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OBJECTIVE: To investigate the temporal relationship between cerebral and autonomic activities before and during periodic limb movements in NREM and REM sleep (PLMS). METHODS: Patterns of EEG, cardiac and muscle activities associated with PLMS were drawn from polysomnographic recordings of 14 outpatients selected for the presence of PLMS both in NREM and REM sleep. PLMS were scored during all sleep stages from tibial EMG. Data from a bipolar EEG channel were analyzed by wavelet transform. Heart rate (HR) was evaluated from the electrocardiogram. EEG, HR and EMG activations were detected as transient increase of signal parameters and examined by analysis of variance and correlation analysis independently in NREM and REM sleep. Homologous parameters in REM and NREM sleep were compared by paired t-test. RESULTS: The autonomic component, expressed by HR increase, took place before the motor phenomenon both in REM and NREM sleep, but it was significantly earlier during NREM. In NREM sleep, PLM onset was heralded by a significant activation of delta-EEG, followed by a progressive increase of all the other bands. No significant activations of delta EEG were found in REM sleep. HR and EEG activations positively correlated with high frequency EEG activations and negatively (in NREM) with slow frequency ones. CONCLUSIONS: Our findings suggested a heralding role for delta band only in NREM sleep and for HR during both NREM and REM sleep. Differences in EEG and HR activation between REM and NREM sleep and correlative data suggested a different modulation of the global arousal response. SIGNIFICANCE: In this study, time-frequency analysis and advanced statistical methods enabled an accurate comparison between brain and autonomic changes associated to PLM in NREM and REM sleep providing indications about interaction between autonomic and slow and fast EEG components of arousal response.

Objective: To investigate the temporal relationship between cerebral and autonomic activities before and during periodic limb movements in NREM and REM sleep (PLMS). Methods: Patterns of EEG, cardiac and muscle activities associated with PLMS were drawn from polysomnographic recordings of 14 outpatients selected for the presence of PLMS both in NREM and REM sleep. PLMS were scored during all sleep stages from tibial EMG. Data from a bipolar EEG channel were analyzed by wavelet transform. Heart rate (HR) was evaluated from the electrocardiogram. EEG, HR and EMG activations were detected as transient increase of signal parameters and examined by analysis of variance and correlation analysis independently in NREM and REM sleep. Homologous parameters in REM and NREM sleep were compared by paired t-test. Results: The autonomic component, expressed by HR increase, took place before the motor phenomenon both in REM and NREM sleep, but it was significantly earlier during NREM. In NREM sleep, PLM onset was heralded by a significant activation of delta-EEG, followed by a progressive increase of all the other bands. No significant activations of delta EEG were found in REM sleep. HR and EEG activations positively correlated with high frequency EEG activations and negatively (in NREM) with slow frequency ones. Conclusions: Our findings suggested a heralding role for delta band only in NREM sleep and for HR during both NREM and REM sleep. Differences in EEG and HR activation between REM and NREM sleep and correlative data suggested a different modulation of the global arousal response. Significance: In this study, time–frequency analysis and advanced statistical methods enabled an accurate comparison between brain and autonomic changes associated to PLM in NREM and REM sleep providing indications about interaction between autonomic and slow and fast EEG components of arousal response.

STUDY OBJECTIVE: We present a mathematical model of sleep-EEG structure applied to the analysis of sleep patterns in narcoleptics by combining the 2-process model of sleep regulation and the reciprocal interaction model of REM regulation suggested by McCarley and Hobson. The aim was the individuation of parameters characterizing narcoleptic sleep in comparison to controls. DESIGN: Polysomnographic data were drawn from a previous study about sleep in narcolepsy. The mathematical model was fitted to quantitative EEG data by an optimization procedure. SETTING: Polysomnographic data were recorded in single and sound attenuated hospital rooms, for one night following an adaptation night. PARTICIPANTS: 9 narcoleptic subjects (7 males, 2 females, mean age 39.6 +/- 4.3 years) and 9 age- and sex- matched controls. MEASUREMENTS: Slow Wave Activity (SWA) time series were evaluated by spectral analysis. The sleep model was fitted to SWA profile for each recording and to the averaged SWA profile for each group. Bartlett and Kolmogorov-Smirnov test were used to evaluate the goodness of fit and the accuracy of model predictions. RESULTS: In both controls and narcoleptics the optimization procedure produced a good fit of SWA raw data. The only significant difference between the groups were the RemOn/RemOff coupling parameters, reflecting an enhanced strength of the REM oscillator in narcoleptics. CONCLUSIONS: The mathematical model of sleep provides a substantial description of empirical patterns for both controls and narcoleptics. The variation of values in the parameters describing the strength of RemOn /RemOff interaction is the major feature characterizing narcoleptics; it can explain sleep onset REM periods (SOREMPs) and variations of REM-NREM sleep cycle duration.

STUDY OBJECTIVES: Night work can be dangerous because both circadian sleep propensity (process C) and sleep pressure due to the prolonged wakefulness (process S) contribute to the reduction of vigilance levels. As naps are a countermeasure to sleepiness, this study evaluates the role they play in preventing sleep-related accidents in Italian shift-working police drivers. DESIGN/SETTING/PARTICIPANTS: The study concerns highway car accidents that occurred to Italian shift-working police drivers; it was performed in 2 steps: a retrospective analysis of the overall number of accidents that occurred during the years 1993--1997 (n, 1195), followed by a validation analysis of a smaller cohort of accidents prospectively collected during 2003 (n, 84). INTERVENTIONS: N/A. MEASUREMENTS AND RESULTS: RETROSPECTIVE ANALYSIS: The influence of process S, process C, driver characteristics, and context conditions on accident risk, estimated by means of Cox hazard regression, revealed that nighttime accident risk was mainly influenced by process S levels. Consequently, an experimental mathematical model linking the hourly observed number of accidents to process S levels was designed. Its generalization to the theoretical case of drivers omitting naps showed an increase of about 38% of accidents. PROSPECTIVE ANALYSIS: In order to validate our results, we compared retrospective and prospective sleep patterns: no statistical difference was found. Again, the hourly number of accidents increased with homeostatic sleep pressure; the theoretical efficacy of napping was quantified in 48% accidents decrease. CONCLUSIONS: Our data seem to confirm that napping before working a night shift is an effective countermeasure to alertness and performance deterioration associated with night work. Moreover, this self-initiated behavior could have a prophylactic efficacy in reducing the number of car accidents.

OBJECTIVE: Visual contrast stimulation evokes in man an oscillatory mass response at approximately 20.0-35.0 Hz, consistent with stimulus-dependent synchronous oscillations in multiunit animal recordings from visual cortex, but shorter in duration and phase-locked to stimulus. A factor analysis was applied to characterize the signal structure under stimulus conditions inducing an oscillatory response and to identify possible subcomponents in normal volunteers. METHODS: Contrast stimuli were gratings with a sinusoidal luminance profile (9.0 degrees; 5.0 cycle/degree; 80% contrast; reversal 1.06 Hz). The amplitude spectrum of the signal was computed by Discrete Fourier Transform (DFT) and the oscillatory response was separated from the corresponding visually evoked potential (VEP) by DFT high-pass filter at 19.0 Hz. Nine consecutive waves were identified in all subjects (60 volunteers), with amplitudes/latencies consistent with normative studies. A factor analysis was computed 1- in the frequency domain, on the amplitude values of the signal components (2 Hz resolution), and 2- in the time domain, on the latencies/amplitudes of the averaged VEP and oscillatory responses. RESULTS: (1) Two non-overlapping factors accounted for the approximately 2-20.0 and approximately 20.0-40.0 Hz signal components, with separation of the approximately 20.0-35.0 Hz oscillatory response from low frequency VEPs. (2) Two factors on latencies and one factor on amplitudes (independent of each other and from those of VEPs) accounted for the average approximately 20.0-35.0 Hz oscillatory response. CONCLUSIONS: The factor structure further indicates an oscillatory structure and some independence from conventional VEPs of the human oscillatory response to contrast, with a separation between the oscillatory response early and late waves possibly reflecting functional differences.

Breathing is an essential feature of living organisms, and control of breathing is a very complex topic in human physiology. Breathing is differently modulated under conditions of wakefulness and sleep, and ventilation decreases during sleep in normal subjects. In patients with respiratory diseases, sleep represents a very vulnerable condition, since gas exchange usually worsens, especially in REM sleep. Several types of respiratory events can occur during sleep, and knowledge of control of breathing is essential to understand their pathophysiology. This chapter summarizes the main characteristics of ventilation during both wakefulness and sleep, the differences found between genders, and the effects of sleep deprivation/fragmentation on control of breathing, with special attention to metabolic control of ventilation which plays a major role in maintaining arterial blood gases homeostasis.