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A statistical analysis of the separability of EEG A-phases, with respect to basal activity, is presented in this study. A-phases are short central events that build up the Cyclic Alternating Pattern (CAP) during sleep. The CAP is a brain phenomenon which is thought to be related to the construction, destruction and instability of sleep stages dynamics. From the EEG signals, segments obtained around the onset and offset of the A-phases were used to evaluate the separability between A-phases and basal sleep stage oscillations. In addition, a classifier was trained to separate the different A-phase types (A1, A2 and A3). Temporal, energy and complexity measures were used as descriptors for the classifier. The results show a percentage of separation between onset and preceding basal oscillations higher than 85 % for all A-phases types. For Offset separation from following baseline, the accuracy is higher than 80 % but specificity is around 75%. Concerning to A-phase type separation, A1-phase and A3-phase are well separated with accuracy higher than 80, while A1 and A2-phases show a separation lower than 50%. These results encourage the design of automatic classifiers for Onset detection and for separating among A-phases type A1 and A3. On the other hand, the A-phase Offsets present a smooth transition towards the basal sleep stage oscillations, and A2-phases are very similar to A1-phases, suggesting that a high uncertainty may exist during CAP annotation.

Insomnia is a condition that affects the nervous and muscular system. Thirty percent of the population between 18 and 60 years suffers from insomnia. The effects of this disorder involve problems such as poor school or job performance and traffic accidents. In addition, patients with insomnia present changes in the cardiac function during sleep. Furthermore, the structure of electroencephalographic A-phases, which builds up the Cyclic Alternating Pattern during sleep, is related to the insomnia events. Therefore, the relationship between these brain activations (A-phases) and the autonomic nervous system would be of interest, revealing the interplay of central and autonomic activity during insomnia. With this goal, a study of the relationship between A-phases and heart rate fluctuations is presented. Polysomnography recording of five healthy subjects, five sleep misperception patients and five patients with psychophysiological insomnia were used in the study. Detrended Fluctuation Analysis (DFA) was used in order to evaluate the heart rate dynamics and this was correlated with the number of A-phases. The results suggest that pathological patients present changes in the dynamics of the heart rate. This is reflected in the modification of A-phases dynamics, which seems to modify of heart rate dynamics.

Objective: The aim of this study is to provide an improved method for the automatic classification of the Cyclic Alternating Pattern (CAP) sleep by applying a segmentation technique to the computation of descriptors from the EEG., Methods: A dataset of 16 polysomnographic recordings from healthy subjects was employed, and the EEG traces underwent first an automatic isolation of NREM sleep portions by means of an Artificial Neural Network and then a segmentation process based on the Spectral Error Measure. The information content of the descriptors was evaluated by means of ROC curves and compared with that of descriptors obtained without the use of segmentation. Finally, the descriptors were used to train a discriminant function for the automatic classification of CAP phases A., Results: A significant improvement with respect to previous scoring methods in terms of both information content carried by the descriptors and accuracy of the classification was obtained., Conclusions: EEG segmentation proves to be a useful step in the computation of descriptors for CAP scoring., Significance: This study provides a complete method for CAP analysis, which is entirely automatic and allows the recognition of A phases with a high accuracy thanks to EEG segmentation., (Copyright © 2013 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.)

This work aims to investigate new markers for the quantitative characterization of insomnia, in the context of sleep microstructure, as expressed by cyclic alternating pattern (CAP) sleep. The study group includes 11 subjects with normal sleep and 10 subjects with diagnosed primary insomnia. Differences between normal sleepers and insomniacs are investigated, in terms of dynamics and content of CAP events. The overall rate of CAP and of different phases is considered. The dynamic in the structure and alternation of CAP events is further studied in different scales by use of wavelet analysis, and calculation of energy/entropy features. The content of CAP events is studied in terms of electroencephalography (EEG) complexity analysis for the different types of events. Statistically significant differences are highlighted, both in structure and content. Besides confirming the increase in CAP rate, main findings regarding the microstructure difference in insomnia include: 1) as regards the deep sleep building phases, more irregular activation-deactivation patterns, with bigger deactivation time, i.e., distance between consecutive activation events, and appearing with higher EEG complexity in deactivation, and 2) a bigger duration of desynchronisation phases, with increased EEG complexity and more irregular patterns. This analysis extends previous findings on the relation between CAPrate increase and sleep instability mechanisms, proposing specific features of CAP that seem to play a role in insomnia (as consistently presented via classification analysis). This opens new perspectives for the understanding of the role of CAP in the quantitative characterization of sleep and its disorders.

The aim of this study was to arrange an automatic quantitative measure of the electroencephalographic (EEG) signal amplitude variability during non-rapid eye movement (NREM) sleep, correlated with the visually extracted cyclic alternating pattern (CAP) parameters. Ninety-eight polysomnographic EEG recordings of normal controls were used. A new algorithm based on the analysis of the EEG amplitude variability during NREM sleep was designed and applied to all recordings, which were also scored visually for CAP. All measurements obtained with the new algorithm correlated positively with corresponding CAP parameters. In particular, total CAP time correlated with total NREM variability time (r = 0.596; P < 1E-07), light sleep CAP time with light sleep variability time (r = 0.597; P < 1E-07) and slow wave sleep CAP time with slow wave sleep variability time (r = 0.809; P < 1E-07). Only the duration of CAP A phases showed a low correlation with the duration of variability events. Finally, the age-related modifications of CAP time and of NREM variability time were found to be very similar. The new method for the automatic analysis of NREM sleep amplitude variability presented here correlates significantly with visual CAP parameters; its application requires a minimum work time, compared to CAP analysis, and might be used in large studies involving numerous recordings in which NREM sleep EEG amplitude variability needs to be assessed., (© 2011 European Sleep Research Society.)

Cyclic alternating pattern CAP is the EEG marker of unstable sleep, a concept which is poorly appreciated among the metrics of sleep physiology. Besides, duration, depth and continuity, sleep restorative properties depend on the capacity of the brain to create periods of sustained stable sleep. This issue is not confined only to the EEG activities but reverberates upon the ongoing autonomic activity and behavioral functions, which are mutually entrained in a synchronized oscillation. CAP can be identified both in adult and children sleep and therefore represents a sensitive tool for the investigation of sleep disorders across the lifespan. The present review illustrates the story of CAP in the last 25 years, the standardized scoring criteria, the basic physiological properties and how the dimension of sleep instability has provided new insight into pathophysiolology and management of sleep disorders., (Copyright © 2011 Elsevier Ltd. All rights reserved.)

This study analyzes the nonlinear properties of the EEG at transition points of the sequences that build the Cyclic Alternating Pattern (CAP). CAP is a sleep phenomenon built up by consecutive sequences of activations and non-activations observed during the sleep time. The sleep condition can be evaluated from the patterns formed by these sequences. Eleven recordings from healthy and good sleepers were included in this study. We investigated the complexity properties of the signal at the onset and offset of the activations. The results show that EEG signals present significant differences (p&#60;0.05) between activations and non-activations in the Sample Entropy and Tsallis Entropy indices. These indices could be useful in the development of automatic methods for detecting the onset and offset of the activations, leading to significant savings of the physician's time by simplifying the manual inspection task.

Objective: This study aims to identify, starting from a single EEG trace, quantitative distinctive features characterizing the A phases of the Cyclic Alternating Pattern (CAP)., Methods: The C3-A2 or C4-A1 EEG leads of the night recording of eight healthy adult subjects were used for this analysis. CAP was scored by an expert and the portions relative to NREM were selected. Nine descriptors were computed: band descriptors (low delta, high delta, theta, alpha, sigma and beta); Hjorth activity in the low delta and high delta bands; differential variance of the EEG signal. The information content of each descriptor in recognizing the A phases was evaluated through the computation of the ROC curves and the statistics sensitivity, specificity and accuracy., Results: The ROC curves show that all the descriptors have a certain significance in characterizing A phases. The average accuracy obtained by thresholding the descriptors ranges from 59.89 (sigma descriptor) to 72.44 (differential EEG variance)., Conclusions: The results show that it is possible to attribute a significant quantitative value to the information content of the descriptors., Significance: This study gives a mathematical confirm to the features of CAP generally described qualitatively, and puts the bases for the creation of automatic detection methods., (Copyright © 2011 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.)

The aim of this study is to implement a high-accuracy automatic detector of the Cyclic Alternating Pattern (CAP) during sleep. EEG data from four healthy subjects were used. Both the C4-A1 and the F4-C4 leads were analyzed for this study. Seven features were extracted from each of the two leads and two separate studies were performed for each set of descriptors. For both sets, a Support Vector Machine was trained and tested on the data with the Leave One Out cross-validation method. The two final classifications obtained on the two sets were merged, by considering a CAP A phase scored only if it had been recognized both on the central and on the frontal lead. The length of the A phase was then determined by the result on the fronto-central lead. This method leads to encouraging results, with a classification sensitivity on the whole dataset equal to 73.82%, specificity equal to 85.93%, accuracy equal to 84,05% and Cohen's kappa equal to 0.50.

This study aimed to develop an automatic algorithm to detect the activation phases (A phases) of the Cyclic Alternating Pattern. The sleep EEG microstructure of 4 adult, healthy subjects was scored by a sleep medicine expert. Features were calculated from each of the six EEG bands (low delta, high delta, theta, alpha, sigma and beta), and three additional characteristics were computed: the Hjorth activity in the low delta and high delta bands, and the differential variance of the raw EEG signal. The correlation between couples of features was analyzed to find redundancies for the automatic analysis. The features were used to train an Artificial Neural Network to automatically find the A phases of CAP. The data were divided into training, validation and testing set, and the visual scoring provided by the clinician was used as the desired output. The statistics on the second by second classification show an average sensitivity equal to 76%, specificity equal to 83% and accuracy equal to 82%. The results obtained are encouraging, since an automatic classification of the A phases could benefit the practice in clinics, preventing the physician from the time-consuming activity of visually scoring the sleep microstructure over the whole eight-hour sleep recordings. Moreover, it would provide an objective criterion capable of overcoming the problems of inter-scorer variability.

Objective: To analyze the functional connectivity patterns of the different EEG bands during wakefulness and sleep (different sleep stages and cyclic alternating pattern (CAP) conditions), using concepts derived from Graph Theory., Methods: We evaluated spatial patterns of EEG band synchronization between all possible pairs of electrodes (19) placed over the scalp of 10 sleeping healthy young normal subjects using two graph theoretical measures: the clustering coefficient (Cp) and the characteristic path length (Lp). The measures were obtained during wakefulness and the different sleep stages/CAP conditions from the real EEG connectivity networks and randomized control (surrogate) networks (Cp-s and Lp-s)., Results: We found values of Cp and Lp compatible with a small-world network organization in all sleep stages and for all EEG bands. All bands below 15Hz showed an increase of these features during sleep (and during CAP-A phases in particular), compared to wakefulness., Conclusions: The results of this study seem to confirm our initial hypothesis that during sleep there exists a clear trend for the functional connectivity of the EEG to move forward to an organization more similar to that of a small-world network, at least for the frequency bands lower than 15Hz., Significance: Sleep network "reconfiguration" might be one of the key mechanisms for the understanding of the "global" and "local" neural plasticity taking place during sleep.

Objective: To analyze the functional connectivity patterns of the EEG slow-wave activity during the different sleep stages and Cyclic Alternating Pattern (CAP) conditions, using concepts derived from Graph Theory., Methods: We evaluated spatial patterns of EEG slow-wave synchronization between all possible pairs of electrodes (19) placed over the scalp of 10 sleeping healthy young normal subjects using two graph theoretical measures: the clustering coefficient (Cp) and the characteristic path length (Lp). The measures were obtained during the different sleep stages and CAP conditions from the real EEG connectivity networks and randomized control (surrogate) networks (Cp-s and Lp-s)., Results: Cp and Cp/Cp-s increased significantly from wakefulness to sleep while Lp and Lp/Lp-s did not show changes. Cp/Cp-s was higher for A1 phases, compared to B phases of CAP., Conclusions: The network organization of the EEG slow-wave synchronization during sleep shows features characteristic of small-world networks (high Cp combined with low Lp); this type of organization is slightly but significantly more evident during the CAP A1 subtypes., Significance: Our results show feasibility of using graph theoretical measures to characterize the complexity of brain networks during sleep and might indicate sleep, and the A1 phases of CAP in particular, as a period during which slow-wave synchronization shows optimal network organization for information processing.

Arousal systems play a topical neurophysiologic role in protecting and tailoring sleep duration and depth. When they appear in NREM sleep, arousal responses are not limited to a single EEG pattern but are part of a continuous spectrum of EEG modifications ranging from high-voltage slow rhythms to low amplitude fast activities. The hierarchic features of arousal responses are reflected in the phase A subtypes of CAP (cyclic alternating pattern) including both slow arousals (dominated by the <1Hz oscillation) and fast arousals (ASDA arousals). CAP is an infraslow oscillation with a periodicity of 20-40s that participates in the dynamic organization of sleep and in the activation of motor events. Physiologic, paraphysiologic and pathologic motor activities during NREM sleep are always associated with a stereotyped arousal pattern characterized by an initial increase in EEG delta power and heart rate, followed by a progressive activation of faster EEG frequencies. These findings suggest that motor patterns are already written in the brain codes (central pattern generators) embraced with an automatic sequence of EEG-vegetative events, but require a certain degree of activation (arousal) to become visibly apparent. Arousal can appear either spontaneously or be elicited by internal (epileptic burst) or external (noise, respiratory disturbance) stimuli. Whether the outcome is a physiologic movement, a muscle jerk or a major epileptic attack will depend on a number of ongoing factors (sleep stage, delta power, neuro-motor network) but all events share the common trait of arousal-activated phenomena.

Objective: To analyze in detail the frequency content of the different EEG components of the Cyclic Alternating Pattern (CAP), taking into account the ongoing EEG background and the nonCAP (NCAP) periods in the whole night polysomnographic recordings of normal young adults., Methods: Sixteen normal healthy subjects were included in this study. Each subject underwent one polysomnographic night recording; sleep stages were scored following standard criteria. Subsequently, each CAP A phase was detected in all recordings, during NREM sleep, and classified into 3 subtypes (A1, A2, and A3). The same channel used for the detection of CAP A phases (C3/A2 or C4/A1) was subdivided into 2-s mini-epochs. For each mini-epoch, the corresponding CAP condition was determined and power spectra calculated in the frequency range 0.5-25 Hz. Average spectra were obtained for each CAP condition, separately in sleep stage 2 and SWS, for each subject. Finally, the first 6h of sleep were subdivided into 4 periods of 90 min each and the same spectral analysis was performed for each period., Results: During sleep stage 2, CAP A subtypes differed from NCAP periods for all frequency bins between 0.5 and 25 Hz; this difference was most evident for the lowest frequencies. The B phase following A1 subtypes had a power spectrum significantly higher than that of NCAP, for frequencies between 1 and 11 Hz. The B phase after A2 only differed from NCAP for a small but significant reduction in the sigma band power; this was evident also after A3 subtypes. During SWS, we found similar results. The comparison between the different CAP subtypes also disclosed significant differences related to the stage in which they occurred. Finally, a significant effect of the different sleep periods was found on the different CAP subtypes during sleep stage 2 and on NCAP in both sleep stage 2 and SWS., Conclusions: CAP subtypes are characterized by clearly different spectra and also the same subtype shows a different power spectrum, during sleep stage 2 or SWS. This finding underlines a probable different functional meaning of the same CAP subtype during different sleep stages. We also found 3 clear peaks of difference between CAP subtypes and NCAP in the delta, alpha, and beta frequency ranges which might indicate the presence of 3 frequency components characterizing CAP subtypes, in different proportion in each of them. The B component of CAP differs from NCAP because of a decrease in power in the sigma frequency range., Significance: This study shows that A components of CAP might correspond to periods in which the very-slow delta activity of sleep groups a range of different EEG activities, including the sigma and beta bands, while the B phase of CAP might correspond to a period in which this activity is quiescent or inhibited.

Objective: To assess inter-rater reliability between different scorers, from different qualified sleep research groups, in scoring visually the Cyclic Alternating Pattern (CAP), to evaluate the performances of a new tool for the computer-assisted detection of CAP, and to compare its output with the data from the different scorers., Methods: CAP was scored in 11 normal sleep recordings by four different raters, coming from three sleep laboratories. CAP was also scored in the same recordings by means of a new computer-assisted method, implemented in the Hypnolab 1.2 (SWS Soft, Italy) software. Data analysis was performed according to the following steps: (a) the inter-rater reliability of CAP parameters between the four different scorers was carried out by means of the Kendall W coefficient of concordance; (b) the analysis of the agreement between the results of the visual and computer-assisted analysis of CAP parameters was also carried out by means of the Kendall W coefficient; (c) a 'consensus' scoring was obtained, for each recording, from the four scorings provided by the different raters, based on the score of the majority of scorers; (d) the degree of agreement between each scorer and the consensus score and between the computer-assisted analysis and the consensus score was quantified by means of the Cohen's k coefficient; (e) the differences between the number of false positive and false negative detections obtained in the visual and in the computer-assisted analysis were also evaluated by means of the non-parametric Wilcoxon test., Results: The inter-rater reliability of CAP parameters quantified by the Kendall W coefficient of concordance between the four different scorers was high for all the parameters considered and showed values above 0.9 for total CAP time, CAP time in sleep stage 2 and percentage of A phases in sequence; also CAP rate showed a high value (0.829). The most important global parameters of CAP, including total CAP rate and CAP time, scored by the computer-assisted analysis showed a significant concordance with those obtained by the raters. The agreement between the computer-assisted analysis and the consensus scoring for the assignment of the CAP A phase subtype was not distinguishable from that expected from a human scorer. However, the computer-assisted analysis provided a number of false positives and false negatives significantly higher than that of the visual scoring of CAP., Conclusions: CAP scoring shows good inter-rater reliability and might be compared in different laboratories the results of which might also be pooled together; however, caution should always be taken because of the variability which can be expected in the classical sleep staging. The computer-assisted detection of CAP can be used with some supervision and correction in large studies when only general parameters such as CAP rate are considered; more editing is necessary for the correct use of the other results., Significance: This article describes the first attempt in the literature to evaluate in a detailed way the inter-rater reliability in scoring CAP parameters of normal sleep and the performances of a human-supervised computerized automatic detection system.

Objective: To define a unified method for the automatic recognition and quantitative description of EEG phasic events of sleep microstructure occurring during NREM sleep, particularly arousals, phase A subtypes of cyclic alternating pattern and spindles., Methods: The NREM sleep EEG of 10 normal young subjects was examined in order to recognize formal phasic events of sleep microstructure. The following 'formal' events (i.e. events defined exclusively on the basis of automatic analysis criteria) were classified: arousals, A1-phases (A-phases not including arousals) and A2- and A3-phases (A-phases including arousals). Spindle bursts, corresponding to visually recognized spindles, were also formally defined. The identification of these events was carried out following a three-step procedure: (1) computation of band-related descriptors derived from the EEG signal, (2) introduction of suitable thresholds and (3) application of simple logical principles, i.e. an exclusion principle and an overlapping principle., Results: Formal A-phases, arousals and spindle bursts showed spectral characteristics which were consistent with visual inspection. The value of the parameter Correctness for the recognition of the A-phases was 83.5%. In particular, the different physiological distribution of the A-phases in Stage 2 preceding slow wave sleep with respect to Stage 2 preceding REM sleep was confirmed., Conclusions: The proposed method provides a unified quantitative approach to the study of sleep microstructure. Visually defined events can be reliably identified by means of automatic recognition.

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.

Objective: Polysomnographic (PSG) measures consistently reflect poor sleep quality and effective treatment in insomniac patients., Methods: The PSG findings of 47 patients (18 M and 29 F, 42.5+/-10 years) meeting the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition criteria for a diagnosis of primary insomnia were compared with those of 25 age- and gender-balanced healthy subjects (controls) without sleep complaints. After one adaptation night to the sleep lab, each patient underwent two randomized double-blind PSG recordings. Twenty-four patients followed a placebo-drug sequence and 23 a drug-placebo succession. Active treatment consisted of widely used hypnotic drugs, i.e. zolpidem, triazolam, zopiclone, brotizolam. Conventional PSG measures, electroencephalogram (EEG) arousals and CAP variables (including phase A subtypes) were quantified and statistically analyzed., Results: Compared to controls, insomniac patients under placebo showed a significant increase of CAP rate, subtypes A1 and A2, EEG arousals, nocturnal wakefulness and stage 1, associated with reduced values of total sleep time and slow wave sleep (stages 3 and 4). In insomniac patients, sleep quality was significantly improved by hypnotic treatment. Compared to placebo, active medication significantly reduced CAP rate, subtypes A1 and A2, but had only marginal effects on subtypes A3 and on EEG arousals. Under hypnotic treatment total sleep time, nocturnal awakenings, stage 1 and slow wave sleep recuperated normal values. The most significant correlation between sleep quality and PSG variables was found for CAP rate (P<0.0001)., Conclusions: PSG investigation extended to CAP variables and EEG arousals can be an important procedure for the diagnosis of primary insomnia and evaluation of treatment efficacy.

The objective of this work was to study the non-linear aspects of sleep EEG, taking into account the different sleep stages and the peculiar organization of its phasic events in ordered sequences (CAP) by applying a series of new non-linear measures (non-linear cross prediction or NLCP), which appear more reliable for the detection and characterization of non-linear structures in experimental data than the commonly used correlation dimension. Eight healthy subjects aged 18-20 years participated in this study. Polysomnography was performed in all subjects; signals were sampled at 128 Hz and stored on hard disk. The C3 or C4 derivation was used for all the subsequent computational steps, which were performed on EEG epochs (4096 data points) selected from sleep stage 2 (S2) and slow-wave sleep (SWS), in both CAP and non-CAP (NCAP) conditions. Also, epochs from sleep stage 1 (S1), REM and wakefulness preceding sleep were recorded. The dynamic properties of the EEG were assessed by means of the non-linear cross-prediction test, which uses three different 'model' time series in order to predict non-linearly the original data set (Pred, Ama, and Tir). Pred is a measure of the predictability of the time series, and Ama and Tir are measures of asymmetry, indicating non-linear structure. The non-linear measures applied in this study indicate that sleep EEG tends to show non-linear structure only during CAP periods, both during S2 and SWS. Moreover, during CAP periods, non-linearity can only be detected during the phase A1 subtypes (and partially A2) of CAP. The A3 phases show characteristics of non-stationarity and bear some resemblance to wakefulness. Based on the results of this study, sleep might be considered as a dynamically evolving sequence of different states of the EEG, which we could track by detecting non-linearity, mostly in association with CAP. Our results clearly show that detectable non-linearity in the EEG is closely related to the occurrence of the phase A of CAP.

Study Objectives: Besides arousals (according to the ASDA definition), sleep contains also K-complexes and delta bursts which, in spite of their sleep-like features, are endowed with activating effects on autonomic functions. The link between phasic delta activities and enhancement of vegetative functions indicates the possibility of physiological activation without sleep disruption (i.e., arousal without awakening). A functional connection seems to include slow (K-complexes and delta bursts) and rapid (arousals) EEG events within the comprehensive term of activating complexes. CAP (cyclic alternating pattern) is the spontaneous EEG rhythm that ties both slow and rapid activating complexes together during NREM sleep. The present study aims at exploring the relationship between arousals and CAP components in a selected sample of healthy sleepers., Design: Polysomnographic analysis according to the scoring rules for sleep stages and arousals. CAP analysis included also tabulation of subtypes A1 (slow EEG activating complexes), A2 and A3 (activating complexes with fast EEG components)., Setting: 40 sleep-lab accomplished recordings., Participants: Healthy subjects belonging to a wide age range (38 +/- 20 yrs.)., Interventions: N/A., Measurement and Results: Of all the arousals occurring in NREM sleep, 87% were inserted within CAP. Subtypes A2 and A3 of CAP corresponded strikingly with arousals (r=0.843; p<0.0001), while no statistical relationship emerged when arousals were matched with subtypes A1 of CAP. Subtypes A1 instead correlated positively with the percentages of deep sleep (r=0.366; p<0.02)., Conclusions: The CAP subtype classification encompasses both the process of sleep maintenance (subtypes A1) and sleep fragmentation (subtypes A2 and A3), and provides a periodicity dimension to the activating events of NREM sleep.

During non-REM (NREM) sleep it is possible to identify two complementary conditions of arousal stability and arousal instability. Unstable sleep, which can be detected in all stages, is expressed by the recurrence of arousal complexes (sequences of K-complexes, delta bursts, K-alpha, conventional arousals), which translate a brief (10-15 s) activation of the sleeping brain. These repetitive arousal complexes compose the cyclic alternating pattern (CAP). During stable NREM sleep, arousal complexes are rare or absent and the EEG lacks any cyclic pattern (non-CAP). CAP is a spontaneous feature of normal sleep as it is typically involved in stage changes and nocturnal motor activity. The physiological amount of CAP rate (ratio of CAP time to NREM sleep time) varies with age according to a U-shape curve. Within these ranges, sleep is perceived as continuous and restorative. Conversely, an excess of CAP rate fragments sleep and impairs its quality. Polysomnographic investigation reveals that untreated insomniac patients exhibit significantly higher values of CAP rate compared to healthy sleepers. Effective hypnotic treatment restores physiological amounts of CAP rate, with specific differences between the administered drugs. The sensitivity of CAP parameters to drug manipulation can provide circumscribed information on the hypnotic properties of active compounds.

Purpose: to analyze the activating role of cyclic alternating pattern (CAP) and EEG synchrony on generalized interictal paroxysms in the first part of the night, when all sleep patterns are represented., Methods: nocturnal polysomnographic investigation was accomplished on a randomized series of 18 subjects with an active form of primary generalized epilepsy (PGE), but only six patients showed a complete and regular profile of the first two sleep cycles (SCs). Completeness and regularity of the selected SCs consisted in the absence of intervening wakefulness, in the presence of all sleep stages, and in the identification of three main units, (a) a descending branch, dominated by the build-up of EEG synchrony in the transition from light to deep non-rapid eye movement (NREM) sleep; (b) a trough, where the magnitude of EEG synchrony is greatest and gives rise to stages 3 and 4; (c) an ascending branch characterized by a decrease of EEG synchrony preceding the onset of rapid eye movement (REM) sleep. Generalized paroxysms were evaluated in terms of discharge rates (number of interictal bursts per minute of sleep) and distribution within the investigated sleep parameters., Results: the discharge rates decreased from SC1 to SC2, with higher values quantified during NREM sleep (mean, 2.8) compared with REM sleep (mean, 0.8). Both SCs showed a progressive decrease of activation across the three units, from the highest discharge rates reached during the descending branches (mean, 3.6) to the more attenuated discharge rates during the troughs (mean, 2.4) down to the lowest rates during the ascending limbs (mean, 1.1). The magnitude of activation during the descending branches was closely related to the CAP condition (mean, 5.2) and to the powerful effect of phase A (mean, 13.9). The great majority (82%) of EEG discharges occurring in phase A were distributed within the A1 subtypes (identified by sequences of k-complexes or delta bursts)., Conclusions: within the first two SCs, the features of NREM sleep endowed with the major activating power on generalized bursts are represented by the rise of EEG synchrony (descending branch) and by the A phases of CAP involved in the regulation of its build-up.

Objectives: The present study aimed at offering a standardized database for cyclic alternating pattern (CAP) parameters across representative ages of life., Methods: CAP parameters were quantified in 40 healthy sleepers and polygraphically investigated in a partially sound-proof recording chamber under a standard laboratory setting. Four age groups were investigated (teenagers: 10-19 years; young adults: 20-39 years; middle-aged: 40-59 years; elderly: 60 years). Each group included 10 subjects (5 males and 5 females). Nocturnal recordings were accomplished after adaptation to the sleep laboratory that also served to rule out the presence of sleep-related disorders. The study indicated that CAP is a natural phenomenon of NREM sleep, with specific age-related characteristics across the life cycle., Results: CAP rate in NREM sleep, defined as the percentage ratio of total CAP time to total NREM sleep time, showed a U-shape profile with minimum in young adults (31.9%), maximum in the elderly group (55.3%), and intermediate values in teenagers (43.4%) and in middle-aged subjects (37.5%). The longest duration of CAP cycles was found among the older subjects (31 s). The highest amounts of subtypes A1 were identified in teenagers (n = 261), while the highest amounts of A2 and A3 subtypes occurred in the elderly group (n = 183). Across the ages, the level of arousal mostly fluctuated in stages 1 and 3, whereas stage 4 emerged as the most stable NREM stage. Overall, stage 2 better reflected the CAP values referred to as total NREM sleep., Conclusions: The periodic arousal fluctuations reflected by CAP are a natural phenomenon of NREM sleep with specific age-related variations across the life cycle.

Ten healthy volunteers (six men and four women) aged between 20 and 30 years underwent a nocturnal polygraphic recording on paper and on tape. Spectral analysis was accomplished and EEG sleep scored according to standard criteria and to the guidelines for the identification of cyclic alternating pattern (CAP). The initial 25 min of sleep, starting from the first clear-cut k-complex and ending within stage 4, was subdivided into five consecutive blocks of 5 min each. Using a zero-crossing technique, we evaluated the number of total power oscillations in each block and we tested the hypothesis of significant modifications of the number of total power oscillations and of their periodicity in the successive sessions. In addition, we measured the gap between the maximum and minimum values, respectively, of two successive half-waves making up each oscillation. The hypothesis of time-related trends of the values of the gaps was tested by means of linear regression techniques. Within the 25-min time span, the number of periodic oscillations and the number of CAP cycles showed significant increases. The amplitude gaps underwent a decreasing trend. The present data suggest that slow rhythmic oscillations expressed by CAP can be detected by means of spectral analysis. Their dynamics suggests a close relationship with the EEG synchronization processes.

Although EEG is generally considered a useful tool for the diagnosis of Creutzfeldt-Jakob disease (CJD), some cases have been reported where the EEG was non-specific. We reviewed a series of 15 CJD patients, observed in our institute in the period 1975-91. In 12 cases the diagnosis was confirmed on post-mortem examination. The prominent aspect of the present series was the homogeneity of clinical, neurophysiological and neuropathological data. All patients showed the presence of periodic sharp wave complexes (PSWC) and EEG reactivity to external stimuli or drugs was uniform. The EEG can give essential information for the diagnosis of CJD if 2 basic conditions are satisfied: (1) serial recordings are performed in relation to the different stages of the disease, and (2) not only the presence of PSWC is considered, but also the reactivity of EEG to dynamic events such as the response to external stimuli and drugs, and the level of consciousness.

Hjorth's descriptors (NSD) (activity, mobility, and complexity) provide a useful tool for evaluating micro- and macrostructural elements of sleep. In rats, the automatic analysis of sleep recordings by means of NSD calculated from SM and Vis derivations allow different sleep stages to be discriminated. Activity distribution over the total recording permits the definition of a global effect on the EEG. Moreover, the quality of sleep can be evaluated by the variations of the SM activity distribution by considering different classes of amplitude. Index and rate of unstable amplitude segments (UAS) constitute useful parameters to analyse the stability and quality of sleep with different models of insomnia and after pharmacological treatment. The UAS in rats can be compared to CAPs in humans. The NSD are also able to define quality and stability of human sleep. EEG analysis using NSD provides a novel perspective for the analysis of the stability and quality of rat and human sleep. This microstructural analysis of sleep appears to be a useful tool for pharmaco-EEG studies.

Clinical and EEG manifestations of juvenile myoclonic epilepsy (JME) occur in a strict relationship to the sleep-wake cycle, particularly to transition phases (awakening, falling asleep, afternoon relaxation after work). JME manifestations are deactivated during sleep. Because arousal fluctuations during NREM sleep may be controlled by the same neurophysiologic mechanisms regulating awakening, we studied the relationship between the cyclic alternating pattern (CAP) and JME manifestations. All-night polysomnographic recordings of 10 JME patients were analyzed for variations of epileptiform EEG abnormalities in relation to sleep stages and to different microstructural variables (NCAP, CAP, phases A and B). CAP rates (ratio between total CAP duration and total NREM sleep duration) were also calculated. Average CAP rate was 46.70%, significantly higher than that (23%) of an age-matched control group. Macrostructural analysis showed only a trend toward a slight predominance of EEG epileptiform activity during slow wave sleep but no significant correlation between spiking rates and sleep stages. Microstructural analysis confirmed the CAP modulation of EEG epileptiform activity, with maximum appearance of epileptiform abnormalities during phase A CAP (normalized spiking rate = 4.00 +/- 0.98) and strong inhibition during phase B (0.06 +/- 00.6). Intermediate values were noted during NCAP (0.54 +/- 0.27). No correlation was noted between spiking rates during NREM sleep and CAP rates, possibly indicating that in JME patients the increased CAP rate may be partially independent of epileptiform EEG activity. Our data suggest that in JME patients CAP may be a neurophysiologic oscillator organizing expression of the epileptiform discharges independent of the tendency of the individual patient to produce epileptiform EEG discharges.

Generalized interictal EEG discharges are influenced by a biphasic (phase A and B) modality of arousal control during non-rapid eye movement (REM) sleep termed cyclic alternating pattern (CAP). Each phase A and the following phase B compose a CAP cycle. The percentage ratio of total CAP time to total non-REM sleep time is the CAP rate, a sleep parameter that measures the instability and fragmentation of sleep. Since CAP exerts a powerful influence on generalized interictal EEG discharges during sleep, the polysomnograms of seven epileptic patients affected by a clinically active form of primary generalized epilepsy were matched with those of seven healthy volunteers of the same age and sex to assess the influence of interictal discharges on sleep organization. No remarkable differences emerged when the traditional polysomnographic parameters were compared between the two groups. However, the epileptic patients showed significantly higher CAP rate values (52.7 vs. 34.6%; p less than 0.003), indicating a greater arousal instability in the sleep records of these subjects. Within the epileptic group, the CAP cycles that included at least one interictal paroxysm were significantly longer than those without EEG discharges (31.2 vs. 25.4 s; p less than 0.007). The selective lengthening of CAP cycles is likely due to an exaggeration of the natural activating power of phase A when coupled with EEG paroxysms and an intensification of the inhibitory properties of the following phase B. The dynamic relationships and differences between spindles in animals, k-complexes, and slow-wave bursts in humans may have a functional linkage with epileptic phenomena during sleep.

In nonrapid eye movement (NREM) sleep, electroencephalographic cyclic alternating patterns (CAPS) express the organized complexity of arousal-related phasic events. As a translation of sustained instability of the arousal level, CAPS increase under perturbation and decrease under sleep-promoting conditions. After adaptation to the sleep lab, 18 subjects (12 with persistent psychophysiological insomnia and 6 without sleep complaints), all aged 40-60 years, underwent a random sequence of two nonconsecutive nocturnal recordings, one under placebo and one under an imidazopyridine hypnotic agent (zolpidem). The choice of this drug was oriented by its capability to warrant, at the selected dose of 10 mg, a physiological profile of sleep. Polysomnographic parameters and self-report of sleep quality by means of visual analogue scale (VAS) were assessed by analysis of variance followed by a Scheffé test when F statistics were significant. The placebo nights of controls showed a significantly longer duration of total sleep time (+60 minutes) compared with the placebo nights of insomniacs. However, no relevant differences emerged from the other traditional polysomnographic variables. Conversely, CAP rate (CAP time x 100/NREM sleep time) showed highly sensitive and consistent modifications. Under placebo, CAP rate was significantly higher among insomniacs compared with controls (62.2% vs. 38.5%). Under medication, CAP rate dropped to 26.8% in insomniacs, whereas it was only moderately reduced in controls (31%). CAP rate values paralleled the self-rating estimation of sleep quality, but the VAS means differed significantly only among insomniacs where a sharp average fall from 45 mm (placebo nights) to 15 mm (zolpidem nights) could be detected. Our findings suggest that CAP rate in sleep analysis can represent a useful tool for the diagnosis of insomnia and for gaining insight into the therapeutic efficacy of hypnotic-sedative drugs.

Twenty epileptic patients (10 male and 10 female) were polygraphically recorded during nocturnal sleep. Ten subjects, with a wide age range, were affected by focal lesional epilepsy, and 10 were children affected by benign epilepsy with rolandic spikes (BERS). In five cases a bihemispheric expression of the focal lesional bursts emerged occasionally during the night recordings. The behavior of interictal electroencephalographic (EEG) paroxysms were analyzed with respect to the two arousal states of non-rapid-eye-movement (REM) sleep: (a) the cyclic alternating pattern (CAP), expressed by biphasic EEG periodic activities and related to long-lasting fluctuations between greater (phase A) and lesser (phase B) arousal levels; and (b) the non-CAP (NCAP), manifested by EEG stationarities that reflect a sustained relative stability of arousal. The CAP/NCAP modality affected the spiking activity and distribution of the focal lesional EEG paroxysms, which appeared enhanced during CAP and which were mostly collected in phase A. The even more powerful influence of CAP and especially phase A on the secondary bisynchronous bursts suggests a crucial integration among thalamocortical circuits, arousal modulation, and epileptic generalization mechanisms. Conversely, in the BERS recordings no significant differences emerged throughout CAP and NCAP. The intense activity of the rolandic foci induced by sleep as such could be explained on the basis of the greater dependence of these functional cortical EEG abnormalities on the degree of synchronization during sleep.

The unusual association of acute confusional states with migraine was observed in three adult women. This syndrome, which was previously studied in the content of juvenile migraine, seem to have the following distinctive features: a) sudden onset of confusion and agitation in the course of violent headache attacks; b) possible close relationship with neurological deficits, suggesting basilar migraine; c) demonstration of marked electroencephalographical abnormalities in either one or both posterior quadrants; d) tendency to a rapid recovery, on both clinical and electroencephalographical standpoint. Focal edema in the vertebrobasilar arterial territory appears to be the most likely mechanism.

E.E.G. features as presented by 20 patients of rheumatic chorea (15 females and 6 males, from 11 and up to 16 year old) are examined. Pathological patterns concerned 17 patients (85%); in three cases (15%) no characteristic E.E.G. enregistrations were found. Three types of pathological E.E.G. patterns are reckoned: A type (7 patients, i.e. 35%): increased amount of Posterior Slow Waves. B type (5 patients, i.e. 25%): conspicuous alterations of the alpha rhythm, and appearance of bursts of diffused, monomorphous, slow activities. C type (5 patients, i.e. 25%): inconstant outcoming by bursts, of 2--4 cycles sec. waves on posterior regions. No correlation can be established, on the ground both of E.E.G. features and clinical findings, neither between altered E.E.G. patterns and choreic movements severity, nor between localization of the former abnormalities over the scalp, and distribution of the latter. Yet, E.E.G. alterations, when present, are not to be ruled out, as they can be the demonstration of organical impairment and can give evidence about the course and recovering. To distinguish, in rheumatic chorea between clearly pathological, by the one hand, and "functional" by the other, E.E.G. features, is assumed as difficult, as subjects are in a range of ages in which cerebral biorhythms have not reached ultimate definition.

Because arousal plays a critical role in activation of epileptic phenomena, we analyzed the behavior of interictal generalized spike-and-waves (S-W) during the two recently identified modalities of arousal control during NREM sleep: (a) the cyclic alternating pattern (CAP), expressed by successive biphasic (phase A and phase B) fluctuations of arousal; and (b) non-CAP (NCAP), characterized by prolonged stable periods of EEG and arousal level. Within the single NREM stages, phase A consists of clusters of phasic events associated with transient arousal activation, whereas phase B is represented by the periodic reappearance of the EEG background and reflects a rebound inhibitory condition. The polysomnograms of 10 subjects with a wide age range and affected by primary generalized epilepsy displayed significant differences (all at p less than 0.01) between the spike indexes (S-W/min) of CAP (2.9) and NCAP (1.3), of phase A (7.4) and phase B (0.3), and of NCAP and phase B. For distribution of S-W, a significant prevalence of EEG paroxysms was detected during CAP as compared with NCAP (68 vs. 32%, p less than 0.001), and 93% of all the interictal bursts that occurred in CAP occurred in phase A (p less than 0.001). Our data stress the arousal-dependent influence of CAP and NCAP on modulation of generalized epileptic mechanisms during sleep.

Two cases are reported of patients whose EEGs showed localised rhythmic seizure activity in the midtemporal regions of one or both hemispheres, unaccompanied by any clinical symptoms: the patients' histories differed: one was of classic migraine and the other complex partial epilepsy. The frequency and morphology of the paroxysmal anomalies was identical in the waking state and in sleep. The nosographic classification of the phenomenon is discussed with reference to Gibbs' reports regarding the "psychomotor variant type of seizure discharge", to the work of Lipman and Hughes on "rhythmic mid-temporal discharge" and to that of Westmoreland and Klass on the "subclinical rhythmic EEG discharge of adults". But in contrast to the last phenomenon there were no signs pointing to a diffuse cerebrovascular disease. Reports of such a pattern are rare in the European literature and nonexistent in the Italian literature, facts which make an ordinary interpretation of the phenomenon difficult.

The cyclic alternating pattern (CAP) characterizes stage-II coma according to Fischgold and Mathis. Its evolution and prognostic value are still uncertain. An analytic investigation of CAP and its components (phase A of greater arousal and phase B of lesser arousal) was conducted on a patient with post-traumatic coma who completely recovered. The relationships between this type of pattern and the stages of physiologic sleep were investigated during four prolonged night recordings taken at regular intervals. The evolution of CAP in post-traumatic coma is compared with the development of CAP observed in Creutzfeldt-Jakob disease. The hypothesis is suggested that CAP may be related to other cyclic phenomena, especially the Lundberg B-type CSF pressure waves. According to this assumption, in a coma with CAP, mechanisms for the organization of arousal, which are not known but persist up to the threshold of death, could still be acting. In sleep these same mechanisms are supposed to be integrated with others, more sophisticated and less resistant to pathogenic injuries.

The Cyclic Alternating Pattern (CAP) is an EEG biphasic periodic activity of normal NREM sleep, generally organized in sequences of two or more cycles and correlated to long-lasting fluctuations in the level of arousal. The remaining NREM sleep is characterized by prolonged stationary activities or non-CAP (NCAP). Criteria for the detection of morphology and reactivity of CAP and NCAP segments are defined in the sleep recordings of 10 healthy young adults. Standard sleep variables and CAP rates, referred to the ratio of CAP time to Total NREM sleep time (CAPR/NREM) and to each NREM sleep stage, were scored for statistical inferences and correlations. CAPR/NREM may be considered an additional non-redundant sleep variable which determines the measure of arousal instability during NREM sleep. The relationship between CAP and the biphasic behavior of microarousals is discussed.

The cyclic alternating pattern (CAP) is a long-lasting periodic activity consisting of two alternate electroencephalogram (EEG) patterns. This variation in EEG is closely related to fluctuations in the level of arousal that characterize two different functional states in the arousal control mechanism. We studied 20 sleep records of 10 healthy subjects to see if CAP appears under physiologic conditions. During NREM sleep, CAP corresponded to a periodic succession of spontaneous phasic phenomena recurring within every stage, i.e., intermittent alpha rhythm, K-complex sequences, and reactive slow wave sequences. The following analyses were performed. Each EEG specific alternating pattern, defined as a cycle, was subdivided into two phases depending on the arousal response to stimulation. Average cycle length, average duration of each phase, and average ratio phase/cycle were calculated. CAP rate defined as (CAP time/Sleep time) was calculated for total sleep time (TST), (Cap time/TST); for NREM sleep, (CAP time/Total NREM); and for each NREM sleep stage. CAP is the EEG translation of the reorganization of the sleeping brain challenged by the modification of environmental conditions.