtime, and sleep onset latency was significantly reduced. Although sleep length was unchanged, deep (slow wave) sleep, particularly stage 4 sleep, was increased significantly. There was some increase in sleep disturbance. REM sleep was unaffected. Subjects awoke feeling refreshed the next day. There was a small but significant carryover of a further SWS increase in the first part of the following night.     

Sleep and circadian rhythms in mining operators: limited evidence of adaptation to night shifts

Cumulative sleep deprivation is often associated with work patterns involving night shift or early morning shifts. Adaptation of the circadian system to the shift pattern is reported to promote improved duration and quality of sleep and a concurrent improvement in performance. The current study followed twenty-nine operators at a live-in mining operation working to a seven-day, seven-night shift pattern who collected saliva samples for melatonin measurement, recorded sleep using activity monitors and diaries, and underwent performance testing (psychomotor vigilance task) for one complete roster cycle. The time of onset of melatonin secretion changed significantly (P=0.022) across the week of both Day and Night shifts (2104 h ± 16 min versus 2130 h ± 16 min, respectively), but the small magnitude of the change indicates a lack of true circadian rhythm adaptation to the lifestyle. Total sleep time was longer following the seventh Day shift (associated with a period of 24 h off prior to the commencement of Night shifts). There were no other changes in total sleep time. Further, there were no improvements in sleep onset latency or sleep efficiency on Day or Night shifts. However, reaction times recorded at the end of the shifts slowed across the seven Day and seven Night shifts indicative of impairments in psychomotor performance (F(6,168)=6.087, P<0.001). The results suggest that previous reports of adaptation to consecutive night shifts cannot necessarily be applied to onshore or Australian environments. Adaptation is dependent on factors such as light exposure, environmental conditions, shift parameters such as wake-up, work start and work end times and individual characteristics.     

Tolerance to Shift of Sleep,as Related to the Individual's Circadian Phase Position

The differential adaptation of ‘morning’ and ‘evening’ types to changes in sleep schedules was investigated in two studies. The first was carried out on six female subjects, classified as morning or evening types on the basis of their rhythms of body temperature, and on their answers to a morningness-eveningness questionnaire. Bed times on single nights were varied between 2100h and 0300 h. On each night measurements were taken of latency of sleep onset and of sleep duration. Body temperature, heart rate and respiratory rate were also recorded, as was self-estimated vigilance during the day following the experimental sleep. Evidence of sleep deficiency after late bed limes was found in the morning but not in the evening types; the latter, however, had longer sleep latencies after early bed times than the morning types.

In the second study eight male subjects underwent the same basic experimental treatment, but in this case sleep was monitored for three consecutive nights with a given bed time, in order to examine adaptive processes. The overall results were similar to those of the first study. However, it was also found that, whereas the duration of sleep tended to increase over the three consecutive nights with later bed times in all subjects, this was more marked in the evening than in the morning types.     

睡眠环境智能控制系统

拥有一个舒适的室内睡眠环境是许多人所梦寐以求的,结合睡眠的医学知识和自动控制理论,提出一种以单片机为控制核心,整合多种家居电器的睡眠环境智能调节系统的设计方案,并介绍了系统的总体规划、控制策略和软件实现,经实际测试表明该系统实用价值较高,不仅能有效地改善睡眠环境也可有助睡眠医学的相关研究.     

基于CNN-BiLSTM的自动睡眠分期算法

睡眠分期是睡眠数据分析的基础, 针对目前睡眠分期存在的依赖人工提取、人工判别效率低、自动睡眠分期准确率不高等问题, 本文研究模型是基于卷积神经网络和双向长短时记忆神经网络2个深度学习神经网络相结合的, 利用脑电信号来进行自动睡眠分期的模型方法. 算法能提取得到原始脑电信号的梅尔频谱, 利用卷积神经网络和双向长短时记忆神经网络进行时频域的特征提取, 卷积神经网络能够提取睡眠信号高级特征, 双向长短时记忆神经网络结合睡眠数据不同时期的关联性, 提高自动睡眠分期的准确率. 实验结果表明, 本文方法在Sleep-EDF数据集的3种状态睡眠分期任务中取得89.0%的平均准确率. 与传统的基于统计规则的分期模型相比, 本文模型的准确率更高, 且简单高效, 泛化性能更好. 本文算法适用于非线性、不稳定、有幅度起伏变动的脑电信号, 有效提高了自动睡眠分期模型结果的准确率, 对现代睡眠医学、睡眠障碍等分析研究具有一定的实用价值.     

Sleep package model mathematical model for a sleep pattern

In order to design the better sleep-wake schedule for shift workers, the mathematical model for sleep called SLEEP PACKAGE MODEL was developed. When we get three parameters; time to fall asleep (t), the length of sleep period (Ls), and the waking time (Tw) before a sleep are given, the model can predict the sleep pattern, namely NonREM-REM cycle pattern. In order to examine the ability of the model, the sleep patterns for 60 nights of 20 male young adults were recorded under the usual night condition (t=23:00, Ls=8 hrs, and Tw=16 hrs), and the recovery sleep of one university male student was recorded after the 72 hrs sleep deprivation condition (t=09:00,Ls=10 hrs, and Tw=72 hrs). The usual night sleep and the recovery sleep after sleep deprivation were predicted by the model. The estimated sleep patterns by the model were compared with the actual sleep pattern in the experiment, as the results, the estimated sleep patterns by the model is very similar to the acutal sleep patterns. So thatm the result suggests that the application of this model to human engineering field could be expected.     

层次传感器网络的交错同步休眠调度方法研究

结合层次传感器网络中基础层的多跳分簇结构、周期性短数据为主、有明显方向性的数据流特点,提出了一种交错的周期倍增同步休眠调度方案。该方案通过让不同层节点运行不同频率的激活/休眠周期可以达到降低时延和降低能耗的目的。将该机制应用到基础层MAC协议中,仿真证明可以保证簇中上下行两个方向的数据都有较低的时延,并且在节能和降低时延的同时拥有较大的吞吐量,能够较好的满足基础层的要求。     

基于心冲击信号的睡姿识别

研究证明,睡眠质量与睡姿有着密切关系,不良的睡姿甚至会加剧多种疾病的潜在风险。为了更精准地进行睡眠健康监控,提出了一种基于心冲击（BCG）信号的睡姿模式识别算法,使用非接触、无干扰的压电薄膜传感器采集BCG信号,在腰腹部采集仰卧、俯卧、左侧卧和右侧卧4种睡姿信号,经小波变换降噪等预处理后提取基于J波的特征值,设计并比较基于神经网络和KNN的睡姿识别分类器。实验结果表明,神经网络睡眠识别算法的平均正确识别率为93%,KNN算法为84%,因此基于BCG信号的神经网络睡姿识别算法可以广泛用于睡眠监测应用。     

基于双通道脑电信号的在线实时睡眠分期系统

当代社会睡眠问题日益突出,及时检测评估睡眠质量有助于诊断睡眠疾病.针对目前市面上睡眠监测类产品发展参差不齐的现状,本文搭建了一个基于双通道脑电信号的在线实时睡眠分期系统,利用第三方接口脑环获取脑电数据,结合CNN-BiLSTM神经网络模型,在PC电脑端实现了在线的实时睡眠分期与音乐调控功能.系统使用基于卷积神经网络CNN和双向长短时记忆神经网络BiLSTM相结合的算法模型对脑电信号进行自动特征提取,CNN能够提取高阶特征, BiLSTM可以捕捉睡眠数据前后的依赖性和关联性,睡眠分期准确率更高.实验结果表明,本文算法模型在Sleep-EDF公共数据集上的四分类任务中取得了92.33%的分期准确率,其Kappa系数为0.84,本系统的实时睡眠分期功能在自采集睡眠数据分期实验中取得79.17%的分期准确率,其Kappa系数为0.70.相比其他睡眠监测类产品,本系统睡眠分期准确率更高,应用场景更多样,实时性和可靠性强,并且可以根据分期结果对用户进行相应的音乐调控,改善用户睡眠质量.     

Memory Consolidation and Forgetting During Sleep: A Neural Network Model

Experimental research with humans and animals suggests that sleep — particularly REM sleep — is, in some way, associated with learning. However, the nature of the association and the underlying mechanism remain unclear. A number of theoretical models have drawn inspiration from research into Artificial Neural Networks. Crick and Mitchinson's ‘unlearning’ and Robins and McCallum's ‘pseudo-rehearsal’ models suggest alternative mechanisms through which sleep could contribute to learning. In this paper we present simulations, suggesting a possible synthesis. Our simulations use a modified version of a Hopfield network to model the possible contribution of sleep to memory consolidation. Sleep is simulated by removing all sensory input to the network and by exposing it to a ‘noise’, intended as a highly abstract model of the signals generated by the Ponto-geniculate-occipital system during sleep. The results show that simulated sleep does indeed contribute to learning and that the relationship between the observed effect and the length of simulated sleep can be represented by a U-shaped curve. It is shown that while high-amplitude, low-frequency noise (reminiscent of NREM sleep) leads to a general reinforcement of memory, low-amplitude, high-frequency noise (as observed in REM sleep) leads to ‘forgetting’ of all but the strongest memory traces. This suggests that a combination of the two kinds of sleep might produce a stronger effect than either kind of sleep on its own and that effective consolidation of memory during sleep may depend not just on REM or NREM sleep but on the overall dynamics of the sleep cycle.     

Sleep deprivation,chronic exercise and muscular performance

Muscular performance was tested during 64 hours of sleep deprivation with and without intermittent excercise (treadmill walking at 28% of maximum oxygen intake). The subjects (12 males aged 22·7 ± 2·2 years) carried out a cross-over trial with an 8 week interval separating the two periods of sleep deprivation. The sleep deprivation did not change the time for a 40 m dash, isometric handgrip force or balance (stabilometer test). Vertical jump height decreased, the change being significant for simple sleep deprivation, but not for the combination of deprivation and intermittent exercise. Sleep deprivation decreased isokinetic extension force at 60° s?1, while intermittent walking decreased isokinetic extension force at both 60 and 180° ?1; however, there was no significant difference between exercise plus sleep deprivation and sleep deprivation alone at either angular velocity. We conclude that the moderate intensity of physical activity likely in industrial work has little influence upon human performance under conditions of sleep deprivation.     

改进的IEEE802．16e睡眠模式算法

IEEE802．16e标准采用睡眠模式降低移动台的能量消耗。提出一种改进的睡眠模式控制算法,该算法根据接入速率的变化动态调整睡眠模式的睡眠周期。在给出的4种实际模型上比较IEEE802．16e标准算法与改进算法,结果表明改进算法在低接入速率下能节约更多能量,在高接入速率下与标准算法的性能相当。     

基于LabVIEW的睡眠呼吸暂停检测与远程监护

睡眠呼吸暂停综合症(SAS)是一种发病率极高的睡眠呼吸疾病,严重影响人的睡眠质量;针对目前SAS的检测尚未普及,研究了一种家用简易型的检测方法;通过实时采集被测者的呼吸信号,利用LabVIEW对信号进行分析处理判断发生睡眠呼吸暂停的情况,呼吸暂停每小时出现5次以上或7小时的睡眠中出现30次以上,即诊断为患有SAS,经实验证明该方法可实现对SAS的初步筛查;通过互联网,可使医护人员对被测者进行远程监护,该方法对SAS的预防和早期诊断具有重要价值.     

Sleep at Home and in the Sleep Laboratory: Disturbance by Recording Procedures

Abstract

Nineteen healthy men, aged 20 to 55 yr, who did not suffer from insomnia had their sleep recorded by EEG and EOG in the laboratory for 3 to 12 nights each over periods of 1 to 4 weeks. They also gave subjective reports each day which proved to be valid although not always very accurate assessments of their sleep latency, the number of mid-night awakenings and the times of sleeping in the laboratory. There was some ' adaptation ' to the laboratory over the first 2 nights, but awakenings during the night continued for up to 12 nights to be reported more than twice as frequently in the laboratory than at home. They were apparently often caused by intermittent traction of electrodes on the face and scalp as the subjects turned over in bed From the point of view of mid-night awakenings, EEG/EOG recordings are unlikely to give an accurate assessment of the subject's usual sleep, even after several ‘ adaptation ’ nights.     

休眠时间动态调整的能量节约算法

在目前节点调度方案中,节点的休眠时间在整个节点调度过程中都采用固定的休眠设置,这样会导致网络中部分节点的能量消耗过快,引起节点过早失效,无法实现应用监测。针对该问题,提出了动态调整休眠时间的方法,根据节点以及网络剩余能量延长或者缩短节点休眠时间,在此基础上设计出节点休眠时间动态调整的能量节约算法STDA (sleep time dynamically adjustment the energy saving algorithm)。仿真结果表明,该算法不仅能缓解节点过早死亡的问题,并且有效的延长了网络的生存时间。     

DTN中一种基于接触时间的休眠机制

针对容迟/容断网络(DTN)中能量供应受限的问题,提出一种基于接触时间的休眠机制SSCT(Sleep Scheme based on Contact Time)。节点依据历史接触时间自适应调整等待时间和休眠时间,从而降低休眠期间错失通信机会的概率。仿真实验表明,添加SSCT的Epidemi。算法能够在保证消息交付率的基础上降低网络开销和能耗。相比First Contact算法,SSCT对多副本路由算法的性能提升更加明显。     

基于3CNN-BiGRU的睡眠自动分期研究

针对单通道脑电信号睡眠自动分期效率和准确率问题，提出采用三尺度并行卷积神经网络提取睡眠信号特征和双向门控循环单元学习睡眠阶段之间内部时间关系的3CNN-BiGRU睡眠自动分期模型。首先对原始单通道脑电信号进行带通滤波处理，并采用合成少数类过采样技术进行类平衡，然后送入搭建的模型中进行训练和验证实验，其中采用预训练和微调训练对模型进行优化，采用10次和20次交叉验证提高训练可靠性。不同数据集下的不同模型对比实验结果表明，3CNN-BiGRU模型取得了更高的训练效率和更好的分期准确率。     

小波包能量谱在睡眠分期中的应用

利用小波包技术,根据脑电信号在不同睡眠状态下各脑电节律所占的成分不同,提出一种基于小波包能量谱的睡眠脑电分期方法。首先依据脑电信号各节律的频率特点选择好分解层数对信号进行小波包分解,再重构信号,提取出睡眠脑电信号的各节律;然后运用小波包能量谱计算各节律所占的能量比重;最后用3例脑电数据进行实验。实验结果表明,不同睡眠状态下各脑电节律所占比重不同,随着睡眠的深入,睡眠脑电节律θ和δ所占的能量比重增大,而节律α和β所占的比重在减少。因此,可以运用睡眠脑电信号中各节律所占的成分不同来区分不同的睡眠状态,并可作为睡眠分期的一个特征参数。     

基于神经网络集成的睡眠脑电分期研究

用近似熵对睡眠脑电信号进行分期,由于睡眠Ⅲ期和Ⅳ期近似熵值非常接近,靠近似熵值无法区分,提出基于神经网络集成的睡眠脑电信号分期,采用BP神经网络为分类器,对用AR参数提取的睡眠脑电特征对睡眠Ⅲ期和Ⅳ期进行分期。为进一步提高BP神经网络性能,采用Bagging算法对BP神经网络分类器进行加权投票,实验表明,提出的方法具有很好的分期效果。     

基于周期分割的睡眠自动分期研究

为实现高效的自动睡眠分期，提出一种基于周期分割的时域信号处理方法，采用合并增减序列方法对三个通道多导睡眠图记录（2路脑电，1路眼电）进行周期分割，根据信号波形的周期标记睡眠各期的特征波形，提取特征波形在每一帧数据的时长占比与平均幅值作为特征。双向长短时记忆网络（Bi-directional Long Short-Term Memory，Bi-LSTM）作为分类器，解决传统机器学习方法无法利用睡眠数据时间上下文信息的缺点。对42?699个样本使用交叉验证方法得到了84.8%的平均准确率，实验结果表明合并增减序列方法可以降低脑电信号分析的复杂度，是一种有效的时域信号处理方法，双向长短时记忆网络可以有效提高睡眠分期准确率，具有良好的应用前景。