Spectral analysis of sleep EEG in patients with restless legs syndrome.

OBJECTIVE: Conventional analyses of sleep EEG recordings according to standard criteria indicate severe sleep disturbances in patients with restless legs syndrome (RLS). Spectral analysis of sleep EEG may be a sensitive tool to detect functional alterations of sleep mechanisms beyond the visual scoring of polysomnographic records. We analysed sleep EEG spectral power differences between RLS patients and healthy subjects. Furthermore, we studied the relationship of sleep EEG spectral power to the occurrence of periodic leg movements in sleep (PLMS) and arousal events. METHODS: Sleep EEGs from 20 patients with idiopathic RLS and of 20 age and sex matched healthy subjects were investigated. The spectral analysis was carried out on the same 30s epochs for which sleep stages had been determined. As a first step, whole-night spectral power excluding epochs with an arousal or with a PLMS was compared separately for REM and NREM sleep between RLS and healthy subjects. In a second step, we evaluated the spectral effects of PLMS, PLMS with associated arousals and isolated arousals relative to epochs of sleep without such events in both groups. In this part of the analysis, we only included the epochs of sleep stage 2 (the main and most stable non-REM sleep stage) and of REM sleep. RESULTS: Spectral power of all sleep epochs (excluding arousals and PLMS) did not differ between patients with RLS and healthy subjects. As expected, arousals and PLMS-associated arousals resulted in a significant increase in higher-frequency activity (alpha, beta1, beta2 and gamma bands) in both groups. Spectral power in epochs with PLMS alone did not significantly differ from spectral power in epochs without PLMS and without arousal in any of the groups. CONCLUSIONS: We found no evidence for an altered cortical activity in sleep stage 2 and REM sleep epochs in RLS patients compared to that in healthy controls if epochs with arousals were not considered. Furthermore, while PLMS associated with an arousal have a high impact on EEG spectra, the effect of a PLMS without arousal seems to be minor and transient. SIGNIFICANCE: Our data suggest that RLS related symptoms may intermittently disrupt sleep but do not appear to involve a persistent disturbance of the basic sleep generating patterns.     

Time-dependent variation in cerebral and autonomic activity during periodic leg movements in sleep: implications for arousal mechanisms.

OBJECTIVES: A hierarchy in arousal response has been proposed for spontaneous arousal by analyzing the temporal changes in heart rate (HR) and electroencephalographic (EEG) activity. To address the question as to whether the same continuum may be proposed in sleep disorders, we performed temporal spectral EEG and HR analyses during periodic leg movements (PLM) associated or not with microarousal (MA). METHODS: Data were obtained in 12 patients with restless leg syndrome and/or PLM syndrome. PLMs were classified into 3 types including PLM associated with MA, PLM without MA, and PLM associated with delta or K-complex bursts. HR and EEG spectral analyses were done for 10s before and 10s after the PLM onset. RESULTS: Each type of PLM was associated with a typical EEG and autonomic pattern consisting of an increase in HR and delta band activity before the PLM, regardless of the presence or absence of MA. Thereafter, a rise in delta, alpha and beta(2) activity was noted associated with tachycardia. This was greater when MA or bursts of slow wave activity were present. In the period following the PLM, HR, delta and alpha power showed a long-lasting decrease with values significantly below the baseline. CONCLUSIONS: From these data, we can conclude that: (1) cardiac and cerebral changes occur in association with PLM even when MA cannot be detected; (2) the combined increase in delta activity and HR before the onset of PLM suggests that these changes are part of the arousal response during PLM; (3) the graded arousal response during PLM confirms that the human arousal response involves a progression of central nervous system activation from brainstem to cortical level.     

Age and gender effects on heart rate activation associated with periodic leg movements in patients with restless legs syndrome.

OBJECTIVE: Periodic leg movements during sleep (PLMS) are often associated with electroencephalographic (EEG) changes, such as microarousals (MA), and with heart rate (HR) variations. The aim of the present study was to evaluate the effects of age and gender on HR changes associated with PLMS in restless legs syndrome (RLS) patients. METHODS: Forty-two RLS patients underwent one night of polysomnographic recordings. They were divided into 3 groups of 14 subjects (7 women and 7 men) according to age, i.e. young (25-40 years), middle-aged (41-55 years) and elderly (56-71 years) patients. The RR interval was calculated for 5 intervals before and 15 intervals after the onset of 50 PLMS in each patient. RESULTS: PLMS were associated with HR changes characterized by a tachycardia followed by a bradycardia. However, a reduction in the tachycardia and the bradycardia was observed with age. Moreover, women showed a higher amplitude in the bradycardia than men. No age or gender difference was found for MA index and duration. CONCLUSIONS: This study showed age and gender differences in the magnitude of the HR changes associated with PLMS. The knowledge of HR variations during sleep, including rapid HR changes associated with sleep events such as PLMS or MA, may be helpful in understanding the potential mechanisms involved in the increased cardiac risk observed in elderly.     

Cardiovascular variability during periodic leg movements: a spectral analysis approach.

OBJECTIVE: Changes in cardiovascular measures have been advocated as sensitive markers of phasic events arising from sleep. The current study was aimed to analyse the effects of periodic leg movements (PLMS) on heart rate variability (HRV) during NREM sleep in patients having restless legs syndrome and periodic leg movements during sleep. METHODS: The absolute and normalized high- and low-frequency peaks from spectral analysis (FFT) of R-R intervals, the HRV changes using wavelet transform, the geometric and time domain HRV were measured in 14 patients with restless legs syndrome and PLMS. The analysis was done comparing one hundred, 10 min periods with PLMS (PLMS+) and 60 periods without PLMS (PLMS-) in stage 2 of NREM sleep. In 8 patients analysis was also done in slow wave sleep (SWS). RESULTS: Occurrence of PLMS induced changes in geometrical indices of HRV, with a rise of the triangular index and the triangular interpolation of R-R intervals in PLMS+ periods (P < 0.0001). Small changes in time domain indices were found during PLMS+ periods, while the SD of the R-R intervals (SDNN), reflecting global HRV, was significantly higher (P = 0.001). While the low frequency (LF) power significantly increased in PLMS+ periods (P < 0.0001), high frequency (HF) power showed a weak and not significant reduction. The rise in sympathetic activity as detected by frequency domain HRV analysis was related to density and interval of PLMS. Comparison between sleep stages of NREM sleep demonstrated lower values of HRV measures when PLMS+ periods occur in SWS. CONCLUSIONS: Overall, PLMS occurrence was associated with a shift to increased sympathetic activity without significant changes in cardiac parasympathetic activity. The frequency domain analysis of HRV appears to be an easy tool to estimate the autonomic changes related to PLMS and PLMS- arousals and to differentiate their occurrence during stage 2 and deep sleep. SIGNIFICANCE: Spectral HRV measures may offer a simple approach to estimate the degree of autonomic changes occurring in relation to periodic leg movements in restless legs patients.     

Are periodic leg movements during sleep (PLMS) responsible for sleep disruption in insomnia patients?

On the basis of polygraphic findings, it has been suggested that periodic leg movements during sleep are not responsible for sleep impairment (Lugaresi et al., 1972). However, for some authors it is an important cause of insomnia (Guilleminault et al., 1975; Coleman, 1982). Thus, the relationship between periodic leg movements during sleep, sleep disruption and the complaint of patients is particularly complex. We investigated the macro- and micro-structure of sleep with and without leg movements in 10 PLMS patients complaining of insomnia to clarify whether periodic leg movements are responsible for sleep disruption. The total sleep time without periodic leg movements was significantly longer than sleep time with leg movements. Sleep time without leg movements was longer than sleep time with leg movements in stage 2, slow wave sleep (SWS) and rapid eye movement (REM) sleep. Short lasting awakenings were significantly more frequent during periodic leg movements associated sleep whilst long lasting awakenings were equally frequent during sleep with and without periodic leg movements. The percentage of the four electroencephalogram (EEG) activities (delta, theta, alpha and spindles) did not show any significant difference between periodic leg movements associated and not associated with sleep stages and total sleep time. The lack of significant differences in both the macro- and micro-structure of sleep and EEG activity content regarding the association with movements confirm the hypothesis that periodic leg movements did not primarily cause sleep disturbance.     

Sleep and wakefulness in somnambulism: a spectral analysis study

OBJECTIVE: The sleep structure and the dynamics of EEG slow-wave activity (SWA) were investigated in 12 young adults and age- and gender-matched controls. METHODS: Polysomnography was performed in subjects with well-documented chronic sleepwalking and in matched controls. Blinded visual scoring was performed using the international criteria from the Rechtschaffen and Kales atlas [A manual of standardized technology, techniques and scoring systems for sleep stages of human subjects. Los Angeles: UCLA Brain Information Service, Brain Research Institute, 1968.] and by determining the presence of microarousals as defined in the American Sleep Disorders Association (ASDA) atlas [Sleep 15 (1992) 173.]. An evaluation of SWA overnight was performed on total nocturnal sleep to determine if a difference existed between groups of subjects, since sleepwalking usually originates with slow-wave sleep. Investigation of the delta power in successive nonoverlapping 4-second windows in the 32 seconds just prior to EMG activity associated with a confusional arousal was also conducted. One central EEG lead was used for all analyses. RESULTS: Somnambulistic individuals experienced more disturbed sleep than controls during the first NREM-REM sleep cycle. They had a higher number of ASDA arousals and presented lower peak of SWA during the first cycle that led to a lower SWA decline overnight. When the investigation focused on the short segment immediately preceding a confusional arousal, they presented an important increase in the relative power of low delta (0.75-2 Hz) just prior to the confusional arousal. CONCLUSION: Sleepwalkers undergo disturbed nocturnal sleep at the beginning of the night. The increased power of low delta just prior to the confusional arousal experienced may not be related to Stages 3-4 NREM sleep. We hypothesize that it may be translated as a cortical reaction to brain activation.     

Influence of sleep stage and wakefulness on spectral EEG activity and heart rate variations around periodic leg movements.

OBJECTIVE: Typical changes in spectral electroencephalographic (EEG) activity and heart rate (HR) have been described in periodic leg movements (PLM) associated with or without microarousals (MA). We aimed to determine the effects of sleep stage and wakefulness on these responses to ascertain whether a common pattern of EEG and HR activation takes place. METHODS: The time course of EEG spectral activity and HR variability associated with PLM was analysed in 13 patients during light NREM sleep, rapid-eye-movement (REM) sleep and wakefulness. The same analysis was also conducted for PLM without MA occurring in stage 2. RESULTS: A significant EEG and electrocardiogram (ECG) activation was found associated with PLM during sleep, but not during wakefulness. While in light NREM sleep, an increase in delta and theta bands was detected before the PLM onset, in REM sleep the EEG activation occurred simultaneously with the PLM onset. Moreover, during stage 1 and REM sleep, alpha and fast frequencies tended to remain sustained after the PLM onset. In contrast, during wakefulness, a small and not significant increase in cerebral activity was present, starting at the PLM onset and persisting in the post-movement period. A typical pattern of cardiac response was present during NREM and REM sleep, the autonomic activation being lesser and prolonged during wakefulness. CONCLUSIONS: We conclude that the EEG and HR responses to PLM differ between sleep stages and wakefulness with lesser changes found during wakefulness. SIGNIFICANCE: These findings suggest that specific sleep state-dependent mechanisms may underlie the occurrence of PLM.     

Periodic leg movements in REM sleep behavior disorder and related autonomic and EEG activation

OBJECTIVE: To assess the frequency of periodic leg movements (PLM) in idiopathic REM sleep behavior disorder (RBD) and to analyze their polysomnographic characteristics and associated autonomic and cortical activation. BACKGROUND: PLM during sleep (PLMS) and wakefulness (PLMW) are typical features of restless legs syndrome (RLS), but are also frequently observed in patients with RBD. METHODS: Forty patients with idiopathic RBD underwent one night of polysomnographic recording to assess PLMS frequency. PLM features, PLMS-related cardiac activation during stage 2 sleep, and EEG changes were analyzed in 15 of these patients with RBD. Results were compared with similar data obtained in 15 sex- and age-matched patients with primary RLS. RESULTS: Twenty-eight (70%) of 40 patients with RBD showed a PLMS index greater than 10. No between-group differences were found in sleep architecture or indexes of PLMW and PLMS during non-REM sleep, but a trend for a higher PLMS index during REM sleep was found in patients with RBD. PLM mean duration and interval in the two conditions were similar. A transient tachycardia followed by a bradycardia was observed in close association with every PLMS in both groups, but the amplitude of the cardiac activation was significantly reduced in patients with RBD. In addition, significantly fewer PLMS were associated with microarousal in this condition. CONCLUSIONS: Periodic leg movements are very common in idiopathic RBD, occurring in all stages of sleep, especially during REM sleep. In idiopathic RBD, the reduction of cardiac and EEG activation associated with PLMS suggests the presence of an impaired autonomic and cortical reactivity to internal stimuli.     

Impaired cortical and autonomic arousal during sleep in multiple system atrophy.

OBJECTIVE: Periodic limb movements during sleep (PLMS) in Restless Legs Syndrome (RLS) are associated with arousals and stereotyped EEG and heart rate (HR) changes. We investigated PLMS-related EEG and HR variations in multiple system atrophy (MSA) in order to detect possible abnormalities in cortical and autonomic arousal responses. METHODS: Ten patients with MSA were contrasted against ten patients with primary RLS. Cortical (EEG) and autonomic (HR) variations associated with PLMS during NREM sleep were analysed by means of Fast Fourier Transform and HR analysis. In addition, we analysed the cyclic alternating pattern (CAP) during sleep, CAP representing a measure of the spontaneous arousal oscillations during NREM sleep. RESULTS: PLMS in RLS were associated with tachycardia and spectral EEG variations, beginning about 2s before the onset of PLMS, and peaking 1-4s after. The HR and spectral EEG variations were strikingly reduced or absent in MSA. MSA patients also had significantly lower CAP rate compared to RLS patients. CONCLUSIONS: Blunted HR and EEG spectral changes adjacent to PLMS indicated impaired cortical and autonomic arousal responses during sleep in MSA patients. SIGNIFICANCE: PLMS, when present, may represent a useful means to study the arousal responses during sleep.     

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.     

Alpha activity and cardiac correlates: three types of relationships during nocturnal sleep.

OBJECTIVE: We examined simultaneously alpha activity and cardiac changes during nocturnal sleep, in order to differentiate non-rapid eye movement (NREM) sleep, REM sleep, and intra-sleep awakening. METHODS: Ten male subjects displaying occasionally spontaneous intra-sleep awakenings underwent EEG and cardiac recordings during one experimental night. The heart rate and heart rate variability were calculated over 5 min periods. Heart rate variability was estimated: (1) by the ratio of low frequency (LF) to high frequency (HF) power calculated from spectral analysis of R-R intervals; and (2) by the interbeat autocorrelation coefficient of R-R intervals (rRR). EEG spectral analysis was performed using a fast Fourier transform algorithm. RESULTS: Three types of relationships between alpha waves (8-13 Hz) and cardiac correlates could be distinguished. During NREM sleep, alpha activity and cardiac correlates showed opposite variations, with high levels of alpha power associated with decreased heart rate, rRR and LF/HF ratio, indicating low sympathetic activity. Conversely, during REM sleep, alpha activity was low whereas heart rate, rRR, and the LF/HF ratio peaked, indicating high sympathetic activity. During intra-sleep awakenings, alpha activity and cardiac correlates both increased. No difference in time-course between alpha 1 (8-10 Hz) and alpha 2 (10-13 Hz) activity could be shown. Alpha waves occurred in fronto-central areas during slow wave sleep (SWS), migrated to posterior areas during REM sleep, and were localized in occipital areas during intra-sleep awakenings. CONCLUSIONS: These results suggest that alpha waves are not simply a sign of arousal, as is commonly thought. Fronto-central alpha waves, associated with decreased heart rate, possibly reflect sleep-maintaining processes.     

Automatic real-time analysis of human sleep stages by an interval histogram method

A new interval histogram method for automatic, all-night sleep stage scoring, simulated on a digital computer, is described. The system consists of a 2-step analysis. The first step is recognition of elementary patterns in EEG, EOG and EMG, and the second step is the determination of sleep stages based on these parameters. Correlation of this method with power spectral analysis of the dominant EEG patterns during each sleep stage supported the reliability of the first step analysis. Overall agreement (89.1%) between the computer and human judges was only 3% less than the agreement (92.1%) among the scorers, indicating considerable reliability of the second step. The primary areas of disagreement that arose in the identification of sleep stages occurred with stages 1, 2 and REM. To improve scoring accuracy, the system may require epoch sequence information. The profile of the elementary parameters of the EEG signals clearly illustrated the cyclic nature of these activities throughout the night. The alpha and delta 2 waves clearly separated the awake state from sleep stages. Beta 2 can discriminate stages 1 and REM from stage 2, and the best indicator for distincting stage 1 from REM was muscle activity. Sigma and spindles were prominent during stage 2 sleep. Both delta 2 and high voltage delta waves distinguished stage 3 from stage 4. On the other hand, delta 1 was evenly distributed and seemed to be common to all sleep stages.     

Band-specific electroencephalogram and brain cooling abnormalities during NREM sleep in patients with winter depression.

BACKGROUND: We previously reported that delta wave activity and facial skin temperatures, an index of brain cooling activity, were both abnormal during sleep in patients with winter depression (SAD). Because other electroencephalographic (EEG) frequencies may also convey relevant thermal, homeostatic, and circadian information, we sought to spectrally analyze delta, theta, alpha, and sigma frequencies during sleep from 23 patients with SAD and 23 healthy control subjects. METHODS: We computed means for delta, theta, alpha, and sigma power during both NREM and REM sleep. We also generated 22 cross-correlation functions for each group by crossing facial and rectal temperature with each other, as well as with delta, theta, alpha, and sigma frequencies. RESULTS: We found that delta, theta, and alpha frequency activities were all increased during NREM, but not REM sleep, in patients with SAD. In addition, there were significant and abnormal cross-correlations between facial temperatures and delta and theta frequencies during NREM sleep in patients with SAD. CONCLUSIONS: Patients with winter depression exhibit correlated abnormalities of sleep homeostasis and brain cooling during NREM sleep. Their EEG profiles during NREM sleep resemble the EEG profiles of subjects who have been sleep deprived. Further studies of NREM sleep homeostasis in patients with SAD seem warranted.     

Design and validation of a computer-based sleep-scoring algorithm

A computer-based sleep scoring algorithm was devised for the real time scoring of sleep-wake state in Wistar rats. Electroencephalogram (EEG) amplitude (microV(rms)) was measured in the following frequency bands: delta (delta; 1.5-6 Hz), theta (Theta; 6-10 Hz), alpha (alpha; 10.5-15 Hz), beta (beta; 22-30 Hz), and gamma (gamma; 35-45 Hz). Electromyographic (EMG) signals (microV(rms)) were recorded from the levator auris longus (neck) muscle, as this yielded a significantly higher algorithm accuracy than the spinodeltoid (shoulder) or temporalis (head) muscle EMGs (ANOVA; P=0.009). Data were obtained using either tethers (n=10) or telemetry (n=4). We developed a simple three-step algorithm that categorizes behavioural state as wake, non-rapid eye movement (NREM) sleep, rapid eye movement (REM) sleep, based on thresholds set during a manually-scored 90-min preliminary recording. Behavioural state was assigned in 5-s epochs. EMG amplitude and ratios of EEG frequency band amplitudes were measured, and compared with empirical thresholds in each animal.STEP 1: EMG amplitude greater than threshold? Yes: "active" wake, no: sleep or "quiet" wake. STEP 2: EEG amplitude ratio (delta x alpha)/(beta x gamma) greater than threshold? Yes: NREM, no: REM or "quiet" wake. STEP 3: EEG amplitude ratio Theta(2)/(delta x alpha) greater than threshold? Yes: REM, no: "quiet" wake. The algorithm was validated with one, two and three steps. The overall accuracy in discriminating wake and sleep (NREM and REM combined) using step one alone was found to be 90.1%. Overall accuracy using the first two steps was found to be 87.5% in scoring wake, NREM and REM sleep. When all three steps were used, overall accuracy in scoring wake, NREM and REM sleep was determined to be 87.9%. All accuracies were derived from comparisons with unequivocally-scored epochs from four 90-min recordings as defined by an experienced human rater. The algorithms were as reliable as the agreement between three human scorers (88%).     

Quantitative analysis of sleep EEG microstructure in the time-frequency domain

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.     

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.     

Quantitative analysis of sleep EEG microstructure in the time–frequency domain

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.     

Cardiac activation during arousal in humans: further evidence for hierarchy in the arousal response.

OBJECTIVES: One major subject of discussion in sleep studies is whether bursts of K-complexes (K-bursts) and delta waves (D-bursts), expressions of a subcortical arousal, truly reflect an arousal response during sleep. To address this question we studied the changes in heart rate (HR) during spontaneous arousals in healthy subjects. METHODS: Twenty-seven healthy adults were examined. Arousals were graded in 4 levels, including the standard definition of a microarousal (MA), phases of transitory activation (PAT), D-bursts and K-bursts. HR was analyzed for 10 beats before and 20 beats during arousal. EEG spectral analysis was performed for all types of arousals, including in the analysis the 20 s period preceding the actual event. RESULTS: Each type of arousal was associated with HR changes consisting of a tachycardia followed by a bradycardia. Changes were more pronounced during MA and PAT. Detailed analysis of the HR response showed that HR always increased before MA and PAT onset, associated with a rise in delta, theta and fast EEG activities, and suggesting a cerebral activation. CONCLUSIONS: Our data suggest that such subcortical arousals represent a real arousal response inducing cardiac activation similar to that found during MA and PAT. During MA and PAT, a rise in HR appears before the onset of the actual arousal associated with an increase in EEG slow and fast activity. The link between EEG and HR variation during MA and PAT and the fluctuations in HR during subcortical arousal suggest a continuous spectrum in the arousal mechanisms, starting at the brainstem level and progressing to cortical areas.     

Propriospinal myoclonus: a motor phenomenon found in restless legs syndrome different from periodic limb movements during sleep.

Three patients presented with a 25-, 15-, and 5-year history of restless legs syndrome (RLS) and periodic limb movements during sleep (PLMS). For 1, 4, and 5 years, they reported additional involuntary trunk and limbs jerks preceding falling asleep and occasionally during intrasleep wakefulness. Videopolysomnography revealed jerks during relaxed wakefulness arising in axial muscles with a caudal and rostral propagation at a slow conduction velocity, characteristic of propriospinal myoclonus (PSM). Jerk-related EEG-EMG back-averaging did not disclose any preceding cortical potential. During relaxed wakefulness preceding falling asleep and during intrasleep wakefulness, PSM coexisted with motor restlessness and sensory discomfort in the limbs. PSM disappeared when spindles and K-complexes appeared on the EEG. At this time, typical PLMS appeared every 20 to 40 seconds, especially during light sleep stages. PLMS EMG activity was limited to leg, especially tibialis anterior muscles, and did not show propriospinal propagation. In one patient, alternating leg muscle activation was also present. Jerks with a PSM pattern represent another motor phenomenon associated with RLS and different from the more usual PLMS.     

Neurofeedback Treatment of Restless Legs Syndrome and Periodic Leg Movements in Sleep

Restless leg syndrome (RLS) and periodic limb movements in sleep (PLMS) are prevalent and chronic movement disorders that result in sleep deprivation and impaired quality of life. Although there is no single pathophysiological explanation, EEG studies commonly implicate alpha activity as being involved. This article presents the first case reports of the treatment of RLS and PLMS with neurofeedback (EEG biofeedback). The encouraging results warrant further controlled research.