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睡眠脑电的分形维数分析   总被引:3,自引:0,他引:3  
为了研究是否用维数来描述脑功能的复杂程序,我们计算了5例正常人整夜睡眠时不同阶段的EEG资料维数。结果显示,随着睡眠深度的增加而维数降低。这一事实和生理概念及其他作者所报道的结果相一致,故我们考虑维数分析在表示脑功能状态上有一定的理论意义。如果我们用简化法来计算维数,此法就有可能实时地应用于临床诊断。  相似文献   

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Rotating shift and permanent night work arrangements are known to compromise sleep. This study examined the effects of work schedule on sleep duration, excessive sleepiness, sleep attacks, driving, and domestic/professional accidents. A representative sample of the general population of the state of New York—3,345 individuals ≥18 yrs of age—was interviewed by telephone regarding their sleep and psychiatric and organic disorders. Multivariate models were applied to derive odds ratios (OR) after adjustment for age, sex, physical illness, mental disorders, obstructive sleep apnea, and sleep duration. On average (±SE), workers slept 6.7?±?1.5?h, but 40% slept <6.5?h/main sleep episode. Short-sleep duration (<6?h) was strongly associated with fixed night (OR: 1.7) and day-evening-night shiftwork arrangement (OR: 1.9). Some 20% of the workers manifested excessive sleepiness in situations requiring high attention, and it was associated with the fixed night (OR: 3.3) and day-evening-night work arrangements (OR: 1.5). Overall, 5% of the workers reported sleep attacks; however, they occurred three-times more frequently in the fixed night (15.3%) than other work arrangements (OR: 3.2). Driving accidents during the previous 12 months were reported by 3.6% of the workers and were associated with fixed night (OR: 3.9) and day-evening-night (OR: 2.1) work schedules. The findings of this study indicate that working outside the regular daytime hours was strongly associated with shorter sleep duration, sleepiness, and driving accident risk. Night work is the most disrupting, as it is associated with insufficient sleep during the designated rest span and excessive sleepiness and sleep attacks during the span of activity, with an associated consequence being increased driving accident risk. (Author correspondence: mohayon@stanford.edu)  相似文献   

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The circadian pacemaker and sleep homeostasis play pivotal roles in vigilance state control. It has been hypothesized that age-related changes in the human circadian pacemaker, as well as sleep homeostatic mechanisms, contribute to the hallmarks of age-related changes in sleep, that is, earlier wake time and reduced sleep consolidation. Assessments of circadian parameters in healthy young (~20–30 years old) and older people (~65–75 years old)—in the absence of the confounding effects of sleep, changes in posture, and light exposure—have demonstrated that an earlier wake time in older people is accompanied by about a 1h advance of the rhythms of core body temperature and melatonin. In addition, older people wake up at an earlier circadian phase of the body temperature and plasma melatonin rhythm. The amplitude of the endogenous circadian component of the core body temperature rhythm assessed during constant routine and forced desynchrony protocols is reduced by 20–30% in older people. Recent assessments of the intrinsic period of the human circadian pacemaker in the absence of the confounding effects of light revealed no age-related reduction of this parameter in both sighted and blind individuals. Wake maintenance and sleep initiation are not markedly affected by age except that sleep latencies are longer in older people when sleep initiation is attempted in the early morning. In contrast, major age-related reductions in the consolidation and duration of sleep occur at all circadian phases. Sleep of older people is particularly disrupted when scheduled on the rising limb of the temperature rhythm, indicating that the sleep of older people is more susceptible to arousal signals genernpated by the circadian pacemaker. Sleep-homeostatic mechanisms, as assayed by the sleep-deprivation–induced increase of EEG slow-wave activity (SWA), are operative in older people, although during both baseline sleep and recovery sleep SWA in older people remains at lower levels. The internal circadian phase advance of awakening, as well as the age-related reduction in sleep consolidation, appears related to an age-related reduction in the promotion of sleep by the circadian pacemaker during the biological night in combination with a reduced homeostatic pressure for sleep. Early morning light exposure associated with this advance of awakening in older people could reinforce the advanced circadian phase. Quantification of the interaction between sleep homeostasis and circadian rhythmicity contributes to understanding age-related changes in sleep timing and quality. (Chronobiology International, 17(3), 285–311, 2000)  相似文献   

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The purpose of this study was to determine whether a sleep log parameter could be used to estimate the circadian phase of normal, healthy, young adults who sleep at their normal times, and thus naturally have day-to-day variability in their times of sleep. Thus, we did not impose any restrictions on the sleep schedules of our subjects (n=26). For 14 d, they completed daily sleep logs that were verified with wrist activity monitors. On day 14, salivary melatonin was sampled every 30 min in dim light from 19:00 to 07:30h to determine the dim light melatonin onset (DLMO). Daily sleep parameters (onset, midpoint, and wake) were taken from sleep logs and averaged over the last 5, 7, and 14 d before determination of the DLMO. The mean DLMO was 22:48±01:30 h. Sleep onset and wake time averaged over the last 5 d were 01:44±01:41 and 08:44±01:26 h, respectively. The DLMO was significantly correlated with sleep onset, midpoint, and wake time, but was most strongly correlated with the mean midpoint of sleep from the last 5 d (r=0.89). The DLMO predicted using the mean midpoint of sleep from the last 5 d was within 1 h of the DLMO determined from salivary melatonin for 92% of the subjects; in no case did the difference exceed 1.5 h. The correlation between the DLMO and the score on the morningness-eveningness questionnaire was significant but comparatively weak (r=-0.48). We conclude that the circadian phase of normal, healthy day-active young adults can be accurately predicted using sleep times recorded on sleep logs (and verified by actigraphy), even when the sleep schedules are irregular.  相似文献   

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