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A number of phasic events influence sleep quality and sleep macrostructure. The detection of arousals and the analysis of cyclic alternating patterns (CAP) support the evaluation of sleep fragmentation and instability. Sixteen polygraphic overnight recordings were visually inspected for conventional Rechtscaffen and Kales scoring, while arousals were detected following the criteria of the American Sleep Disorders Association (ASDA). Three electroencephalograph (EEG) segments were associated to each event, corresponding to background activity, pre-arousal period and arousal. The study was supplemented by the analysis of time-frequency distribution of EEG within each subtype of phase A in the CAP. The arousals were characterized by the increase of alpha and beta power with regard to background. Within NREM sleep most of the arousals were preceded by a transient increase of delta power. The time-frequency evolution of the phase A of the CAP sequence showed a strong prevalence of delta activity during the whole A1, but high amplitude delta waves were found also in the first 2/3 s of A2 and A3, followed by desynchronization. Our results underline the strict relationship between the ASDA arousals, and the subtype A2 and A3 within the CAP: in both the association between a short sequence of transient slow waves and the successive increase of frequency and decrease of amplitude characterizes the arousal response.

Unlike other sleep disorders, such as sleep-related breathing disorders and periodic limb movement (PLM), the nature and severity of which are quantified by specific respiratory and motor indexes, no apparent organ dysfunction underlies several cases of insomnia (in particular primary insomnia), which can be objectively diagnosed only through the structural alterations of sleep. Polysomnography (PSG) investigation indicates that insomnia is the outcome of a neurophysiological disturbance that impairs the regulatory mechanisms of sleep control, including sleep duration, intensity, continuity and stability. In particular, analysis of sleep microstructure has permitted to establish that etiologic factors of different nature (including depressive disorders) exert a common destabilizing action on sleep, which is reflected in an increase of cyclic alternating pattern (CAP) rate. These premises allow us to attribute a more objective identity to insomnia, which risks otherwise to be considered as an unexplainable mental complaint. In conclusion, PSG remains the "gold standard" for measuring sleep, and especially insomnia.

The internal structure of night sleep in subjects with narcolepsy significantly differs as compared to the controls. The differences consist in the presence of sleep onset REM periods (SOREMPs) and in a longer duration (120 minutes versus 90 minutes) of the NREM-REM cycles. Another difference consists in the lack of increase in REM sleep duration during the night, cycle after cycle. These characteristics, taken as a whole, suggest that the sleep structure of narcoleptic patients is due to an imbalance between the homeostatic process and the enhanced pressure of REM inducing mechanisms. With regard to the intrasleep dynamics the current extension of the Two-process model cannot give a satisfactory explanation because REM sleep is included as an external trigger with no intrinsic rhythmic property. Neurobiologic studies lead to consider the REM cyclic occurrence as the result of reciprocal interaction between two populations of REM-ON and REM-OFF cells. The introduction in the Two-process-model extended to the intra-night dynamics of an ultradian oscillator, based on a REM-ON--REM-OFF reciprocal interaction, allows the theoretical possibility to simulate a SOREMP. This revised model accounts for the progressive extension of REM sleep-periods duration in the course of the night. The sleep structure of narcoleptic patients can be simulated leaving unaltered the pressure of the homeostatic process and enhancing the REM inducing pressure. Such an enhanced REM pressure can be achieved by increasing the value of the numerical coefficient, which represents the strength of connection between the two types of REM-ON and REM-OFF cells. This modification allows not only to obtain a stronger ultradian oscillator but also a longer periodicity of REM sleep occurrence. By coupling the homeostatic process, considered as normal, to such a modified ultradian oscillator, our model can explain the intra-night sleep dynamics of narcoleptic subjects. A REM-ON REM-OFF dysregulation can be hypothesized to explain the pathophysiological basis of nocturnal sleep features in narcolepsy.

Objective: The hypothesis that the two main synchronizing mechanisms (spindle and delta oscillations) acting during non-rapid eye movement sleep show opposite relationship with the distribution of generalized epileptiform discharges (GEDs) during sleep was evaluated., Methods: We studied the temporal relationship between the distribution of sleep GEDs and the dynamics of Sigma Activity (SA, 12-16 Hz) and Delta Activity (DA, 0.5-4.5 Hz) in 5 children affected by childhood absence epilepsy., Results: Using correlation techniques, we found a high and positive correlation between GEDs and SA, while DA resulted negatively correlated with GEDs., Conclusion: Sleep generalized spike-and-slow-waves seems to be produced when spindle synchronizing mechanisms are active while DA production seem to exert an inhibiting role. Such a feature seems to be common to other childhood partial and undetermined epileptic syndromes.

Purpose: Landau-Kleffner syndrome (LKS) is characterized by a marked increase of interictal epileptiform discharges (IEDs) during sleep. During nonrapid eye movement (NREM) sleep, neuronal membrane potential oscillations lead to the appearance of spindles and delta waves in the surface EEG and might develop into paroxysmal synchronization. Spectral analysis allows the quantitative description of the dynamics of delta (slow-wave activity, SWA, 0.5-4.5 Hz) and sigma activity (SA, 12.0-16.0 Hz) and can be used to assess the relation between SA, SWA, and IEDs during sleep., Methods: We performed six overnight continuous EEG-polysomnographic studies in three patients with LKS. The temporal series of SWA and SA were obtained from a spike-free derivation lead. The IEDs count was performed on the most active lead. Relations between sigma and SWA and time series of IEDs were tested by means of correlation techniques after data normalization., Results: Our results revealed a significantly higher correlation between IEDs and SA with respect to SWA in all the subjects, in total sleep time. The same analysis limited to NREM sleep highlights the better correlation between SA and IEDs., Conclusions: Our data suggest that neural mechanisms involved in the generation of sleep spindles facilitate IEDs production in LKS.

A method for the automatic detection of arousals in digital polysomnographic recordings is described. The computer program analyzed two EEG and one EMG derivations marking variable length segments as arousals. The processing of EEG data started from the wavelet transform, which characterizes the signal in the time-frequency domain, and resulted in a set of indices used to discriminate possible arousal segments. Transient increases in muscle activity were also identified, while a multichannel and context sensitive analysis allowed arousal detection. Out of 11 overnight recordings, 3 were used as the training set and 8 as the program testing set. In the first stage of the study two EEG experts inspected the tracings independently to score arousals. They then reviewed all recordings and jointly examined each event for validation, both those scored by themselves and those scored by the computer. A reference set of definite arousals (1125 in the testing set) and a number of uncertain events (266) were thus obtained. The sensitivity of the automatic system (88.1%) was higher than that of the human experts (72.4 and 78.4%) while the selectivity was lower (74.5% for the automatic system, 83.0 and 82.0% for the experts). This suggested that automatic detection, followed by an expert's validation, may render the analysis of arousals more widely feasible as well as support the study of arousal features.

We present a new model of sleep regulation which integrates the two process model and the limit cycle reciprocal interaction model. In the two process model the interaction of a homeostatic process (S signal) and of a circadian process determines the timing of sleep and waking. In the limit cycle model, the NREM-REM sleep cycle is generated by the reciprocal interaction of two coupled cell population. The present model integrates the two considered models introducing the trigger REM-NREM generated by the REM-ON signal and activated when REM-ON overcomes the S signal.

Abstract: 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. [Copyright &y& Elsevier]