Clinical applications of cyclic alternating pattern

Deep-Learning-Based Classification of Cyclic-Alternating-Pattern Sleep Phases

Determining the cyclic-alternating-pattern (CAP) phases in sleep using electroencephalography (EEG) signals is crucial for assessing sleep quality. However, most current methods for CAP classification primarily rely on classical machine learning techniques, with limited implementation of deep-learning-based tools. Furthermore, these methods often require manual feature extraction. Herein, we propose a fully automatic deep-learning-based algorithm that leverages convolutional neural network architectures to classify the EEG signals via their time-frequency representations. Through our investigation, we explored using time-frequency analysis techniques and found that Wigner-based representations outperform the commonly used short-time Fourier transform for CAP classification. Additionally, our algorithm incorporates contextual information of the EEG signals and employs data augmentation techniques specifically designed to preserve the time-frequency structure. The model is developed using EEG signals of healthy subjects from the publicly available CAP sleep database (CAPSLPDB) on Physionet. An experimental study demonstrates that our algorithm surpasses existing machine-learning-based methods, achieving an accuracy of 77.5% on a balanced test set and 81.8% when evaluated on an unbalanced test set. Notably, the proposed algorithm exhibits efficiency and scalability, making it suitable for on-device implementation to enhance CAP identification procedures.

Computerized detection of cyclic alternating patterns of sleep: A new paradigm, future scope and challenges

BACKGROUND AND OBJECTIVES

Sleep quality is associated with wellness, and its assessment can help diagnose several disorders and diseases. Sleep analysis is commonly performed based on self-rating indices, sleep duration, environmental factors, physiologically and polysomnographic-derived parameters, and the occurrence of disorders. However, the correlation that has been observed between the subjective assessment and objective measurements of sleep quality is small. Recently, a few automated systems have been suugested to measure sleep quality to address this challenge. Sleep quality can be assessed by evaluating macrostructure-based sleep analysis via the examination of sleep cycles, namely Rapid Eye Movement (REM) and Non Rapid Eye Movement (NREM) with N1, N2, and N3 stages. However, macrostructure sleep analysis does not consider transitory phenomena like K-complexes and transient fluctuations, which are indispensable in diagnosing various sleep disorders. The CAP, part of the microstructure of sleep, may offer a more precise and relevant examination of sleep and can be considered one of the candidates to measure sleep quality and identify sleep disorders such as insomnia and apnea. CAP is characterized by very subtle changes in the brain's electroencephalogram (EEG) signals that occur during the NREM stage of sleep. The variations among these patterns in healthy subjects and subjects with sleep disorders can be used to identify sleep disorders. Studying CAP is highly arduous for human experts; thus, developing automated systems for assessing CAP is gaining momentum. Developing new techniques for automated CAP detection installed in clinical setups is essential. This paper aims to analyze the algorithms and methods presented in the literature for the automatic assessment of CAP and the development of CAP-based sleep markers that may enhance sleep quality assessment, helping diagnose sleep disorders.

METHODS

This literature survey examined the automated assessment of CAP and related parameters. We have reviewed 34 research articles, including fourteen ML, nine DL, and ten based on some other techniques.

RESULTS

The review includes various algorithms, databases, features, classifiers, and classification performances and their comparisons, advantages, and limitations of automated systems for CAP assessment.

CONCLUSION

A detailed description of state-of-the-art research findings on automated CAP assessment and associated challenges has been presented. Also, the research gaps have been identified based on our review. Further, future research directions are suggested for sleep quality assessment using CAP.

A-phase index: an alternative view for sleep stability analysis based on automatic detection of the A-phases from the cyclic alternating pattern

STUDY OBJECTIVES

Sleep stability can be studied by evaluating the cyclic alternating pattern (CAP) in electroencephalogram (EEG) signals. The present study presents a novel approach for assessing sleep stability, developing an index based on the CAP A-phase characteristics to display a sleep stability profile for a whole night's sleep.

METHODS

Two ensemble classifiers were developed to automatically score the signals, one for "A-phase" and the other for "non-rapid eye movement" estimation. Both were based on three one-dimension convolutional neural networks. Six different inputs were produced from the EEG signal to feed the ensembles' classifiers. A proposed heuristic-oriented search algorithm individually tuned the classifiers' structures. The outputs of the two ensembles were combined to estimate the A-phase index (API). The models can also assess the A-phase subtypes, their API, and the CAP cycles and rate.

RESULTS

Four dataset variations were considered, examining healthy and sleep-disordered subjects. The A-phase average estimation's accuracy, sensitivity, and specificity range was 82%-87%, 72%-80%, and 82%-88%, respectively. A similar performance was attained for the A-phase subtype's assessments, with an accuracy range of 82%-88%. Furthermore, in the examined dataset's variations, the API metric's average error varied from 0.15 to 0.25 (with a median range of 0.11-0.24). These results were attained without manually removing wake or rapid eye movement periods, leading to a methodology suitable to produce a fully automatic CAP scoring algorithm.

CONCLUSIONS

Metrics based on API can be understood as a new view for CAP analysis, where the goal is to produce and examine a sleep stability profile.

The Future of Sleep Staging, Revisited

In 1996, I published a paper entitled "The Future of Sleep Staging". At this time, paper and ink records were the standard way of recording sleep records. Computerised systems had only recently become commercially available. The original article was a response to those initial computer-based systems, pointing out the potential limitations of the systems. Now, digital sleep recording is ubiquitous and software and hardware capabilities have improved immeasurably. However, I will argue that despite 50 years of progress, there has not been an increase in the accuracy of sleep staging. I will propose that this is due to the limitations of the task that we have set the automatic analysis methods.

Effects of CPAP Treatment on Electroencephalographic Activity in Patients with Obstructive Sleep Apnea Syndrome During Deep Sleep with Consideration of Cyclic Alternating Pattern

Objective

To investigate whether continuous positive airway pressure (CPAP) treatment would change EEG activities associated with cyclic alternating pattern (CAP subtype A1, A2, and A3) and non-CAP (NCAP) during non-rapid eye movement sleep stage 3 (N3) in patients with obstructive sleep apnea (OSA).

Methods

The effects of CPAP treatment on the percentages of sleep stage N3 occupied by the CAP and NCAP, power of EEG waves in the CAP and NCAP were examined in 18 patients with moderate-to-severe OSA undergoing polysomnographic recordings.

Results

Apnea and hypopnea index during sleep stage N3 was positively correlated with ratios of phases A2 and A3 duration to total phase A duration [Phase (A2+A3) /Phase A] and negatively correlated with phase A1/phase A. With CPAP treatment, percentages of sleep stage N3 occupied by total CAPs and subtypes A2 and A3, as well as CAP A2 and CAP A3 indexes were significantly decreased while percentages of sleep stage N3 occupied by NCAP (NCAP/N3) and CAP A1 index were significantly increased. In addition, CPAP treatment significantly decreased percentage of respiratory events associated CAPs and increased percentage of non-respiratory related CAPs. Moreover, absolute and relative delta power was significantly increased during phase A1, unchanged during phase A2 and phase B2, and significantly decreased during phases B1, A3 and B3. The absolute power of faster frequency EEG waves in CAPs showed a general trend of decrease. The absolute and relative power of delta waves with amplitudes ≥75 μV, but not <75 μV, was significantly increased.

Conclusion

CPAP treatment improves the sleep quality in OSA patients mainly by increasing delta power and decreasing power of higher frequency waves during phase A1, and decreasing CAP A2 and A3 indexes as well as increasing NCAP/N3 and power of delta waves with amplitudes ≥75 μV during NCAP.

Heuristic Optimization of Deep and Shallow Classifiers: An Application for Electroencephalogram Cyclic Alternating Pattern Detection

Methodologies for automatic non-rapid eye movement and cyclic alternating pattern analysis were proposed to examine the signal from one electroencephalogram monopolar derivation for the A phase, cyclic alternating pattern cycles, and cyclic alternating pattern rate assessments. A population composed of subjects free of neurological disorders and subjects diagnosed with sleep-disordered breathing was studied. Parallel classifications were performed for non-rapid eye movement and A phase estimations, examining a one-dimension convolutional neural network (fed with the electroencephalogram signal), a long short-term memory (fed with the electroencephalogram signal or with proposed features), and a feed-forward neural network (fed with proposed features), along with a finite state machine for the cyclic alternating pattern cycle scoring. Two hyper-parameter tuning algorithms were developed to optimize the classifiers. The model with long short-term memory fed with proposed features was found to be the best, with accuracy and area under the receiver operating characteristic curve of 83% and 0.88, respectively, for the A phase classification, while for the non-rapid eye movement estimation, the results were 88% and 0.95, respectively. The cyclic alternating pattern cycle classification accuracy was 79% for the same model, while the cyclic alternating pattern rate percentage error was 22%.

Can Daytime Transcranial Direct Current Stimulation Treatment Change the Sleep Electroencephalogram Complexity of REM Sleep in Depressed Patients? A Double-Blinded, Randomized, Placebo-Controlled Trial

STUDY OBJECTIVES

The purpose of this study was to determine the effects of daytime transcranial direct current stimulation (tDCS) on sleep electroencephalogram (EEG) in patients with depression.

METHODS

The study was a double-blinded, randomized, controlled clinical trial. A total of 37 patients diagnosed with a major depression were recruited; 19 patients (13 females and 6 males mean age 44.79 ± 15.25 years) received tDCS active stimulation and 18 patients (9 females and 9 males; mean age 43.61 ± 11.89 years) received sham stimulation. Ten sessions of daytime tDCS were administered with the anode over F3 and the cathode over F4. Each session delivered a 2 mA current for 30 min per 10 working days. Hamilton-24 and Montgomery scales were used to assess the severity of depression, and polysomnography (PSG) was used to assess sleep structure and EEG complexity. Eight intrinsic mode functions (IMFs) were computed from each EEG signal in a channel. The sample entropy of the cumulative sum of the IMFs were computed to acquire high-dimensional multi-scale complexity information of EEG signals.

RESULTS

The complexity of Rapid Eye Movement (REM) EEG signals significantly decreased intrinsic multi-scale entropy (iMSE) (1.732 ± 0.057 vs. 1.605 ± 0.046, P = 0.0004 in the case of the C4 channel, IMF 1:4 and scale 7) after tDCS active stimulation. The complexity of the REM EEG signals significantly increased iMSE (1.464 ± 0.101 vs. 1.611 ± 0.085, P = 0.001 for C4 channel, IMF 1:4 and scale 7) after tDCS sham stimulation. There was no significant difference in the Hamilton-24 (P = 0.988), Montgomery scale score (P = 0.726), and sleep structure (N1% P = 0.383; N2% P = 0.716; N3% P = 0.772) between the two groups after treatment.

CONCLUSION

Daytime tDCS changed the complexity of sleep in the REM stage, and presented as decreased intrinsic multi-scale entropy, while no changes in sleep structure occurred. This finding indicated that daytime tDCS may be an effective method to improve sleep quality in depressed patients. Trial registration This trial has been registered at the ClinicalTrials.gov (protocol ID: TCHIRB-10409114, in progress).

Automated Characterization of Cyclic Alternating Pattern Using Wavelet-Based Features and Ensemble Learning Techniques with EEG Signals

Sleep is highly essential for maintaining metabolism of the body and mental balance for increased productivity and concentration. Often, sleep is analyzed using macrostructure sleep stages which alone cannot provide information about the functional structure and stability of sleep. The cyclic alternating pattern (CAP) is a physiological recurring electroencephalogram (EEG) activity occurring in the brain during sleep and captures microstructure of the sleep and can be used to identify sleep instability. The CAP can also be associated with various sleep-related pathologies, and can be useful in identifying various sleep disorders. Conventionally, sleep is analyzed using polysomnogram (PSG) in various sleep laboratories by trained physicians and medical practitioners. However, PSG-based manual sleep analysis by trained medical practitioners is onerous, tedious and unfavourable for patients. Hence, a computerized, simple and patient convenient system is highly desirable for monitoring and analysis of sleep. In this study, we have proposed a system for automated identification of CAP phase-A and phase-B. To accomplish the task, we have utilized the openly accessible CAP sleep database. The study is performed using two single-channel EEG modalities and their combination. The model is developed using EEG signals of healthy subjects as well as patients suffering from six different sleep disorders namely nocturnal frontal lobe epilepsy (NFLE), sleep-disordered breathing (SDB), narcolepsy, periodic leg movement disorder (PLM), insomnia and rapid eye movement behavior disorder (RBD) subjects. An optimal orthogonal wavelet filter bank is used to perform the wavelet decomposition and subsequently, entropy and Hjorth parameters are extracted from the decomposed coefficients. The extracted features have been applied to different machine learning algorithms. The best performance is obtained using ensemble of bagged tress (EBagT) classifier. The proposed method has obtained the average classification accuracy of 84%, 83%, 81%, 78%, 77%, 76% and 72% for NFLE, healthy, SDB, narcolepsy, PLM, insomnia and RBD subjects, respectively in discriminating phases A and B using a balanced database. Our developed model yielded an average accuracy of 78% when all 77 subjects including healthy and sleep disordered patients are considered. Our proposed system can assist the sleep specialists in an automated and efficient analysis of sleep using sleep microstructure.

Characterization of cyclic alternating pattern during sleep in older men and women using large population studies

STUDY OBJECTIVES

To assess the microstructural architecture of non-rapid eye movement (NREM) sleep known as cyclic alternating pattern (CAP) in relation to the age, gender, self-reported sleep quality, and the degree of sleep disruption in large community-based cohort studies of older people.

METHODS

We applied a high-performance automated CAP detection system to characterize CAP in 2,811 men from the Osteoporotic Fractures in Men Sleep Study (MrOS) and 426 women from the Study of Osteoporotic Fractures (SOF). CAP was assessed with respect to age and gender and correlated to obstructive apnea-hypopnea index, arousal index (AI-NREM), and periodic limb movements in sleep index. Further, we evaluated CAP across levels of self-reported sleep quality measures using analysis of covariance.

RESULTS

Age was significantly associated with the number of CAP sequences during NREM sleep (MrOS: p = 0.013, SOF = 0.051). CAP correlated significantly with AI-NREM (MrOS: ρ = 0.30, SOF: ρ = 0.29). CAP rate, especially the A2+A3 index, was inversely related to self-reported quality of sleep, independent of age and sleep disturbance measures. Women experienced significantly fewer A1-phases compared to men, in particular, in slow-wave sleep (N3).

CONCLUSIONS

We demonstrate that automated CAP analysis of large-scale databases can lead to new findings on CAP and its subcomponents. We show that sleep disturbance indices are associated with the CAP rate. Further, the CAP rate is significantly linked to subjectively reported sleep quality, independent from traditionally scored markers of sleep fragmentation. Finally, men and women show differences in the microarchitecture of sleep as identified by CAP, despite similar macro-architecture.

On the use of patterns obtained from LSTM and feature-based methods for time series analysis: application in automatic classification of the CAP A phase subtypes

The cyclic alternating pattern is a marker of sleep instability identified in the electroencephalogram signals whose sequence of transient variations compose the A phases. These phases are divided into three subtypes (A1, A2, and A3) according to the presented patterns. The traditional approach of manually scoring the cyclic alternating pattern events for the full night is unpractical, with a high probability of miss classification, due to the large quantity of information that is produced during a full night recording. To address this concern, automatic methodologies were proposed using a long short-term memory to perform the classification of one electroencephalogram monopolar derivation signal. The proposed model is composed of three classifiers, one for each subtype, performing binary classification in a one versus all procedure. Two methodologies were tested: feed the pre-processed electroencephalogram signal to the classifiers; create features from the pre-processed electroencephalogram signal which were fed to the classifiers (feature-based methods). It was verified that the A1 subtype classification performance was similar for both methods and the A2 subtype classification was higher for the feature-based methods. However, the A3 subtype classification was found to be the most challenging to be performed, and for this classification, the feature-based methods were superior. A characterization analysis was also performed using a recurrence quantification analysis to further examine the subtypes characteristics. The average accuracy and area under the receiver operating characteristic curve for the A1, A2, and A3 subtypes of the feature-based methods were respectively: 82% and 0.92; 80% and 0.88; 85% and 0.86.

Cyclic alternating pattern (CAP) rate in the polygraphic investigation of classic and novel hypnotic drugs

The difficulty to recruit homogeneous samples of insomniacs requires alternative approaches for sleep studies. Acoustic perturbation in healthy volunteers made it possible to determine an experimental model of acute situational insomnia in order to investigate the effects of classic and novel hypnotic compounds. Unfortunately, the traditional scoring parameters of sleep are inadequate to provide reliable information for defining the neurophysiological bases of insomnia and for evaluating the efficacy of hypnotic drugs. Recent studies on the microstructure of sleep have permitted to identify a specific EEG feature, the cyclic alternating pattern (CAP), correlated with the subjective appreciation of sleep quality. Comparing placebo, zolpidem, zopiclone, lorazepam and triazolam, given at equivalent therapeutic doses in middle-aged healthy volunteer subjects under basal conditions and under acute situational insomnia, provided non-significant information when using classical sleep parameters whereas CAP rate (the percentage ratio of CAP time to non-REM sleep time) permitted to discriminate the basal nights from the perturbed nights and the drug nights from the placebo nights. These data have been confirmed in clinical studies using zolpidem versus placebo in chronic insomniacs.

Automated phase classification in cyclic alternating patterns in sleep stages using Wigner-Ville Distribution based features

Sleep is one of the most important body mechanisms responsible for the proper functioning of human body. Cyclic alternating patterns (CAP) play an indispensable role in the analysis of sleep quality and related disorders like nocturnal front lobe epilepsy, insomnia, narcolepsy etc. The traditional manual segregation methods of CAP phases by the medical experts are prone to human fatigue and errors which may lead to inaccurate diagnosis of sleep stages. In this paper, we present an automated approach for the classification of CAP phases (A and B) using Wigner-Ville Distribution (WVD) and Rényi entropy (RE) features. The WVD provides a high-resolution time-frequency analysis of the signals whereas RE provides least time-frequency uncertainty with WVD. The classification is performed using medium Gaussian kernel-based support vector machine with 10-fold cross-validation technique. We have presented the results for randomly sampled balanced data sets. The proposed approach does not require any pre-processing or post-processing stages, making it simple as compared to the existing techniques. The proposed method is able to achieve an average classification accuracy of 72.35% and 87.45% for balanced and unbalanced data sets respectively. The proposed method can aid the medical experts to analyze the cerebral stability as well as the sleep quality of a person.

Non-equilibrium critical dynamics of bursts in θ and δ rhythms as fundamental characteristic of sleep and wake micro-architecture

Origin and functions of intermittent transitions among sleep stages, including short awakenings and arousals, constitute a challenge to the current homeostatic framework for sleep regulation, focusing on factors modulating sleep over large time scales. Here we propose that the complex micro-architecture characterizing the sleep-wake cycle results from an underlying non-equilibrium critical dynamics, bridging collective behaviors across spatio-temporal scales. We investigate θ and δ wave dynamics in control rats and in rats with lesions of sleep-promoting neurons in the parafacial zone. We demonstrate that intermittent bursts in θ and δ rhythms exhibit a complex temporal organization, with long-range power-law correlations and a robust duality of power law (θ-bursts, active phase) and exponential-like (δ-bursts, quiescent phase) duration distributions, typical features of non-equilibrium systems self-organizing at criticality. Crucially, such temporal organization relates to anti-correlated coupling between θ- and δ-bursts, and is independent of the dominant physiologic state and lesions, a solid indication of a basic principle in sleep dynamics.

Disturbed EEG sleep, paranoid cognition and somatic symptoms identify veterans with post-traumatic stress disorder

BACKGROUND

Chronic post-traumatic stress disorder (PTSD) behavioural symptoms and medically unexplainable somatic symptoms are reported to occur following the stressful experience of military combatants in war zones.

AIMS

To determine the contribution of disordered EEG sleep physiology in those military combatants who have unexplainable physical symptoms and PTSD behavioural difficulties following war-zone exposure.

METHOD

This case-controlled study compared 59 veterans with chronic sleep disturbance with 39 veterans with DSM-IV and clinician-administered PTSD Scale diagnosed PTSD who were unresponsive to pharmacological and psychological treatments. All had standardised EEG polysomnography, computerised sleep EEG cyclical alternating pattern (CAP) as a measure of sleep stability, self-ratings of combat exposure, paranoid cognition and hostility subscales of Symptom Checklist-90, Beck Depression Inventory and the Wahler Physical Symptom Inventory. Statistical group comparisons employed linear models, logistic regression and chi-square automatic interaction detection (CHAID)-like decision trees.

RESULTS

Veterans with PTSD were more likely than those without PTSD to show disturbances in non-rapid eye movement (REM) and REM sleep including delayed sleep onset, less efficient EEG sleep, less stage 4 (deep) non-REM sleep, reduced REM and delayed onset to REM. There were no group differences in the prevalence of obstructive sleep apnoeas/hypopnoeas and periodic leg movements, but sleep-disturbed, non-PTSD military had more EEG CAP sleep instability. Rank order determinants for the diagnosis of PTSD comprise paranoid thinking, onset to REM sleep, combat history and somatic symptoms. Decision-tree analysis showed that a specific military event (combat), delayed onset to REM sleep, paranoid thinking and medically unexplainable somatic pain and fatigue characterise chronic PTSD. More PTSD veterans reported domestic and social misbehaviour.

CONCLUSIONS

Military combat, disturbed REM/non-REM EEG sleep, paranoid ideation and medically unexplained chronic musculoskeletal pain and fatigue are key factors in determining PTSD disability following war-zone exposure.

DECLARATION OF INTEREST

None.

COPYRIGHT AND USAGE

© The Royal College of Psychiatrists 2016. This is an open access article distributed under the terms of the Creative Commons Non-Commercial, No Derivatives (CC BY-NC-ND) license.

Looking for a precursor of spontaneous Sleep Slow Oscillations in human sleep: The role of the sigma activity

Sleep Slow Oscillations (SSOs), paradigmatic EEG markers of cortical bistability (alternation between cellular downstates and upstates), and sleep spindles, paradigmatic EEG markers of thalamic rhythm, are two hallmarks of sleeping brain. Selective thalamic lesions are reportedly associated to reductions of spindle activity and its spectrum ~14 Hz (sigma), and to alterations of SSO features. This apparent, parallel behavior suggests that thalamo-cortical entrainment favors cortical bistability. Here we investigate temporally-causal associations between thalamic sigma activity and shape, topology, and dynamics of SSOs. We recorded sleep EEG and studied whether spatio-temporal variability of SSO amplitude, negative slope (synchronization in downstate falling) and detection rate are driven by cortical-sigma-activity expression (12-18Hz), in 3 consecutive 1s-EEG-epochs preceding each SSO event (Baselines). We analyzed: (i) spatial variability, comparing maps of baseline sigma power and of SSO features, averaged over the first sleep cycle; (ii) event-by-event shape variability, computing for each electrode correlations between baseline sigma power and amplitude/slope of related SSOs; (iii) event-by-event spreading variability, comparing baseline sigma power in electrodes showing an SSO event with the homologous ones, spared by the event. The scalp distribution of baseline sigma power mirrored those of SSO amplitude and slope; event-by-event variability in baseline sigma power was associated with that in SSO amplitude in fronto-central areas; within each SSO event, electrodes involved in cortical bistability presented higher baseline sigma activity than those free of SSO. In conclusion, spatio-temporal variability of thalamocortical entrainment, measured by background sigma activity, is a reliable estimate of the cortical proneness to bistability.

Nonlinear analysis of the change points between A and B phases during the Cyclic Alternating Pattern under normal sleep

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.

Effects of bedtime very low dose cyclobenzaprine on symptoms and sleep physiology in patients with fibromyalgia syndrome: a double-blind randomized placebo-controlled study

OBJECTIVE

To determine the effects of bedtime very low dose (VLD) cyclobenzaprine (CBP) on symptoms and sleep physiology of patients with fibromyalgia (FM), unrefreshing sleep, and the α-nonREM sleep electroencephalographic (EEG) anomaly at screening.

METHODS

Of 37 patients with FM in the screened population, 36 were randomized and treated in this 8-week, double-blind, placebo-controlled, dose-escalating study of VLD CBP 1-4 mg at bedtime. We evaluated changes in subjective symptoms including pain, tenderness, fatigue, mood [Hospital Anxiety and Depression Scale (HAD)], and objective EEG sleep physiology (at screening, baseline, and Weeks 2, 4, and 8).

RESULTS

In the VLD CBP-treated group (n = 18) over 8 weeks, musculoskeletal pain and fatigue decreased, tenderness improved; total HAD score and the HAD depression subscore decreased; patient-rated and clinician-rated fatigue improved. In the placebo-treated group (n = 18), none of these outcome measures changed significantly. Compared to placebo at 8 weeks, VLD CBP significantly improved pain, tenderness, and the HAD Depression subscore. Analysis of cyclic alternating pattern (CAP) sleep EEG revealed that significantly more subjects in the VLD CBP group than the placebo group had increased nights of restorative sleep in which CAP(A2+A3)/CAP(A1+A2+A3) = CAP(A2+A3(Norm)) ≤ 33%. For VLD CBP-treated subjects, the increase in nights with CAP(A2+A3(Norm)) ≤ 33% was correlated to improvements in fatigue, total HAD score, and HAD depression score.

CONCLUSION

Bedtime VLD CBP treatment improved core FM symptoms. Nights with CAP(A2+A3(Norm)) ≤ 33% may provide a biomarker for assessing treatment effects on nonrestorative sleep and associated fatigue and mood symptoms in persons with FM.

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.

Sleep cyclic alternating pattern analysis in healthy children during the first year of life: a daytime polysomnographic study

We evaluated the cyclic alternating pattern (CAP) during the first year of life in order to obtain information on the maturation of arousal mechanisms during NREM sleep and to provide normative data for CAP parameters in this age range (5-16months). Eleven healthy children (mean age 7.9±3.3months, seven boys) were studied while they slept in the morning. They underwent a 3-h video-EEG-polysomnographic recording at the Pediatric Sleep Unit of Sant'Andrea Hospital in Rome, Italy. Sleep was scored visually for sleep architecture and CAP analysis using standard criteria. Our results were complemented by CAP data from a previous sample of healthy infants (2-4months), studied when they slept during the morning, in order to correlate CAP parameters with age. The total sample comprised 24 children. The sleep period was approximately 2h, with a first REM latency of about 30min, and a clear distinction between stages N1, N2, and N3. The arousal index was 12±2.1 events/hour of sleep. The total CAP rate was 23.7±7.6%, and it increased progressively with the deepness of sleep; the highest values were observed during stage N3 and the lowest values during stage N1. A1 phases were the most numerous (78.2%), followed by A2 (14%) and A3 (7.7%) phases. The A1 index was higher than the A2 and A3 indices, whereas the mean duration of B was higher than that of A. The correlation showed that the CAP rate, A1, A2, A3 indices, A2, A3 percentages, and the average duration of B increased with age, whereas the A1 percentage decreased. We provide the first data on CAP analysis in children aged 5-16months, studied when they slept during the morning. Our results confirm the trend toward an increase in CAP rate during the first year of life. In addition, we observed a progressive increase in CAP rate with deepness of sleep, and with age, reflecting maturation of slow-wave activity. The decreased percentage of A1 subtypes may reflect the maturation of arousability.

Clonidine has a paradoxical effect on cyclic arousal and sleep bruxism during NREM sleep

STUDY OBJECTIVE

Clonidine disrupts the NREM/REM sleep cycle and reduces the incidence of rhythmic masticatory muscle activity (RMMA) characteristic of sleep bruxism (SB). RMMA/SB is associated with brief and transient sleep arousals. This study investigates the effect of clonidine on the cyclic alternating pattern (CAP) in order to explore the role of cyclic arousal fluctuation in RMMA/SB.

DESIGN

Polysomnographic recordings from a pharmacological study.

SETTING

University sleep research laboratory.

PARTICIPANTS AND INTERVENTIONS

Sixteen SB subjects received a single dose of clonidine or placebo at bedtime in a crossover design.

MEASUREMENTS AND RESULTS

Sleep variables and RMMA/SB index were evaluated. CAP was scored to assess arousal instability between sleep-maintaining processes (phase A1) and stronger arousal processes (phases A2 and A3). Paired t-tests, ANOVAs, and cross-correlations were performed. Under clonidine, CAP time, and particularly the number of A3 phases, increased (P≤0.01). RMMA/SB onset was time correlated with phases A2 and A3 for both placebo and clonidine nights (P≤0.004). However, under clonidine, this positive correlation began up to 40 min before the RMMA/SB episode.

CONCLUSIONS

CAP phase A3 frequency increased under clonidine, but paradoxically, RMMA/SB decreased. RMMA/SB was associated with and facilitated in CAP phase A2 and A3 rhythms. However, SB generation could be influenced by other factors besides sleep arousal pressure. NREM/REM ultradian cyclic arousal fluctuations may be required for RMMA/SB onset.

EEG complexity during sleep: on the effect of micro and macro sleep structure

This work investigates the relation between EEG complexity measures, in particular Fractal Dimension and Sample Entropy, and sleep structure, in terms of both macrostructure, i.e. sleep stages, and microstructure, i.e. phase A activation of CAP sleep. Activation phases are compared with the non-activation periods of non-REM sleep. The study suggests that complexity features can serve as consistent descriptors of sleep dynamics and can potentially assist in the classification of sleep stages.

Sleep characteristics in children with growth hormone deficiency

BACKGROUND/AIMS

Growth hormone (GH) is preferentially secreted during slow wave sleep and the interactions between human sleep and the somatotropic system are well documented, although only few studies have investigated the sleep EEG in children with GH deficiency (GHD). The aim of this study was to evaluate the sleep structure of children with dysregulation of the GH/insulin-like growth factor axis.

METHODS

Laboratory polysomnographic sleep recordings were obtained from 10 GHD children and 20 normal healthy age-matched children. The classical sleep parameters were evaluated together with sleep microstructure, by means of the cyclic alternating pattern (CAP), in GHD patients and compared to the control group.

RESULTS

GHD children showed a significant decrease in total sleep time, sleep efficiency, movement time and in non-rapid eye movement sleep stage 2. Although some indicators of sleep fragmentation were increased in GHD children, we found a general decrease in EEG arousability represented by a significant global decrease in the CAP rate, involving all CAP A phase subtypes.

CONCLUSIONS

The analysis of sleep microstructure by means of CAP, in children with GHD, showed a reduction of transient EEG amplitude oscillations. Further studies are needed in order to better clarify whether GH therapy is able to modify sleep microstructure in GHD children, and the relationships between sleep microstructure, hormonal secretion and neurocognitive function in these patients.

Alterations in cyclic alternating pattern associated with phase advanced sleep are differentially modulated by gaboxadol and zolpidem

OBJECTIVE

to evaluate cyclic alternating pattern (CAP) in a phase advance model of transient insomnia and the effects of gaboxadol and zolpidem.

DESIGN

a randomized, double-blind, cross-over study in which habitual sleep time was advanced by 4 h.

SETTING

6 sleep research laboratories in US PARTICIPANTS: 55 healthy subjects (18-57 y)

INTERVENTIONS

Gaboxadol 15 mg (GBX), zolpidem 10 mg (ZOL), and placebo (PBO).

MEASUREMENTS

routine polysomnographic (PSG) measures, CAP, spectral power density, and self-reported sleep measures

RESULTS

The phase advance model of transient insomnia produced significant changes in CAP parameters. Both GBX and ZOL significantly and differentially modified CAP parameters in the direction of more stable sleep. GBX brought the CAP rate in stage 1 sleep and slow wave sleep (SWS) closer to baseline levels but did not significantly change the CAP rate in stage 2. ZOL reduced the CAP rate in stage 2 to near baseline levels, whereas the CAP rate in stage 1 and SWS was reduced substantially below baseline levels. The CAP parameter A1 index (associated with SWS and sleep continuity) showed the highest correlation with self-reported sleep quality, higher than any traditional PSG, spectral, or other self-reported measures.

CONCLUSION

disruptions in CAP produced by phase advanced sleep were significantly and differentially modulated by gaboxadol and zolpidem. The relative independence of CAP parameters from other electrophysiological measures of sleep, their high sensitivity to sleep disruption, and their strong association with subjective sleep quality suggest that CAP variables may serve as valuable endpoints in future insomnia research.

Autonomic arousals related to traffic noise during sleep

AIM

To analyze the heart rate (HR) response to traffic noise during sleep and the influence of acoustic parameters, time of night, and momentary sleep stage on these responses.

PARTICIPANTS

Twelve women and 12 men (19-28 years).

MEASUREMENTS AND RESULTS

The participants slept in the laboratory for 4 consecutive nights in each of 3 consecutive weeks and were exposed to aircraft, road, or rail traffic noise with weekly permutations. The 4 nights of each week consisted of a random sequence of a quiet night (32 dBA) and 3 nights during which aircraft, rail traffic, or road traffic noises occurred with maximum levels of 45-77 dBA. The polysomnogram and the electrocardiogram were recorded during all nights. In case of awakenings, the HR alterations consisted of monophasic elevations for >1 min, with mean maximum HR elevations of 30 bpm. Though obviously triggered by the noise events, the awakenings per se rather than the acoustical parameters determined the extent and pattern of the response. Without awakenings, HR responses were biphasic and consisted of initial accelerations with maximum HR elevations of about 9 bpm followed by decelerations below the baseline. These alterations were clearly influenced by the acoustic parameters (traffic mode, maximum level, rate of rise) as well as by the momentary sleep stage.

CONCLUSIONS

Cardiac responses did not habituate to traffic noise within the night and may therefore play a key role in promoting traffic noise induced cardiovascular disease. If so, these consequences are more likely for responses accompanied by awakenings than for situations without awakenings.

The significance of ASDA arousals in children

Sleep disorders are common in children. The sleep disturbances associated with these disease processes may impact neurodevelopment and result in daytime behavioral and cognitive changes. Currently, there are no precise methods to accurately assess sleep disruption in the pediatric age group. There is evidence that American Sleep Disorders Association (ASDA) arousals are insufficient markers of sleep disruption in children. Other techniques that have been used to assess sleep disruption include unconventional means of evaluating the electroencephalogram (EEG) during sleep and evaluating subcortical or autonomic activation. The aim of this review is to discuss the application of conventional and unconventional markers of sleep disruption in children.

Cyclic alternating pattern sequences and non-cyclic alternating pattern periods in human sleep

OBJECTIVE

The CAP cycle is a module of activation (phase A) and inhibition (phase B) which repeats itself in sequences. The study aims at testing the hypothesis that the duration of CAP sequences is determined primarily by the number and not by the length of CAP cycles.

METHODS

The polysomnographic recordings of 24 normal subjects, 12 males and 12 females, ranging in age from 20 to 35 years (mean 27.8+/-7.2), were examined.

RESULTS

A total of 1053 CAP sequences were counted with an average of 43.9 sequences per night. The mean duration of CAP sequences was 2 min and 33 s. Each CAP sequence was composed of an average of 5.6 CAP cycles. All subjects presented CAP sequences lasting at least 5 min and 30s. The mean duration of CAP cycles was 26.9+/-4.1s. CAP cycles including subtypes A1 presented the highest correlation with the CAP sequence length (r=0.92; p<0.0001).

CONCLUSIONS

The progressive increase of CAP sequences length is linked to the progressive accumulation of CAP cycles.

SIGNIFICANCE

CAP sequences can be considered as strings of time-constant modules, i.e., CAP cycles, which are involved in the dynamic tailoring of sleep structure.

Cyclic alternating pattern (CAP) alterations in narcolepsy

BACKGROUND AND PURPOSE

Narcolepsy is a sleep disorder with clinical symptoms attributed to a reduced activation of the arousal system. Cyclic alternating pattern (CAP) is the expression of rhythmic arousability during non-rapid eye movement (NREM) sleep. CAP parameters, arousals and conventional sleep measures were studied in narcoleptic patients with cataplexy.

PATIENTS AND METHODS

Data were collected from all-night polysomnographic (PSG) recordings and the multiple sleep latency test (MSLT) on the intervening day of 25 drug-naive patients (10 males and 15 females; mean age: 34+/-16 years) after adaptation and exclusion of other sleep disorders. A group of 25 age- and gender-matched normal sleepers were selected as controls. Each PSG recording was subdivided into sleep cycles. Analysis of CAP included classification of A phases into subtypes A1, A2, and A3.

RESULTS

There was an increase in sleep period time mainly due to an increased wake time after sleep onset. REM latency was sharply reduced. The percentage of NREM sleep was slightly reduced and the balance between light sleep (S1+S2) and deep sleep (S3+S4) showed a curtailment of the former, while deep sleep was slightly increased. Excluding sleep cycles with sleep onset REM periods (SOREMPs), the duration of ordered sleep cycles was not different between narcoleptics and controls. The two groups showed similar values of arousal index, while CAP time, CAP rate, number of CAP cycles and of phase A subtypes (in particular subtypes A1) were significantly reduced in narcoleptic patients.

CONCLUSIONS

The reduced periods of CAP in narcoleptic NREM sleep could be the electroencephalographic (EEG) expression of a generally reduced arousability or an increased strength of sleep-promoting forces in the balance between sleep and arousal systems. This can explain some of the clinical correlates of the disorder, i.e. excessive sleepiness, short sleep latency and impaired attentive performances, even without any sign of arousal-induced sleep fragmentation.

Sleep cycling alternating pattern (CAP) expression is associated with hypersomnia and GH secretory pattern in Prader–Willi syndrome

BACKGROUND AND PURPOSE

Hypersomnia, sleep-disordered breathing and narcoleptic traits such as rapid eye movement (REM) sleep onset periods (SOREMPs) have been reported in Prader-Willi syndrome (PWS). In a group of young adult patients with genetically confirmed PWS we evaluated sleep and breathing polysomnographically, including cycling alternating pattern (CAP), and we analyzed the potential interacting role of sleep variables, sleep-related breathing abnormalities, hypersomnia, severity of illness variables and growth hormone (GH) secretory pattern.

PATIENTS AND METHODS

Eleven males and 7 females (mean age: 27.5+/-5.5 years) were submitted to a full night of complete polysomnography and the multiple sleep latency test (MSLT). GH secretory pattern was evaluated by a standard GH-releasing hormone plus arginine test. Sixteen non-obese healthy subjects without sleep disturbances were recruited as controls.

RESULTS

Compared to controls PWS patients showed reduced mean MSLT score (P<0.001), reduced mean latency of sleep (P=0.03), increased REM sleep periods (P=0.01), and increased mean CAP rate/non-rapid eye movement (NREM) (P<0.001). Only four PWS patients had apnea/hypopnea index (AHI)>or=10. Conversely, significant nocturnal oxygen desaturation was frequent (83% of patients) and independent from apneas or hypopneas. In the PWS group, CAP rate/NREM showed a significant negative correlation with MSLT score (P=0.02) independently from arousals, respiratory disturbance variables, severity of illness measured by Holm's score or body mass index (BMI). PWS patients with CAP expression characterized by higher proportion of A1 subtypes presented less severe GH deficiency (P=0.01).

CONCLUSIONS

Our study suggests a relationship between hypersomnia and CAP rate, and between CAP expression and GH secretory pattern in PWS, possibly reflecting underlying central dysfunctions.

CAP, epilepsy and motor events during sleep: the unifying role of arousal

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.

CAP and arousals are involved in the homeostatic and ultradian sleep processes

There is growing evidence that cyclic alternating pattern (CAP) and arousals are woven into the basic mechanisms of sleep regulation. In the present study, the overnight sleep cycles (SC) of 20 normal subjects were analyzed according to their stage composition, CAP rate, phase A subtypes and arousals. Individual SC were then divided into 10 normalized temporal epochs. CAP parameters and arousals were measured in each epoch and averaged in relation to the SC order. Subtypes A2 and A3 of CAP in non-rapid eye movement (NREM) sleep, and arousals, both in REM and NREM sleep when not coincident with a A2 or A3 phases, were lumped together as fast electroencephalographic (EEG) activities (FA). Subtypes A1 of CAP, characterized by slow EEG activities (SA), were analyzed separately. The time distribution of SA and FA was compared to the mathematical model of normal sleep structure including functions representing the homeostatic process S, the circadian process C, the ultradian process generating NREM/REM cycles and the slow wave activity (SWA) resulting from the interaction between homeostatic and ultradian processes. The relationship between SA and FA and the sleep-model components was evaluated by multiple regression analysis in which SA and FA were considered as dependent variables while the covariates were the process S, process C, SWA, REM-on and REM-off activities and their squared values. Regression was highly significant (P < 0.0001) for both SA and FA. SA were prevalent in the first three SC, and exhibited single or multiple peaks immediately before and in the final part of deep sleep (stages 3 + 4). The peaks of FA were delayed and prevailed during the pre-REM periods of light sleep (stages 1 + 2) and during REM sleep. SA showed an exponential decline across the successive SC, according to the homeostatic process. In contrast, the distribution of FA was not influenced by the order of SC, with periodic peaks of FA occurring before the onset of REM sleep, in accordance with the REM-on switch. The dynamics of CAP and arousals during sleep can be viewed as an intermediate level between cellular activities and macroscale EEG phenomena as they reflect the decay of the homeostatic process and the interaction between REM-off and REM-on mechanisms while are slightly influenced by circadian rhythm.

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.

Changes in cerebral and autonomic activity heralding periodic limb movements in sleep

BACKGROUND AND PURPOSE

Periodic limb movement disorder (PLMD) is frequently accompanied by awakenings or signs of EEG arousal. However, it is matter of debate whether EEG arousals trigger leg movements or both EEG arousal and leg movements are separate expressions of a common pathophysiological mechanism. Previous studies showed that cardiac and cerebral changes occur in association with periodic limb movements (PLMs), and that a combining increase in delta activity and in heart rate (HR) occurs before the onset of PLMs.

PATIENTS AND METHODS

This paper presents some preliminary data, obtained from a sample of 5 subjects with PLMD not associated to restless legs syndrome. To describe the temporal pattern of cardiac and EEG activities changes concomitant with PLMs in NREM sleep we used time frequency analysis technique.

RESULTS

PLM onset is heralded by a significant activation of HR and delta activity power, beginning 4.25 and 3 s respectively before PLMs onset, with PLMs onset and arousal onset falling together.

DISCUSSION

Delta and HR variations herald PLMs and activation of fast EEG frequencies. Such a stereotyped pattern is common in PLMs and in spontaneous or stimuli-induced arousals. Moreover a similar pattern seems to encompass the CAP phenomenon. The whole of these phenomena can be linked to the activity of a common brainstem system, which receives peripheral inputs, regulating the vascular, cardiac and respiratory activities and synchronizing them to cortical oscillations of EEG.