Regional differences in the dynamics of the cortical EEG in the rat after sleep deprivation.

OBJECTIVE: To investigate regional changes of the cortical sleep EEG in the rat, recordings were obtained from a frontal and an occipital derivation, on a baseline day (n = 14 male rats, Sprague-Dawley strain) and after 24 h sleep deprivation (SD, n = 7). METHODS: Spectral analysis of the vigilance states revealed state and frequency specific differences in EEG power by two-way ANOVA and post-hoc t tests. RESULTS: In the theta band (6.25-9.0 Hz) occipital power was larger than frontal power in waking and REM sleep, whereas frontal power was larger in the frequency range between 10.25-16.0 Hz in non-REM sleep and REM sleep. After SD frontal power in the 2-4 Hz band in non-REM sleep was increased more than occipital power and frontal power in the 10.25-16.0 Hz range was more attenuated. In REM sleep frontal power in the theta band and in the 10.25-16.0 Hz range was more increased than occipital power. Power in the waking EEG did not differ between the two derivations after SD. CONCLUSIONS: The differential responses to SD may reflect regional use-dependent aspects of sleep regulation. These observations support the notion that sleep is not only a global phenomenon but has also local, use-dependent features.     

REM sleep EEG spectral analysis in patients with first-episode schizophrenia

The pathophysiology of schizophrenia includes abnormalities in subcortical-cortical transfer of information that can be studied using REM sleep EEG spectral analysis, a measure that reflects spontaneous and endogenous thalamocortical activity. We recorded 10 patients with first-episode schizophrenia and 30 healthy controls for two consecutive nights in a sleep laboratory, using a 10-electrode EEG montage. Sixty seconds of REM sleep EEG without artifact were analyzed using FFT spectral analysis. Absolute and relative spectral amplitudes of five frequency bands (delta, theta, alpha, beta1 and beta2) were extracted and compared between the two groups. Frequency bands with significant differences were correlated with BPRS positive and negative symptoms scores. Patients with schizophrenia showed lower relative alpha and higher relative beta2 spectral amplitudes compared to healthy controls over the averaged total scalp. Analysis using cortical regions showed lower relative alpha over frontal, central and temporal regions and higher relative beta2 over the occipital region. Absolute spectral amplitude was not different between groups for any given EEG band. However, absolute alpha activity correlated negatively with BPRS positive symptoms scores and correlated positively with negative symptoms scores. Since similar results have been reported following EEG spectral analysis during the waking state, we conclude that abnormalities of subcortical-cortical transfer of information in schizophrenia could be generated by mechanisms common to REM sleep and waking.     

Effect of sleep deprivation on sleep and EEG power spectra in the rat

EEG power spectra of the rat were computed for consecutive 4-s epochs of the daily light period and matched with the scores of the vigilance states. Sleep was characterized by a progressive decline of low frequency spectral values (i.e. slow wave activity) in non-rapid eye movement (non-REM) sleep, and a progressive increase in the amount of REM sleep. During recovery from 24-h total sleep deprivation (TSD) the following changes were observed: an increase of slow wave activity in non REM sleep with a persisting declining trend; an enhancement of theta activity (7.25-10.0 Hz) both in REM sleep and waking; a decrease of non-REM sleep and an increase of REM sleep. In addition, a slow wave EEG pattern prevailed in the awake and behaving animal during the initial recovery period. In selective sleep deprivation paradigms, either REM sleep or slow wave activity in non-REM sleep was prevented during a 2-h period following upon 24-h TSD. During both procedures, non-REM sleep spectra in the lowest frequency band showed no increase. There was no evidence for a further enhancement of slow wave activity after its selective deprivation. The results indicate that: (1) slow wave activity in non-REM sleep and theta activity in REM sleep may reflect sleep intensity; and (2) REM sleep and active waking, the two states with dominant theta activity, may be functionally related.     

Rhythmic hippocampal slow oscillation characterizes REM sleep in humans.

Hippocampal rhythmic slow activity (RSA) is a well-known electrophysiological feature of exploratory behavior, spatial cognition, and rapid eye movement (REM) sleep in several mammalian species. Recently, RSA in humans during spatial navigation was reported, but systematic data regarding human REM sleep are lacking. Using mesio-temporal corticography with foramen ovale electrodes in epileptic patients, we report the presence of a 1.5-3-Hz synchronous rhythmic hippocampal oscillation seemingly specific to REM sleep. This oscillation is continuous during whole REM periods, is clearly observable by visual inspection, and appears in tonic and phasic REM sleep episodes equally. Quantitative analysis proved that this 1.5-3-Hz frequency band significantly differentiates REM sleep from waking and slow-wake sleep (SWS). No other frequency band proved to be significant or showed this high rhythmicity. Even in temporo-lateral surface recordings, although visually much less striking, the relative power of the 1.5-3-Hz frequency band differentiates REM sleep from other states with statistical significance. This could mean that the 1.5-3-Hz hippocampal RSA spreads over other cortical areas in humans as in other mammals. We suggest that this oscillation is the counterpart of the hippocampal theta of mammalian REM sleep, and that the 1.5-3-Hz delta EEG activity is a basic neurophysiological feature of human REM sleep.     

Spectral analysis of all-night sleep EEG in healthy adults

Power and coherence spectra were computed from all-night sleep EEG records in 6 healthy adult subjects. Derivations were from F3, F4, P3, P4, O1, O2, T3, and T4 to the vertex (Cz). Records were conventionally scored into sleep stages. Average power per sleep stage was maximal at frequencies 0.4-6 c/s in stage 4, at 6-10 c/s in either stage 3 or stage 4, at 12-14 c/s in stage 2 and at 14-30 c/s in stage 1. The average power range from highest values in the lowest frequency band to lowest values in the highest frequency band showed marked differences between sleep stages: It was lowest (12-14 dB) in stage 1, followed by stage 2 (20-22 dB), and stage 3 (16-28 dB), and largest in stage 4 (29-32 dB). REM sleep (15-16 sB) was between stage 1 and 2. The waking state showed an average power range of 11-15 dB. Alpha power at 8-10 c/s in occipital and parietal leads was remarkably constant during sleep, i.e. independent of sleep stage. Coherence showed maximal values at 2-8 c/s in REM sleep, at 8-12 c/s in stage 4, at 12-17 c/s in either stage 3 or 4, and at 17-30 c/s again in stage REM. There was significant coherence increase at 2-8 and 17-30 c/s from NREM to REM sleep, most pronounced between parietal to vertex derivations. Overall coherence between both occipital-to-vertex, or between occipital and parietal-to-vertex derivations, was essentially higher than in the other derivations. The results, essentially, give a comprehensive phenomenology of the dynamic spectral structure of all-night sleep EEG. They suggest that the different brain states during sleep (e.g. stage 1 NREM vs. REM) which are associated with different functions (e.g. hypnagogic hallucinations vs. dreams) differ in EEG spectral parameters if coherence is considered. Likewise, they suggest that studies of automatic sleep staging based exclusively on EEG spectral parameters appear promising.     

Functional topography of the human nonREM sleep electroencephalogram

The sleep EEG of healthy young men was recorded during baseline and recovery sleep after 40 h of waking. To analyse the EEG topography, power spectra were computed from 27 derivations. Mean power maps of the nonREM sleep EEG were calculated for 1-Hz bins between 1.0 and 24.75 Hz. Cluster analysis revealed a topographic segregation into distinct frequency bands which were similar for baseline and recovery sleep, and corresponded closely to the traditional frequency bands. Hallmarks of the power maps were the frontal predominance in the delta and alpha band, the occipital predominance in the theta band, and the sharply delineated vertex maximum in the sigma band. The effect of sleep deprivation on EEG topography was determined by calculating the recovery/baseline ratio of the power spectra. Prolonged waking induced an increase in power in the low-frequency range (1-10.75 Hz) which was largest over the frontal region, and a decrease in power in the sigma band (13-15.75 Hz) which was most pronounced over the vertex. The topographic pattern of the recovery/baseline power ratio was similar to the power ratio between the first and second half of the baseline night. These results indicate that changes in sleep propensity are reflected by specific regional differences in EEG power. The predominant increase of low-frequency power in frontal areas may be due to a high 'recovery need' of the frontal heteromodal association areas of the cortex.     

Coherence of the electroencephalogram during the first sleep cycle.

OBJECTIVE: The increasing amplitude of the electroencephalogram (EEG) during non-rapid eye movement sleep implies a progressive synchronization of neuronal activity. We sought to characterize the spatial relationship of cortical activity at different frequencies during the first sleep cycle, focusing on sleep stages 3 and 4 (slow wave sleep). METHODS: Sleep EEGs were obtained at home from six adults using a portable recorder. Signal power and magnitude squared coherence were measured during the first sleep cycle. Spectra obtained from bipolar and common reference derivations were compared. RESULTS: During slow wave sleep, signal power is highest in the delta frequency band and regional coherence below 5 Hz is broadly distributed. Although signal power in the alpha and sigma frequency bands is lower, peaks of regional coherence in those bands are similar to or higher than delta-band coherence. Regional coherence during slow wave sleep is differentially distributed with a 14 Hz component in central and posterior regions and a 10 Hz component in frontal and central regions. CONCLUSIONS: Ten and 14 Hz rhythms are an essential component of slow wave sleep. SIGNIFICANCE: The interpretation of scalp EEG power and coherence spectra is limited by the lack of a satisfactory recording reference. However, conclusions can be made by comparing and contrasting results from both bipolar and common reference recordings.     

Sleep deprivation: effect on sleep stages and EEG power density in man

Sleep was analysed in 8 young adults subjects during two baseline nights and two recovery nights following 40.5 h sleep deprivation. Sleep stages were scored from the polygraph records according to conventional criteria. In addition, the EEG records of the entire nights were subjected to spectral analysis to compute the frequency distribution of the power density in the 0.25-25 Hz range for 0.5 Hz or 1.0 Hz bins. In the first recovery night, the power density in the delta band was significantly higher than baseline for total sleep time as well as for sleep stages 2, 3 and 4, 4 and REM. These changes were not restricted to the delta band, but extended to higher frequency bands. Minor, but significant, effects of sleep deprivation were seen in the power density distribution of the second recovery night. In the baseline nights, a progressive reduction of power density in the delta/theta range was present for successive non-REM-REM sleep cycles for total sleep time and stages 2, 3 and 4, and 4. The results show that effects of sleep deprivation as well as trends within the sleep periods are readily apparent from spectral analysis, but are inadequately reflected by conventional sleep scoring. When the power density values were integrated over the entire frequency range (0.75-25 Hz) for each non-REM-REM sleep cycle, an exponential decline from cycle 1 to cycle 3 was suggested. The present findings support the hypothesis that the EEG power density in the low frequency range is an indicator of a progressively declining process during sleep whose initial value is determined by the duration of prior waking.     

Characterization of the mu rhythm during rapid eye movement sleep.

OBJECTIVE: The rolandic mu rhythm, a resting activity of somatosensory cortex, is a striking feature of the waking human electroencephalogram. This study will demonstrate that activity with identical features occurs during rapid eye movement (REM) sleep. METHODS: Eye and chin leads were added during prolonged closed circuit television (video) electroencephalographic (EEG) recording with scalp (12 patients) or subdural electrodes including 64 contract grids over the frontoparietal cortices (5 patients). Sleep staging was performed by reformatting into standard polysomnography montages (using two EEG channels, and eye and chin channels) and applying standard scoring criteria. The recordings were then reviewed using all EEG channels to assess rhythmic EEG activity by a reader blinded to the sleep staging. RESULTS: During scalp recordings, 7-10 Hz central rhythms were seen during wakefulness in 7 patients, with 6 of these also having similar rhythms during REM sleep. Similar activity was seen over somatosensory cortex during wakefulness and REM in all invasively recorded patients. This activity was blocked by contralateral body movement or contralateral somatosensory stimuli, even during REM sleep. It was absent in other sleep stages. CONCLUSIONS: This REM sleep activity recapitulates all the characteristics of the waking rolandic mu rhythm. This demonstrates functional similarity between the states of wakefulness and REM sleep.     

Alpha-delta sleep in a case with non-24 h sleep-wake syndrome: Quantitative electroencephalogram analysis of alpha and delta band waves

Abstract  Four all-night polysomnograms of a 39-year-old male patient with non-24 h sleep-wake syndrome were recorded. We analysed electroencephalograms (EEG) with the power spectrum method and the wave pattern recognition analysis of Fujimori. The EEC of the rest waking condition showed normal patterns. High-voltage diffuse alpha band waves were observed in sleep stages 2, 3 and 4. The integrated area of the alpha band waves in the analysis epochs showed a strong positive correlation to the delta hand components in the power spectrum of the same epoch during sleep (correlation coefficients r = 0.762–0.815). Alpha hand waves during sleep were clearly different from the alpha waves in the rest waking condition, with respect to slower peak frequency and the frontal dominant voltage distribution.     

EEG topography during sleep inertia upon awakening after a period of increased homeostatic sleep pressure

ObjectiveBehavioral and physiological indexes of high sleep inertia (SI) characterize the awakening from recovery (REC) sleep after prolonged wakefulness, but the associated electroencephalogram (EEG) topography has never been investigated. Here, we compare the EEG topography following the awakening from baseline (BSL) and REC sleep.MethodsWe have recorded the EEG waking activity of 26 healthy subjects immediately after the awakening from BSL sleep and from REC sleep following 40 h of prolonged wakefulness. In both BSL and REC conditions, 12 subjects were awakened from stage 2 sleep, and 14 subjects from rapid eye movement (REM) sleep. The full-scalp waking EEG (eyes closed) was recorded after all awakenings.ResultsSubjects awakened from REC sleep showed a reduction of fronto-central alpha and beta-1 activities, while no significant effects of the sleep stage of awakening have been observed. Positive correlations between pre- and post-awakening EEG modifications following REC sleep have been found in the posterior and lateral cortices in the frequency ranges from theta to beta-2 and (only for REM awakenings) extending to the fronto-central regions in the beta-1 band, and in the midline central and parietal derivations for the alpha and delta bands, respectively.ConclusionsThese findings suggest that the higher SI after REC sleep may be due to the fronto-central decrease of alpha and beta-1 activity and to the persistence of the sleep EEG features after awakening in the posterior, lateral, and fronto-central cortices, without influences of the sleep stage of awakening.     

Ultradian rhythms of alternating cerebral hemispheric EEG dominance are coupled to rapid eye movement and non-rapid eye movement stage 4 sleep in humans

Objective: To replicate the left minus right (L−R) hemisphere EEG power shifts coupled to rapid eye movement (REM) and non-rapid eye movement (NREM) sleep observed in 1972 by Goldstein (Physiol Behav (1972) 811), and to characterize the L−R EEG power spectra for total EEG, delta, theta, alpha and beta bands.Background: Ultradian alternating cerebral hemispheric dominance rhythms are observed using EEG during both waking and sleep, and with waking cognition. The question of whether this cerebral rhythm is coupled to the REM–NREM sleep cycle and the basic rest–activity cycle (BRAC) deserves attention.Methods: L−R EEG signals for ten young, normal adult males were converted to powers and the means were normalized, smoothed and subtracted. Sleep hypnograms were compared with L−R EEGs, and spectra were computed for C3, C4 and L−R EEG powers.Results: Significant peaks were found for all C3, C4 and L−R frequency bands at the 280–300, 75–125, 55–70 and 25–50 min bins, with power dominating in the 75–125 min bin. L−R EEG rhythms were observed for all bands. Greater right hemisphere EEG dominance was found during NREM stage 4 sleep, and greater left during REM for total EEG, delta and alpha bands (Chi-squares, P<0.001). Theta was similar, but not significant (P=0.163), and beta was equivocal.Conclusions: Earlier ultradian studies show that lateral EEG and L−R EEG power have a common pacemaker, or a mutually entrained pacemaker with the autonomic, cardiovascular, neuroendocrine and fuel-regulatory hormone systems. These results for L−R EEG coupling to sleep stages and multi-variate relations may present a new perspective for Kleitman's BRAC and for diagnosing variants of pathopsychophysiological states.     

Effects of bupropion SR on anterior paralimbic function during waking and REM sleep in depression: preliminary findings using

This study sought to clarify the effects of bupropion SR on anterior paralimbic function in depressed patients by studying changes in the activation of these structures from waking to REM sleep both before and after treatment. Twelve depressed patients underwent concurrent EEG sleep studies and [18F]fluoro-2-deoxy-D-glucose ([18F]-FDG) positron emission tomography (PET) scans during waking and during their second REM period of sleep before and after treatment with bupropion SR. Nine subjects completed pre- and post-treatment waking PET studies. Five subjects completed pre- and post-treatment waking and REM sleep PET studies. Bupropion SR treatment did not suppress electrophysiologic measures of REM sleep, nor did it alter an indirect measure of global metabolism during either waking or REM sleep. Bupropion SR treatment reversed the previously observed deficit in anterior cingulate, medial prefrontal cortex and right anterior insula activation from waking to REM sleep. In secondary analyses, this effect was related to a reduction in waking relative metabolism in these structures following treatment in the absence of a significant effect on REM sleep relative metabolism. The implications of these findings for the relative importance of anterior paralimbic function in REM sleep in depression and for the differential effects of anti-depressant treatment on brain function during waking vs. REM sleep are discussed.     

Spectral structure and brain mapping of human alpha activities in different arousal states

In a study with 10 young, healthy subjects, alpha activities were studied in three different arousal states: eyes closed in relaxed wakefulness (EC), drowsiness (DR), and REM sleep. The alpha band was divided into three subdivisions (slow, middle, and fast) which were analyzed separately for each state. The results showed a different spectral composition of alpha band according to the physiological state of the subject. Slow alpha seemed to be independent of the arousal state, whereas middle alpha showed a difference between REM and the other states. The fast-alpha subdivision appears mainly as a waking EEG component because of the increased power displayed only in wakefulness and lower and highly stable values for DR and REM. Scalp distribution of alpha activity was slightly different in each state: from occipital to central regions in EC, this topography was extended to fronto-polar areas in DR, with a contribution from occipital to frontal regions in REM sleep. These results provide evidence for an alpha power modulation and a different scalp distribution according to the cerebral arousal state.     

Changes in forebrain function from waking to REM sleep in depression: preliminary analyses of [18F]FDG PET studies.

Based on recent functional brain imaging studies of healthy human REM sleep, we hypothesized that alterations in REM sleep in mood disorder patients reflect a functional dysregulation within limbic and paralimbic forebrain structures during that sleep state. Six unipolar depressed subjects and eight healthy subjects underwent separate [18F]2-fluoro-2-deoxy-D-glucose ([18F]FDG) PET scans during waking and during their first REM period of sleep. Statistical parametric mapping contrasts were performed to detect changes in relative regional cerebral glucose metabolism (rCMRglu) from waking to REM sleep in each group as well as interactions in patterns of change between groups. Clinical and EEG sleep comparisons from an undisturbed night of sleep were also performed. In contrast to healthy control subjects, depressed patients did not show increases in rCMRglu in anterior paralimbic structures in REM sleep compared to waking. Depressed subjects showed greater increases from waking to REM sleep in rCMRglu in the tectal area and a series of left hemispheric areas including sensorimotor cortex, inferior temporal cortex, uncal gyrus-amygdala, and subicular complex than did the control subjects. These observations indicate that changes in limbic and paralimbic function from waking to REM sleep differ significantly from normal in depressed patients.     

Periodic limb movements both in non-REM and REM sleep: Relationships between cerebral and autonomic activities

ObjectiveTo investigate the temporal relationship between cerebral and autonomic activities before and during periodic limb movements in NREM and REM sleep (PLMS).MethodsPatterns 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.ResultsThe 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.ConclusionsOur 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.SignificanceIn 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.     

Cortical EEG topography of REM onset: the posterior dominance of middle and high frequencies.

OBJECTIVES: To investigate the brain topography of human sleep electroencephalography (EEG) along the antero-posterior axis during rapid eye movement sleep (REM) onset and REM offset, by means of a quantitative analysis of EEG changes. METHODS: EEG power values were calculated across a 1.00-25.75 Hz frequency range during time intervals preceding and following REM onset of the first 4 sleep cycles of 10 normal subjects. Topographical changes were assessed through Fpz-A1, Fz-A1, Cz-A1, Pz-A1, Oz-A1 recordings during NREM-REM-NREM transitions. RESULTS: The temporal dynamics of REM onset is characterized by a specific topographical pattern of EEG changes with a relatively higher EEG activity at posterior sites: Oz does not show any clear change within the alpha and beta frequencies, at variance with the marked reductions of the other sites, while it shows reductions of power in the delta/theta and sigma frequency ranges of smaller size as compared to the other sites. REM offset does not appear as a mirror-image of REM onset, since the pattern of regional differences characterizing the NREM sleep preceding REM onset is not fully reached. CONCLUSIONS: REM onset is characterized by a general change of EEG activity toward a relative occipital diffusion of power, specifically distinguished by a posterior dominance of middle and high frequencies.     

Effects of medial thalamotomy and pallido-thalamic tractotomy on sleep and waking EEG in pain and Parkinsonian patients.

OBJECTIVES: Investigation of sleep and sleep EEG before and after stereotactic neurosurgery. METHODS: All-night polysomnographic recordings were obtained in 3 neurogenic pain patients and 3 parkinsonian patients. One subject of each group was recorded in addition 3 months after surgery. Stereotactic operations were performed in the medial thalamus and on the pallido-thalamic tract to relieve neurogenic pain and parkinsonian symptoms, respectively. RESULTS: Sleep efficiency was little affected by the surgical intervention in neurogenic pain patients and a dramatic reduction in REM sleep occurred, which had recovered in the subject recorded after 3 months. After the surgery parkinsonian patients showed an increase in total sleep time and in sleep efficiency, and a decrease in REM sleep latency. Sleep efficiency remained elevated in the 3 months follow-up. Medial thalamotomy abolished spindle frequency activity (SFA) in the power and coherence spectra in non-REM sleep stage 2 systematically. Pallido-thalamic tractotomy attenuated SFA only to varying degrees. After 3 months SFA had reemerged. The alpha peak of the waking EEG was shifted to lower frequencies after surgery in 5 of 6 patients and had reverted to the original frequency 3 months later. CONCLUSIONS: Medial thalamotomy or pallido-thalamic tractotomy had acute and reversible effects on the EEG and long-term deleterious side effects of stereotactic surgery on sleep and sleep EEG are improbable. The results provide further evidence for the involvement of the human thalamus in the generation of sleep spindles.     

Inverse coupling between ultradian oscillations in delta wave activity and heart rate variability during sleep.

OBJECTIVE: We investigate the relationship between changes in heart rate variability and electroencephalographic (EEG) activity during sleep. METHOD: Nine male subjects with regular non-rapid-eye movement-rapid-eye movement (NREM-REM) sleep cycles were included in the study. They underwent EEG and cardiac recordings during one experimental night. Heart rate variability was determined over 5-min periods by the ratio of low frequency to low frequency plus high frequency power [LF/(LF+HF)] calculated using spectral analysis of R-R intervals. EEG spectra were analyzed using a fast Fourier transform algorithm. RESULTS: We found an ultradian 80-120 min rhythm in the LF/(LF+HF) ratio, with high levels during rapid eye movement (REM) sleep and low levels during slow wave sleep (SWS). During sleep stage 2 there was a progressive decrease in the transition from REM sleep to SWS, and an abrupt increase from SWS to REM sleep. These oscillations were significantly coupled in a 'mirror-image' to the overnight oscillations in delta wave activity, which reflect sleep deepening and lightening. Cardiac changes preceded EEG changes by about 5 min. CONCLUSIONS: These findings demonstrate the existence of an inverse coupling between oscillations in delta wave activity and heart rate variability. They indicate a non-uniformity in sleep stage 2 that underlies ultradian sleep regulation.     

Dimensional complexity and spectral properties of the human sleep EEG. Electroencephalograms.

OBJECTIVE: The relevance of the dimensional complexity (DC) for the analysis of sleep EEG data is investigated and compared to linear measures. METHODS: We calculated DC of artifact-free 1 min segments of all-night sleep EEG recordings of 4 healthy young subjects. Non-linearity was tested by comparing with DC values of surrogate data. Linear properties of the segments were characterized by estimating the self-similarity exponent alpha based on the detrended fluctuation analysis which quantifies the persistence of the signal and by calculating spectral power in the delta, theta, alpha and sigma bands, respectively. RESULTS: We found weak nonlinear signatures in all sleep stages, but most pronounced in sleep stage 2. Strong correlations between DC and linear measures were established for the self-similarity exponent alpha and delta power, respectively. CONCLUSIONS: The dimensional complexity of the sleep EEG is influenced by both linear and nonlinear features. It cannot be directly interpreted as a nonlinear synchronization measure of brain activity, but yields valuable information when combined with the analysis of linear measures.