EEG segmentation for improving automatic CAP detection.

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.

Efficient automatic classifiers for the detection of A phases of the cyclic alternating pattern in sleep.

This study aims to develop an automatic detector of the A phases of the cyclic alternating pattern, periodic activity that generally occurs during non-REM (NREM) sleep. Eight polysomnographic recordings from healthy subjects were examined. From EEG recordings, five band descriptors, an activity descriptor and a variance descriptor were extracted and used to train different machine-learning algorithms. A visual scoring provided by an expert clinician was used as golden standard. Four alternative mathematical machine-learning techniques were implemented: (1) discriminant classifier, (2) support vector machines, (3) adaptive boosting, and (4) supervised artificial neural network. The results of the classification, compared with the visual analysis, showed average accuracies equal to 84.9 and 81.5% for the linear discriminant and the neural network, respectively, while AdaBoost had a slightly lower accuracy, equal to 79.4%. The SVM leads to accuracy of 81.9%. The performance achieved by the automatic classification is encouraging, since an efficient automatic classifier would benefit the practice in everyday clinics, preventing the physician from the time-consuming activity of the visually scoring of the sleep microstructure over whole 8-h sleep recordings. Finally, the classification based on learning algorithms would provide an objective criterion, overcoming the problems of inter-scorer disagreement.

Cyclic alternating pattern (CAP): the marker of sleep instability.

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.

A new quantitative automatic method for the measurement of non-rapid eye movement sleep electroencephalographic amplitude variability.

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.

Characterization of A phases during the cyclic alternating pattern of sleep.

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.

Automatic detection of CAP on central and fronto-central EEG leads via Support Vector Machines.

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.

Automatic detection of a phases of the cyclic alternating pattern during sleep.

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.

The functional connectivity of different EEG bands moves towards small-world network organization during sleep.

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.

Sleep phenotypes of intellectual disability: a polysomnographic evaluation in subjects with Down syndrome and Fragile-X syndrome.

OBJECTIVE: To analyze sleep architecture and NREM sleep alterations by means of the Cyclic Alternating Pattern (CAP) in children with Down syndrome (DS) and Fragile-X syndrome (fraX), the two most common causes of inherited mental retardation, in order to find out eventual alterations of their sleep microstructure related to their mental retardation phenotypes. METHODS: Fourteen patients affected by fraX (mean age 13.1 years) and 9 affected by Down syndrome (mean age 13.8 years) and 26 age-matched normal controls were included. All subjects underwent overnight polysomnography in the sleep laboratory, after one adaptation night and their sleep architecture and CAP were visually scored. RESULTS: FraX subjects showed a reduced time in bed compared to DS subjects, whereas DS subjects showed a lower sleep efficiency, a higher percentage of wakefulness after sleep onset, and a reduced percentage of stage 2 NREM compared to the other groups. Furthermore, DS and fraX subjects, compared to normal controls, showed a higher percentage of stage 1 NREM and a lower percentage of REM sleep. FraX subjects showed the most disrupted sleep microstructure with low total CAP rate and CAP rate in S2 NREM. Both patient groups showed a lower percentage of A1 and higher percentage of A2 and A3 compared to normal controls. CONCLUSIONS: The analysis of CAP might be able to disclose new important findings in the sleep architecture of children with mental retardation and might characterize sleep microstructural patterns of the different phenotypes of intellectual disability. SIGNIFICANCE: The NREM sleep microstructure alterations found in our subjects, associated with the reduction in REM sleep percentage, seem to be distinctive features of intellectual disability.

Sleep in children with autistic spectrum disorder: a questionnaire and polysomnographic study.

OBJECTIVE: To evaluate sleep in children with autistic spectrum disorder (ASD) by means of sleep questionnaires and polysomnography; moreover, to analyze their cyclic alternating pattern (CAP). METHODS: Thirty-one patients with ASD (28 males, 3 females, aged 3.7-19 years) and age-matched normal controls were included. ASD children were evaluated by a standard sleep questionnaire that consisted of 45 items in a Likert-type scale covering several areas of sleep disorders and by overnight polysomnography in the sleep laboratory after one adaptation night. RESULTS: The questionnaire results showed that parents of ASD children reported a high prevalence of disorders of initiating and maintaining sleep, enuresis, repetitive behavior when falling asleep, and daytime sleepiness. Polysomnographically, ASD children showed reduced time in bed, total sleep time, sleep period time and rapid eye movement (REM) latency. ASD subjects had a CAP rate during slow-wave sleep (SWS) lower than normal controls, together with a lower percentage of A1 subtypes. CONCLUSIONS: ASD children questionnaires showed a higher percentage of disorders of initiating and maintaining sleep than normal controls; this was not completely confirmed by sleep staging. CAP measures showed subtle alterations of NREM sleep which could be detected with an appropriate methodology of analysis. The reduction of A1 subtypes during SWS might play a role in the impairment of cognitive functioning in these subjects.

Small-world network organization of functional connectivity of EEG slow-wave activity 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.

Regional scalp EEG slow-wave synchronization during sleep cyclic alternating pattern A1 subtypes.

The levels of EEG synchronization, in the 0.25-2.5 Hz band, during the A1 subtypes of the sleep "cyclic alternating pattern" (CAP) were measured in five healthy subjects by means of the synchronization likelihood (SL) algorithm. SL was measured for seven electrode pairs (F4-F3, C4-C3, P4-P3 for the analysis of interhemispheric SL and F4-C4, C4-P4, F3-C3, and C3-P3, for the analysis of intrahemispheric SL). During the A1 CAP subtypes, SL tended to be highest between pairs of electrodes situated over different hemispheres; in particular, SL obtained from F4-F3 was the highest, followed by that of P4-P3. These results indicate that the transient high level of synchronization in the slow-wave EEG range, during the sleep A1 CAP subtypes, is a phenomenon involving mostly the anterior parts of the brain and is probably based on interhemispheric interactions, possibly mediated by transcallosal connections.

The time structure of the cyclic alternating pattern during sleep.

STUDY OBJECTIVES: To analyze the intervals between A phases of the cyclic alternating pattern (CAP) and to describe their time structure. This might represent an additional aspect to be studied in sleep pathologies that are accompanied by CAP changes. METHODS: Sleep stages and CAP A phases were identified in polysomnographic night recordings of normal adults and children. Intervals between consecutive CAP A phases were measured, counted, and used to draw individual normalized distribution graphs. The intervals during light sleep (stages 1 and 2) were analyzed separately from those occurring during slow-wave sleep (SWS). Subsequently, we performed a Markovian analysis of intervals, in order to describe in detail their time structure. SETTING: N/A. PARTICIPANTS: Twenty-four adults and 28 children. MEASUREMENTS AND RESULTS: In adults, a preponderance of intervals shorter than 60 seconds during SWS was found; light sleep showed a higher number of intervals longer than 60 seconds. A less clear-cut difference between stages was found in children, who showed a shift of the peak in their SWS histogram toward intervals shorter than in adults. Interval sequences were not determined by a random process in both groups. The Markovian analysis showed statistically significant lower values of entropy and higher values of time dependency, mostly in adults during SWS. CONCLUSIONS: The different CAP components of sleep occur in a non-random ordered fashion, and their time structure is characterized by first-order relationships. SIGNIFICANCE: We postulate that CAP components are the expression of a timely ordered process that exhibits specific sleep stage-related features and undergoes age-related modifications.

Dynamics of the EEG slow-wave synchronization during sleep.

OBJECTIVE: To study the dynamics of spatial synchronization of the slow-wave activity recorded from different scalp electrodes during sleep in healthy normal controls. METHODS: We characterized the different levels of EEG synchronization during sleep (in the 0.25-2.5 Hz band) of five healthy subjects by means of the synchronization likelihood (SL) algorithm and analyzed its long-range temporal correlations by means of the detrended fluctuation analysis (DFA). RESULTS: We found higher levels of interregional synchronization during 'cyclic alternating pattern' (CAP) sleep than during nonCAP with a small but significant difference between its A and B phases. SL during CAP showed fluctuations probably corresponding to the single EEG slow-wave elements. DFA showed the presence of two linear scaling regions in the double-logarithmic plot of the fluctuations of SL level as a function of time scale. This indicates the presence of a characteristic time scale in the underlying dynamics which was very stable among the different subjects (1.23-1.33 s). We also computed the DFA exponent of the two scaling regions; the first, with values approximately 1.5, corresponded to fluctuations with period 0.09-0.75 s and the second, with values approximately 1, corresponded to fluctuations with period 1.5-24.0 s. Only the first exponent showed different values during the different sleep stages. CONCLUSIONS: All these results indicate a different role for each sleep stage and CAP condition in the EEG synchronization processes of sleep which show a complex time structure correlated with its neurophysiological mechanisms. SIGNIFICANCE: Very slow oscillations in spatial EEG synchronization might play a critical role in the long-range temporal EEG correlations during sleep which might be the chain of events responsible for the maintenance and correct complex development of sleep structure during the night.

All-night EEG power spectral analysis of the cyclic alternating pattern components in young adult subjects.

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.

Topographic mapping of the spectral components of the cyclic alternating pattern (CAP).

BACKGROUND AND PURPOSE: The aim of this study was to define quantitatively the spectrum content of the sleep pattern termed 'cyclic alternating pattern' (CAP) A phases, their scalp topography and their probable cortical generators, by using data from sleep polygraphic recordings that included a large number of scalp EEG channels. PATIENTS AND METHODS: Polysomnographic recording that include 19 EEG channels were obtained from 5 normal healthy young controls. After sleep staging, for each subject, 5 different CAP A phase subtype epochs were selected, which served for subsequent analysis. Following the analysis of power spectra calculated on the C4 channel by means of the fast Fourier transform, two different frequency bands were detected: 0.25-2.5 and 7-12Hz, representing the frequency peak in the profiles of the different CAP subtypes. All the subsequent analyses were performed on these two bands. Scalp topographic color mapping was carried out using the data from all the 19 EEG channels recorded, and by means of the 4-nearest neighbor algorithm. Individual average maps were obtained for both frequency bands. Finally, we used the low resolution brain electromagnetic tomography (LORETA) functional imaging for the source analysis of the two EEG frequency components of CAP A phases. RESULTS: The quantitative spectral analysis of the different A phase subtypes shows the existence of two distinct spectral components characterizing CAP subtypes A1 (0.25-2.5Hz) and A3 (7-12Hz). These two components coexist in CAP A2 subtypes. The topography of these two components shows a clear prevalence over the anterior frontal regions for the 0.25-2.5Hz band and over the parietal-occipital areas for the 7-12Hz band. Finally, the generators of the low-frequency component of CAP seemed to be localized mostly over the frontal midline cortex; on the contrary, those of the high-frequency band involved both midline and hemispheric areas within the parietal and occipital areas. CONCLUSIONS: The results of this study confirm the presence of two fundamentally distinct frequency bands which are expressed individually (A1 and A3) or in association (A2) in the different CAP A phase subtypes. The analysis of scalp distribution maps indicates that the two frequency components recognized are distributed over clearly different areas of the scalp. Moreover, the LORETA analysis indicates that also the probable cortical generators of these two frequency bands are different and well separated and distinct.

Inter-rater reliability of sleep cyclic alternating pattern (CAP) scoring and validation of a new computer-assisted CAP scoring method.

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.

Obstructive sleep-disordered breathing with a dominant cyclic alternating pattern--a recognizable polysomnographic variant with practical clinical implications.

OBJECTIVES: To define the clinical and polysomnographic features of a distinct variant of obstructive sleep-disordered breathing that is remarkably mild during rapid eye movement (REM) sleep. DESIGN: Observational study and evaluation of polysomnographic and clinical records. SETTING: American Academy of Sleep Medicine-accredited multidisciplinary sleep disorders center and laboratory. PATIENTS: 35 medication-free subjects with clinical and polysomnographic severe obstructive sleep-disordered breathing selected for dominance of 1 of 2 disordered breathing patterns. INTERVENTIONS: Positive airway pressure titration. MEASUREMENTS AND RESULTS: Nasal pressure was used to score respiratory events. Sleep was scored by both the standard criteria and cyclic alternating pattern (CAP), and the distribution of respiratory events was tabulated and analyzed. A distinct clinical and polysomnographic syndrome emerged, CAP-dominant sleep-disordered breathing, characterized by severe relatively short cycle obstructive events during non-REM sleep that were mild in REM sleep. Characteristics include lower body mass index, fewer apneas, and a lower hypoxic burden as reflected by frequency and severity of nocturnal oxygen saturation. During positive pressure titration, a remarkable respiratory instability emerged selectively during CAP, in contrast to stability during REM sleep. This partial treatment failure was associated with persistent clinical symptoms. CONCLUSIONS: This variant of sleep apnea may reflect a dominant component of respiratory instability and periodic breathing coupled with upper-airway obstruction. Its existence questions the conventional practice of calculating global respiratory indexes. Besides positive airway pressure, measures to treat periodic breathing may be required.

Non-linear EEG measures during sleep: effects of the different sleep stages and cyclic alternating pattern.

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.