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

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.

Development and assessment of a risk prediction model for moderate-to-severe obstructive sleep apnea

Background

OSA is an independent risk factor for several systemic diseases. Compared with mild OSA, patients with moderate-to-severe OSA have more severe impairment in the function of all organs of the body. Due to the current limited medical condition, not every patient can be diagnosed and treated in time. To enable timely screening of patients with moderate-to-severe OSA, we selected easily accessible variables to establish a risk prediction model.

Method

We collected 492 patients who had polysomnography (PSG), and divided them into the disease-free mild OSA group (control group), and the moderate-to-severe OSA group according to the PSG results. Variables entering the model were identified by random forest plots, univariate analysis, multicollinearity test, and binary logistic regression method. Nomogram were created based on the binary logistic results, and the area under the ROC curve was used to evaluate the discriminative properties of the nomogram model. Bootstrap method was used to internally validate the nomogram model, and calibration curves were plotted after 1,000 replicate sampling of the original data, and the accuracy of the model was evaluated using the Hosmer-Lemeshow goodness-of-fit test. Finally, we performed decision curve analysis (DCA) of nomogram model, STOP-Bang questionnaire (SBQ), and NoSAS score to assess clinical utility.

Results

There are 6 variables entering the final prediction model, namely BMI, Hypertension, Morning dry mouth, Suffocating awake at night, Witnessed apnea, and ESS total score. The AUC of this prediction model was 0.976 (95% CI: 0.962–0.990). Hosmer-Lemeshow goodness-of-fit test χ² = 3.3222 (P = 0.1899 > 0.05), and the calibration curve was in general agreement with the ideal curve. The model has good consistency in predicting the actual occurrence of moderate-to-severe risk, and has good prediction accuracy. The DCA shows that the net benefit of the nomogram model is higher than that of SBQ and NoSAS, with has good clinical utility.

Conclusion

The prediction model obtained in this study has good predictive power for moderate-to-severe OSA and is superior to other prediction models and questionnaires. It can be applied to the community population for screening and to the clinic for prioritization of treatment.

Causality of cortical and cardiovascular activity during cyclic alternating pattern in non-rapid eye movement sleep

The dynamic interplay between central and autonomic nervous system activities plays a pivotal role in orchestrating sleep. Macrostructural changes such as sleep-stage transitions or phasic, brief cortical events elicit fluctuations in neural outflow to the cardiovascular system, but the causal relationships between cortical and cardiovascular activities underpinning the microstructure of sleep are largely unknown. Here, we investigate cortical-cardiovascular interactions during the cyclic alternating pattern (CAP) of non-rapid eye movement sleep in a diverse set of overnight polysomnograms. We determine the Granger causality in both 507 CAP and 507 matched non-CAP sequences to assess the causal relationships between electroencephalography (EEG) frequency bands and respiratory and cardiovascular variables (heart period, respiratory period, pulse arrival time and pulse wave amplitude) during CAP. We observe a significantly stronger influence of delta activity on vascular variables during CAP sequences where slow, low-amplitude EEG activation phases (A1) dominate than during non-CAP sequences. We also show that rapid, high-amplitude EEG activation phases (A3) provoke a more pronounced change in autonomic activity than A1 and A2 phases. Our analysis provides the first evidence on the causal interplay between cortical and cardiovascular activities during CAP. Granger causality analysis may also be useful for probing the level of decoupling in sleep disorders. This article is part of the theme issue 'Advanced computation in cardiovascular physiology: new challenges and opportunities'.

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.

A systematic review and meta-analysis of the cyclic alternating pattern across the lifespan

BACKGROUND

Cyclic alternating pattern (CAP) is the electroencephalogram (EEG) pattern described as a marker of sleep instability and assessed by NREM transient episodes in sleep EEG. It has been associated with brain maturation. The aim of this review was to evaluate the normative data of CAP parameters according to the aging process in healthy subjects through a systematic review and meta-analysis.

METHODS

Two authors independently searched databases using PRISMA guidelines. Discrepancies were reconciled by a third reviewer. Subgroup analysis and tests for heterogeneity were conducted.

RESULTS

Of 286 studies, 10 submitted a total of 168 healthy individuals to CAP analysis. Scoring of CAP can begin at 3 months of life, when K-complexes, delta bursts, or spindles can be recognized. Rate of CAP increased with age, mainly during the first 2 years of life, then decreased in adolescence, and increased in the elderly. The A1 CAP subtype and CAP rate were high in school-aged children during slow-wave sleep (SWS). A1 CAP subtypes were significantly more numerous in adolescents compared with other groups, while the elderly showed the highest amounts of A2 and A3 CAP subtypes. Our meta-analysis registered the lowest CAP rate in infants younger than 2 years old and the highest in the elderly.

CONCLUSIONS

This review summarized the normative data of CAP in NREM sleep during the aging process. The CAP rate increased with age and sleep depth, especially during SWS. Parameters of CAP may reflect gender hormonal effects and neuroplasticity. More reports on CAP subtypes are needed for their reference values establishment.

Matrix of Lags: A tool for analysis of multiple dependent time series applied for CAP scoring

BACKGROUND

Multiple methods have been developed to assess what happens between and within time series. In a particular type of these series, the previous values of the currently observed series are contingent on the lagged values of another series. These cases can commonly be addressed by regression. However, a model selection criteria should be employed to evaluate the compromise between the amount of information provided and the model complexity. This is the basis for the development of the Matrix of Lags (MoL), a tool to study dependent time series.

METHODS

For each input, multiple regressions were applied to produce a model for each lag and a model selection criterion identifies the lags that will populate an auxiliary matrix. Afterwards, the energy of the lags (that are in the auxiliary matrix) was used to define a row of the MoL. Therefore, each input corresponds to a row of the MoL. To test the proposed tool, the heart rate variability and the electrocardiogram derived respiration were employed to perform the indirect estimation of the electroencephalography cyclic alternating pattern (CAP) cycles. Therefore, a support vector machine was fed with the MoL to perform the CAP cycle classification for each input signal. Multiple tests were carried out to further examine the proposed tool, including the effect of balancing the datasets, application of other regression methods and employment of two feature section models. The first was based on sequential backward selection while the second examined characteristics of a return map.

RESULTS

The best performance of the subject independent model was attained by feeding the lags, selected by sequential backward selection, to a support vector machine, achieving an average accuracy, sensitivity, specificity and area under the receiver operating characteristic curve of, respectively, 77%, 71%, 82% and 0.77.

CONCLUSIONS

The developed model allows to perform a measurement of a characteristic marker of sleep instability (the CAP cycle) and the results are in the upper bound of the specialist agreement range with visual analysis. Thus, the developed method could possibly be used for clinical diagnosis.

A Portable Wireless Device for Cyclic Alternating Pattern Estimation from an EEG Monopolar Derivation

Quality of sleep can be assessed by analyzing the cyclic alternating pattern, a long-lasting periodic activity that is composed of two alternate electroencephalogram patterns, which is considered to be a marker of sleep instability. Experts usually score this pattern through a visual examination of each one-second epoch of an electroencephalogram signal, a repetitive and time-consuming task that is prone to errors. To address these issues, a home monitoring device was developed for automatic scoring of the cyclic alternating pattern by analyzing the signal from one electroencephalogram derivation. Three classifiers, specifically, two recurrent networks (long short-term memory and gated recurrent unit) and one one-dimension convolutional neural network, were developed and tested to determine which was more suitable for the cyclic alternating pattern phase’s classification. It was verified that the network based on the long short-term memory attained the best results with an average accuracy, sensitivity, specificity and area under the receiver operating characteristic curve of, respectively, 76%, 75%, 77% and 0.752. The classified epochs were then fed to a finite state machine to determine the cyclic alternating pattern cycles and the performance metrics were 76%, 71%, 84% and 0.778, respectively. The performance achieved is in the higher bound of the experts’ expected agreement range and considerably higher than the inter-scorer agreement of multiple experts, implying the usability of the device developed for clinical analysis.

Isolated rapid eye movement sleep behavior disorder and cyclic alternating pattern: is sleep microstructure a predictive parameter of neurodegeneration?

Objective

To evaluate the role of sleep cyclic alternating pattern (CAP) in patients with isolated REM sleep behavior disorder (IRBD) and ascertain whether CAP metrics might represent a marker of phenoconversion to a defined neurodegenerative condition.

Methods

Sixty-seven IRBD patients were included and classified into patients who phenoconverted to a neurodegenerative disease (RBD converters: converter REM sleep behavior disorder [cRBD]; n = 34) and remained disease-free (RBD non-converters: non-converter REM sleep behavior disorder [ncRBD]; n = 33) having a similar follow-up duration. Fourteen age- and gender-balanced healthy controls were included for comparisons.

Results

Compared to controls, CAP rate and CAP index were significantly decreased in IRBD mainly due to a decrease of A1 phase subtypes (A1 index) despite an increase in duration of both CAP A and B phases. The cRBD group had significantly lower values of CAP rate and CAP index when compared with the ncRBD group and controls. A1 index was significantly reduced in both ncRBD and cRBD groups compared to controls. When compared to the ncRBD group, A3 index was significantly decreased in the cRBD group. The Kaplan-Meier curve applied to cRBD estimated that a value of CAP rate below 32.9% was related to an average risk of conversion of 9.2 years after baseline polysomnography.

Conclusion

IRBD is not exclusively a rapid eye movement (REM) sleep parasomnia, as non-rapid eye movement (non-REM) sleep microstructure can also be affected by CAP changes. Further studies are necessary to confirm that a reduction of specific CAP metrics is a marker of neurodegeneration in IRBD.

Correlation of sleep microstructure with daytime sleepiness and cognitive function in young and middle-aged adults with obstructive sleep apnea syndrome

PURPOSE

To compare microstructural features of sleep in young and middle-aged adults with differing severities of obstructive sleep apnea syndrome (OSAS), and to investigate the relationship between sleep microstructural fragmentation and cognitive impairment, as well as daytime sleepiness, in these patients.

METHODS

A total of 134 adults with snoring (mean age, 37.54 ± 7.66 years) were classified into four groups based on apnea-hypopnea index: primary snoring, mild OSAS, moderate OSAS, and severe OSAS. Overnight polysomnography was performed to assess respiratory, sleep macrostructure (N1, N2, N3, and R), and sleep microstructure (arousal, cyclic alternating pattern [CAP]) parameters. Cognitive function and daytime sleepiness were assessed using Montreal Cognitive Assessment (MoCA) and Epworth Sleepiness Scale (ESS).

RESULTS

As OSAS severity increased, MoCA gradually decreased and ESS gradually increased. N1%, N2%, and N3% sleep were significantly different between the severe OSAS group and the primary snoring, mild OSAS, and moderate OSAS groups (all P < 0.05). Overall arousal index, respiratory-related arousal index, CAP time, CAP rate, phase A index, number of CAP cycles, and phase A average time differed significantly in the moderate and severe OSAS groups compared with the mild OSAS and primary snoring groups (all P < 0.05). The strongest correlations identified by stepwise multiple regression analysis were between phase A3 index and the MoCA and ESS scores.

CONCLUSIONS

Sleep microstructure exhibited significant fragmentation in patients with moderate and severe OSAS, which was associated with decreased MoCA and increased ESS scores. This suggests that phase A3 index is a sensitive indicator of sleep fragmentation in OSAS.

Visual and automatic classification of the cyclic alternating pattern in electroencephalography during sleep

Cyclic alternating pattern (CAP) is a neurophysiological pattern that can be visually scored by international criteria. The aim of this study was to verify the feasibility of visual CAP scoring using only one channel of sleep electroencephalogram (EEG) to evaluate the inter-scorer agreement in a variety of recordings, and to compare agreement between visual scoring and automatic scoring systems. Sixteen hours of single-channel European data format recordings from four different sleep laboratories with either C4-A1 or C3-A2 channels and with different sampling frequencies were used in this study. Seven independent scorers applied visual scoring according to international criteria. Two automatic blind scorings were also evaluated. Event-based inter-scorer agreement analysis was performed. The pairwise inter-scorer agreement (PWISA) was between 55.5 and 84.3%. The average PWISA was above 60% for all scorers and the global average was 69.9%. Automatic scoring systems showed similar results to those of visual scoring. The study showed that CAP could be scored using only one EEG channel. Therefore, CAP scoring might also be integrated in sleep scoring features and automatic scoring systems having similar performances to visual sleep scoring systems.

Sleep, chronic pain, and opioid risk for apnea

Pain is an unwelcome sleep partner. Pain tends to erode sleep quality and alter the sleep restorative process in vulnerable patients. It can contribute to next-day sleepiness and fatigue, affecting cognitive function. Chronic pain and the use of opioid medications can also complicate the management of sleep disorders such as insomnia (difficulty falling and/or staying asleep) and sleep-disordered breathing (sleep apnea). Sleep problems can be related to various types of pain, including sleep headache (hypnic headache, cluster headache, migraine) and morning headache (transient tension type secondary to sleep apnea or to sleep bruxism or tooth grinding) as well as periodic limb movements (leg and arm dysesthesia with pain). Pain and sleep management strategies should be personalized to reflect the patient's history and ongoing complaints. Understanding the pain-sleep interaction requires assessments of: i) sleep quality, ii) potential contributions to fatigue, mood, and/or wake time functioning; iii) potential concomitant sleep-disordered breathing (SDB); and more importantly; iv) opioid use, as central apnea may occur in at-risk patients. Treatments include sleep hygiene advice, cognitive behavioral therapy, physical therapy, breathing devices (continuous positive airway pressure - CPAP, or oral appliance) and medications (sleep facilitators, e.g., zolpidem; or antidepressants, e.g., trazodone, duloxetine, or neuroleptics, e.g., pregabalin). In the presence of opioid-exacerbated SDB, if the dose cannot be reduced and normal breathing restored, servo-ventilation is a promising avenue that nevertheless requires close medical supervision.

Opportunities for utilizing polysomnography signals to characterize obstructive sleep apnea subtypes and severity

BACKGROUND

Obstructive sleep apnea (OSA) is a heterogeneous sleep disorder with many pathophysiological pathways to disease. Currently, the diagnosis and classification of OSA is based on the apnea-hypopnea index, which poorly correlates to underlying pathology and clinical consequences. A large number of in-laboratory sleep studies are performed around the world every year, already collecting an enormous amount of physiological data within an individual. Clinically, we have not yet fully taken advantage of this data, but combined with existing analytical approaches, we have the potential to transform the way OSA is managed within an individual patient. Currently, respiratory signals are used to count apneas and hypopneas, but patterns such as inspiratory flow signals can be used to predict optimal OSA treatment. Electrocardiographic data can reveal arrhythmias, but patterns such as heart rate variability can also be used to detect and classify OSA. Electroencephalography is used to score sleep stages and arousals, but specific patterns such as the odds-ratio product can be used to classify how OSA patients responds differently to arousals.

OBJECTIVE

In this review, we examine these and many other existing computer-aided polysomnography signal processing algorithms and how they can reflect an individual's manifestation of OSA.

SIGNIFICANCE

Together with current technological advance, it is only a matter of time before advanced automatic signal processing and analysis is widely applied to precision medicine of OSA in the clinical setting.

The AASM Scoring Manual four years later

PURPOSE OF REVIEW

Review published studies and critiques which evaluate the impact and effects of the American Academy of Sleep Medicine (AASM) Sleep Scoring Manual in the four years since its publication.

FINDINGS

USING THE AASM MANUAL RULES TO SCORE SLEEP AND EVENTS IN A POLYSOMNOGRAM (PSG) RESULTS IN: (1) very large differences in apnea-hypopnea indexes (AHI) when using the recommended and alternative rule for scoring hypopneas in adults; (2) increases in NREM 1 and sleep stage shifts with compensatory decreases in NREM 2 in children and adults when following rule 5.C.b. for ending NREM 2 sleep; (3) increases in NREM 3 in adults scoring slow wave activity in the frontal EEG derivations; (4) improved interscorer reliability; and (5) successfully identified fragmented sleep in children with obstructive sleep apnea (OSA) from primary snorers or normal controls because they had more NREM 1 and stage shifts using rule 5.C.b. Criticism of the Manual most often cited: (1) two rules for scoring hypopneas; (2) alternative EEG montage cancellation effects; (3) scoring stages 3 and 4 as NREM 3; and (4) too few rules for scoring arousals and REM sleep without atonia.

SUMMARY

Four years have passed since the AASM Scoring Manual was published with far less criticism than those who developed it feared. The AASM Manual provides a foundation upon which we all can build rules and methods which identify the complexity of sleep and its disorders.

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.

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.

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.

Progressive changes in cortical state before and after spontaneous arousals from sleep in elderly and middle-aged women

Arousals are often considered to be events which have an abrupt onset and offset, indicating abrupt changes in the state of the cortex. We hypothesized that cortical state, as reflected in electroencephalograph (EEG) signals, exhibits progressive systematic changes before and after a spontaneous, isolated arousal and that the time courses of the spectral components of the EEG before and after an arousal would differ between healthy middle-aged and elderly subjects. We analyzed the power spectrum and Sample Entropy of the C3A2 EEG before and after isolated arousals from 20 middle-aged (47.2±2.0 years) and 20 elderly (78.4±3.8 years) women using polysomnograms from the Sleep Heart Health Study database. In middle-aged women, all EEG spectral band powers <16 Hz exhibited a significant increase relative to baseline at some time in the 21 s before an arousal, but only low- (0.2-2.0 Hz) and high-frequency (2.0-4.0 Hz) delta increased in elderly and only during the last 7 s pre-arousal. Post-arousal, all frequency bands below 12 Hz transiently fell below pre-arousal baseline in both age groups. Consistent with these findings, Sample Entropy decreased steadily before an arousal, increased markedly during the arousal, and remained above pre-arousal baseline levels for ∼30 s after the arousal. In middle-aged, but not in elderly, women the presence of early pre-arousal low delta power was associated with shorter arousals. We propose that this attenuation of the effect of the arousing stimulus may be related to the slow (<1 Hz) cortical state oscillation, and that prolonged alterations of cortical state due to arousals may contribute to the poor correlation between indices of arousals and indices of sleepiness or impaired cognitive function.

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.