Polysomnographic analysis of arousal responses in obstructive sleep apnea syndrome by means of the cyclic alternating pattern

The dynamics of cyclic-periodic phenomena during non-rapid and rapid eye movement sleep

Sleep is a complex physiological state characterized by distinct stages, each exhibiting unique electroencephalographic patterns and physiological phenomena. Sleep research has unveiled the presence of intricate cyclic-periodic phenomena during both non-rapid eye movement and rapid eye movement sleep stages. These phenomena encompass a spectrum of rhythmic oscillations and periodic events, including cyclic alternating pattern, periodic leg movements during sleep, respiratory-related events such as apneas, and heart rate variability. This narrative review synthesizes empirical findings and theoretical frameworks to elucidate the dynamics, interplay and implications of cyclic-periodic phenomena within the context of sleep physiology. Furthermore, it invokes the clinical relevance of these phenomena in the diagnosis and management of sleep disorders.

Sleep apnea pathophysiology

OBJECTIVE

The purpose of this study is to examine the pathophysiology underlying sleep apnea (SA).

BACKGROUND

We consider several critical features of SA including the roles played by the ascending reticular activating system (ARAS) that controls vegetative functions and electroencephalographic findings associated with both SA and normal sleep. We evaluate this knowledge together with our current understanding of the anatomy, histology, and physiology of the mesencephalic trigeminal nucleus (MTN) and mechanisms that contribute directly to normal and disordered sleep. MTN neurons express γ-aminobutyric acid (GABA) receptors which activate them (make chlorine come out of the cells) and that can be activated by GABA released from the hypothalamic preoptic area.

METHOD

We reviewed the published literature focused on sleep apnea (SA) reported in Google Scholar, Scopus, and PubMed databases.

RESULTS

The MTN neurons respond to the hypothalamic GABA release by releasing glutamate that activates neurons in the ARAS. Based on these findings, we conclude that a dysfunctional MTN may be incapable of activating neurons in the ARAS, notably those in the parabrachial nucleus, and that this will ultimately lead to SA. Despite its name, obstructive sleep apnea (OSA) is not caused by an airway obstruction that prevents breathing.

CONCLUSIONS

While obstruction may contribute to the overall pathology, the primary factor involved in this scenario is the lack of neurotransmitters.

Sleep electroencephalography biomarkers of cognition in obstructive sleep apnea

Obstructive sleep apnea has been associated with cognitive impairment and may be linked to disorders of cognitive function. These associations may be a result of intermittent hypoxaemia, sleep fragmentation and changes in sleep microstructure in obstructive sleep apnea. Current clinical metrics of obstructive sleep apnea, such as the apnea-hypopnea index, are poor predictors of cognitive outcomes in obstructive sleep apnea. Sleep microstructure features, which can be identified on sleep electroencephalography of traditional overnight polysomnography, are increasingly being characterized in obstructive sleep apnea and may better predict cognitive outcomes. Here, we summarize the literature on several major sleep electroencephalography features (slow-wave activity, sleep spindles, K-complexes, cyclic alternating patterns, rapid eye movement sleep quantitative electroencephalography, odds ratio product) identified in obstructive sleep apnea. We will review the associations between these sleep electroencephalography features and cognition in obstructive sleep apnea, and examine how treatment of obstructive sleep apnea affects these associations. Lastly, evolving technologies in sleep electroencephalography analyses will also be discussed (e.g. high-density electroencephalography, machine learning) as potential predictors of cognitive function in obstructive sleep apnea.

Towards automatic EEG cyclic alternating pattern analysis: a systematic review

This study conducted a systematic review to determine the feasibility of automatic Cyclic Alternating Pattern (CAP) analysis. Specifically, this review followed the 2020 Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines to address the formulated research question: is automatic CAP analysis viable for clinical application? From the identified 1,280 articles, the review included 35 studies that proposed various methods for examining CAP, including the classification of A phase, their subtypes, or the CAP cycles. Three main trends were observed over time regarding A phase classification, starting with mathematical models or features classified with a tuned threshold, followed by using conventional machine learning models and, recently, deep learning models. Regarding the CAP cycle detection, it was observed that most studies employed a finite state machine to implement the CAP scoring rules, which depended on an initial A phase classifier, stressing the importance of developing suitable A phase detection models. The assessment of A-phase subtypes has proven challenging due to various approaches used in the state-of-the-art for their detection, ranging from multiclass models to creating a model for each subtype. The review provided a positive answer to the main research question, concluding that automatic CAP analysis can be reliably performed. The main recommended research agenda involves validating the proposed methodologies on larger datasets, including more subjects with sleep-related disorders, and providing the source code for independent confirmation.

The Contribution of Sleep Texture in the Characterization of Sleep Apnea

Obstructive sleep apnea (OSA) is multi-faceted world-wide-distributed disorder exerting deep effects on the sleeping brain. In the latest years, strong efforts have been dedicated to finding novel measures assessing the real impact and severity of the pathology, traditionally trivialized by the simplistic apnea/hypopnea index. Due to the unavoidable connection between OSA and sleep, we reviewed the key aspects linking the breathing disorder with sleep pathophysiology, focusing on the role of cyclic alternating pattern (CAP). Sleep structure, reflecting the degree of apnea-induced sleep instability, may provide topical information to stratify OSA severity and foresee some of its dangerous consequences such as excessive daytime sleepiness and cognitive deterioration. Machine learning approaches may reinforce our understanding of this complex multi-level pathology, supporting patients' phenotypization and easing in a more tailored approach for sleep apnea.

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.

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.

Sleep medicine: Practice, challenges and new frontiers

Sleep medicine is an ambitious cross-disciplinary challenge, requiring the mutual integration between complementary specialists in order to build a solid framework. Although knowledge in the sleep field is growing impressively thanks to technical and brain imaging support and through detailed clinic-epidemiologic observations, several topics are still dominated by outdated paradigms. In this review we explore the main novelties and gaps in the field of sleep medicine, assess the commonest sleep disturbances, provide advices for routine clinical practice and offer alternative insights and perspectives on the future of sleep research.

Multiple Time Series Fusion Based on LSTM: An Application to CAP A Phase Classification Using EEG

The Cyclic Alternating Pattern (CAP) is a periodic activity detected in the electroencephalogram (EEG) signals. This pattern was identified as a marker of unstable sleep with several possible clinical applications; however, there is a need to develop automatic methodologies to facilitate real-world applications based on CAP assessment. Therefore, a deep learning-based EEG channels' feature level fusion was proposed in this work and employed for the CAP A phase classification. Two optimization algorithms optimized the channel selection, fusion, and classification procedures. The developed methodologies were evaluated by fusing the information from multiple EEG channels for patients with nocturnal frontal lobe epilepsy and patients without neurological disorders. Results showed that both optimization algorithms selected a comparable structure with similar feature level fusion, consisting of three electroencephalogram channels (Fp2-F4, C4-A1, F4-C4), which is in line with the CAP protocol to ensure multiple channels' arousals for CAP detection. Moreover, the two optimized models reached an area under the receiver operating characteristic curve of 0.82, with average accuracy ranging from 77% to 79%, a result in the upper range of the specialist agreement and best state-of-the-art works, despite a challenging dataset. The proposed methodology also has the advantage of providing a fully automatic analysis without requiring any manual procedure. Ultimately, the models were revealed to be noise-resistant and resilient to multiple channel loss, being thus suitable for real-world application.

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%.

Non-rapid eye movement sleep instability in adults with epilepsy: a systematic review and meta-analysis of cyclic alternating pattern

STUDY OBJECTIVES

Epilepsy is characterized by disrupted sleep architecture. Studies on sleep macro- and microstructure revealed that patients with epilepsy experience disturbed rapid eye movement (REM) sleep; however, no consensus has been reached on non-REM (NREM) sleep changes. Cyclic alternating pattern (CAP) is a marker of sleep instability that occurs only during NREM sleep. This meta-analysis investigated CAP differences between patients with epilepsy and healthy controls.

METHODS

This study followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines in searching PubMed, Embase, and Cochrane Central database for studies comparing polysomnographic sleep microstructures between patients with epilepsy and healthy controls. A meta-analysis using a random-effects model was performed. We compared CAP rates, percentages of phase A1, A2, A3 subtypes, and phase B durations between patients with epilepsy and healthy controls.

RESULTS

A total of 11 studies, including 209 patients with epilepsy and 197 healthy controls, fulfilled the eligibility criteria. Compared with healthy controls, patients with epilepsy had significantly increased CAP rates and decreased A1 subtype percentages, and patients with sleep-related epilepsy had increased A3 subtype percentages. Subgroup analyses revealed that antiseizure medications (ASMs) decreased CAP rates and increased phase B durations but did not affect the microstates of phase A in patients with sleep-related epilepsy.

CONCLUSIONS

This meta-analysis detected statistically significant differences in CAP parameters between patients with epilepsy and healthy controls. Our findings suggest patients with epilepsy experience NREM sleep instability. ASMs treatment may decrease NREM instability but did not alter the microstates of phase A.

Periodic limb movements during sleep: a narrative review

Objective

Using narrative review techniques, this paper evaluates the evidence for separable underlying patho-mechanisms of periodic limb movements (PLMs) to separable PLM motor patterns and phenotypes, in order to elucidate potential new treatment modalities.

Background

Periodic limb movement disorder (PLMD) is estimated to occur in 5–8% of the paediatric population and 4–11% of the general adult population. Due to significant sleep fragmentation, PLMD can lead to functional impairment, including hyperactivity and delayed language development in children, and poor concentration and work performance in adults. Longitudinal data demonstrate that those with PLMD are at greater risk of depression and anxiety, and a 4-fold greater risk of developing dementia. PLMD has been extensively studied over the past two decades, and several key insights into the genetic, pathophysiological, and neural correlates have been proposed. Amongst these proposals is the concept of separable PLM phenotypes, proposed on the basis of nocturnal features such as the ratio of limb movements and distribution throughout the night. PLM phenotype and presentation, however, varies significantly depending on the scoring utilized and the nocturnal features examined, across age, and co-morbid clinical conditions. Furthermore, associations between these phenotypes with major neurologic and psychiatric disorders remain controversial.

Methods

In order to elucidate potential divergent biological pathways that may help clarify important new treatment modalities, this paper utilizes narrative review and evaluates the evidence linking PLM motor patterns and phenotypes with hypothesised underlying patho-mechanisms. Distinctive, underlying patho-mechanisms include: a pure motor mechanism originating in the spinal cord, iron deficiency, dopamine system dysfunction, thalamic glutamatergic hyperactivity, and a more cortical-subcortical interplay. In support of the latter hypothesis, PLM rhythmicity appears tightly linked to the microarchitecture of sleep, not dissimilarly to the apnoeic/hypopneic events seen in obstructive sleep apnea (OSA).

Conclusions

This review closes with a proposal for greater investigation into the identification of potential, divergent biological pathways. To do so would require prospective, multimodal imaging clinical studies which may delineate differential responses to treatment in restless legs syndrome (RLS) without PLMS and PLMS without RLS. This could pave the way toward important new treatment modalities.

Cyclic alternating patterns and arousals: what is relevant in obstructive sleep apnea? In Memoriam Mario Giovanni Terzano

PURPOSE OF REVIEW

To review main knowledges and gaps in the field of sleep microstructure, represented by the cyclic alternating pattern (CAP), in obstructive sleep apnea (OSA).

RECENT FINDINGS

The (electroencephalographic and autonomic) 'intensity' of arousals in OSA patients, measured through the metrics of CAP, correlate with OSA severity and with disease burden. Continuous positive airway pressure determines variations in sleep architecture (conventional parameters) and at the microstructural level, at different time points.

SUMMARY

CAP is not only an 'attractor' of arousals, but also organizes distribution of K-complexes and delta bursts in non-rapid eye movement sleep. Although attention is always concentrated on the A-phase of CAP, a crucial role is play by the phase B, which reflects a period of transient inhibition. Respiratory events in OSA are a typical example of phase B-associated condition, as they occur during the interval between successive A-phases. Accordingly sleep microstructure provides useful insights in the pathophysiology and estimation of OSA severity and may be exploited to follow-up treatment efficacy. In the complex relationship among sleep fragmentation, excessive daytime sleepiness, cognition and cardiovascular risk the CAP framework can offer an integrative perspective in a multidisciplinary scenario.

Cyclic Alternating Pattern Analysis in Periodic Leg Movements in Sleep in Patients With Obstructive Sleep Apnea Syndrome Before and After Positive Airway Pressure Treatment

PURPOSE

Cyclic alternating pattern (CAP) is known to increase in many conditions of sleep disruption and sleep disorders, including obstructive sleep apnea syndrome and periodic limb movements in sleep (PLMS). Periodic limb movements in sleep associated with obstructive sleep apnea syndrome may vanish after positive airway pressure treatment, may persist, or emerge at treatment night. Here, the authors aimed to investigate the underlying pathophysiology of nonvanishing, vanishing, or newly emergent PLMS.

METHODS

The authors designed a prospective study and included 10 patients with nonvanishing PLMS during positive airway pressure therapy, 10 patients with vanishing PLMS, 10 patients with newly emergent PLMS, and 10 patients without PLMS at both nights. The CAP analysis was performed in detail at diagnostic polysomnography recording and at positive airway pressure titration. The changes in CAP parameters were evaluated in regard to nonvanishing, vanishing, or newly emergent PLMS.

RESULTS

Periodic limb movements in sleep related to A1 subtype of CAP were observed to decrease under positive airway pressure titration more than PLMS related to A3 subtype of CAP. The A3 subtype of CAP was higher in patients with vanishing PLMS than those with newly emergent PLMS. The newly emergent PLMS were mostly related to A1 subtype of CAP compared with A3 subtype of CAP.

CONCLUSIONS

This study showed that vanishing, nonvanishing, or newly emerging PLMS may indeed represent different underlying pathophysiology. The authors suggest that organization of sleep and preservation of ultradian rhythms during titration may determine whether PLMS will be vanished or persist. Newly emergent PLMS may probably arise from a separate central generator by the activation of higher cortical areas.

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.

Cyclic alternating pattern in obstructive sleep apnea: A preliminary study

Obstructive sleep apnea is linked to cardiovascular disease, metabolic disorders and dementia. The precise nature of the association between respiratory events in obstructive sleep apnea, cortical or subcortical arousals, and cognitive, autonomic and oxidative stress consequences remains incompletely elucidated. Previous studies have aimed to understand the relationship between obstructive sleep apnea and arousal patterns, as defined by the cyclic alternating pattern, but results have been inconsistent, in part likely due to the presence of associated comorbidities. To better define this relationship, we analysed cyclic alternating patterns in patients with obstructive sleep apnea without any additional comorbidities. We identified 18 adult male, non-obese subjects with obstructive sleep apnea and no other comorbidities or medication history, who underwent whole-night electroencephalography and polysomnography. Cyclic alternating pattern analysis was performed and verified by certified somnologists. Pairwise linear regression analysis demonstrated an inverse relationship between obstructive sleep apnea severity and cyclic alternating pattern subtype A1, and a direct correlation with cyclic alternating pattern subtype A3. Cyclic alternating pattern subtypes A1 prevail in milder obstructive sleep apnea phenotype, whilst cyclic alternating pattern subtypes A2 and A3 overcome among moderate-to-severe obstructive sleep apnea patients. The milder obstructive sleep apnea group also presented higher sleep efficiency, and increased percentages of non-rapid eye movement stage 3 and rapid eye movement sleep, as well as longer cyclic alternating pattern sequences in N3, while severe obstructive sleep apnea patients spent more time in lighter sleep stages. These results imply/suggest a balance between cyclic alternating pattern's adaptive and maladaptive arousal processes in obstructive sleep apnea of differing severities. In milder obstructive sleep apnea (apnea-hypopnea index < 20), sleep continuity may be reinforced by cyclic alternating pattern subtype A1, whereas in more severe obstructive sleep apnea, decompensation of these sleep-stabilizing mechanisms may occur and more intrusive cyclic alternating pattern fluctuations disrupt sleep circuitry.

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.

Sleep scoring based on video-electroencephalography monitoring in an Epileptology Unit: Comparison with polysomnography

The aim of the study was to compare the performance of video- electroencephalography (EEG) monitoring and standard polysomnography for sleep scoring in an Epileptology Unit. We calculated the level of agreement between two methods of sleep scoring, using either 27-electrode video-EEG or polysomnography for 1 night in 22 patients admitted to our Epileptology Unit. Independent experts manually scored sleep using the American Academy of Sleep Medicine 2017 guidelines. We evaluated the number of sleep cycles and their distribution on hypnogram, total sleep time, sleep efficiency, sleep and rapid eye movement sleep-onset latency, wake after sleep-onset, and sleep stages. We then extracted sub-samples of recordings to examine the agreement in microarousal and rapid eye movement scoring. We used Bland and Altman plots and Cohen's kappa test to measure agreement. Bland and Altman plots showed at least 95% agreement for all studied sleep parameters with the exception of wake after sleep onset, where there was an 11 min difference. Cohen's kappa test showed an agreement for the recognition of microarousal (0.89) and of rapid eye movements (0.96) in sub-samples. Video-EEG represents an acceptable alternative tool for sleep architecture study in patients admitted to an Epileptology Unit.

Intensity of Respiratory Cortical Arousals Is a Distinct Pathophysiologic Feature and Is Associated with Disease Severity in Obstructive Sleep Apnea Patients

Background: We investigated whether the number, duration and intensity of respiratory arousals (RA) on C3-electroencephalographic (EEG) recordings correlate with polysomnography (PSG)-related disease severity in obstructive sleep apnea (OSA) patients. We also investigated if every patient might have an individual RA microstructure pattern, independent from OSA-severity. Methods: PSG recordings of 20 OSA patients (9 female; age 27–80 years) were analyzed retrospectively. Correlation coefficients were calculated between RA microstructure (duration, EEG-intensity) and RA number and respiratory disturbance index (RDI), oxygen desaturation index (ODI) and arousal index (AI). Intraclass correlations (ICC) for both RA duration and intensity were calculated. Sleep stage-specific and apnea- and hypopnea-specific analyses were also done. The probability distributions of duration and intensity were plotted, interpolated with a kernel which fits the distribution. A Bayesian posterior distribution analysis and pair-wise comparisons of each patient with all other 19 patients were performed. Results: Of the analyzed 2600 RA, strong positive correlations were found between average RA intensity and both RDI and AI. The number of PSG-recorded RA was strongly positively correlated with RDI. Significant correlations between average RA intensity in REM, NREM2 and NREM3 sleep stages and total ODI were identified. No sleep stage-specific correlations of arousal microstructure with age, sex, RDI or AI were identified. Although between-subjects ICC values were <0.25, within-subject ICC values were all >0.7 (all p < 0.05). While apnea-related RA duration did not differ from hypopnea-related RA duration, RA intensity was significantly higher (p = 0.00135) in hypopneas than in apneas. A clear individual pattern of arousal duration for each patient was made distinct. For arousal intensity, a Gaussian distribution was identified in most patients. The Bayesian statistics regarding the arousal microstructure showed significant differences between each pair of patients. Conclusions: Each individual patient with OSA might have an individual pattern of RA intensity and duration indicating a distinct individual pathophysiological feature. Arousal intensity was significantly higher in hypopneic than in apneic events and may be related causally to the diminished (compared to apneas) respiratory distress associated with hypopneas. RA intensity in REM, NREM2 and NREM3 strongly correlated with ODI.

Commonalities and Differences in NREM Parasomnias and Sleep-Related Epilepsy: Is There a Continuum Between the Two Conditions?

Introduction: Differential diagnosis between disorders of arousal (DoA) and sleep-related hypermotor epilepsy (SHE) often represents a clinical challenge. The two conditions may be indistinguishable from a semiological point of view and the scalp video-polysomnography is often uninformative. Both disorders are associated with variable hypermotor manifestations ranging from major events to fragments of a hierarchical continuum of increasing intensity, complexity, and duration. Given their semiological overlap we decided to explore the sleep texture of DoA and SHE seeking for similarities and differences.

Methods: We analyzed sleep macrostructure and CAP (cyclic alternating pattern) parameters in a cohort of 35 adult DoA patients, 40 SHE patients and 24 healthy sleepers, all recorded and scored in the same sleep laboratory. Nocturnal behavioral manifestations included minor motor events, paroxysmal arousals and major attacks in SHE, and simple, rising, or complex arousal movements in DoA.

Results: Compared to healthy controls, DoA and SHE showed similar amounts of sleep efficiency, light sleep, deep sleep, REM sleep, CAP subtypes. Both groups also showed slow wave sleep fragmentation and an increased representation of stage N3 in the second part of the night. The only discriminating elements between the two conditions regarded sleep length (more reduced in DoA) and sleep instability (more elevated in SHE). In DoA recordings, all motor episodes arose from NREM sleep: 37% during light NREM stages and 63% during stage N3 (simple arousal movements: 94%). In SHE recordings, 57% of major attacks occurred during stage N3.

Conclusions: So far, emphasis has been placed on the differentiation of sleep-related epilepsy and NREM arousal disorders. However, the impressive analogies between DoA and SHE suggest the existence of an underestimated continuum across the conditions, linked by increased levels of sleep instability, higher amounts of slow wave sleep and NREM/REM sleep imbalance. Sleep texture is extremely similar in the two conditions, although CAP metrics disclose quantitative differences. In particular, SHE patients show a higher arousal instability compared to DoA subjects. Given their clinical and epidemiological overlap, a common genetic background is also hypothesized. In such a perspective, we suggest that the consolidated dichotomy DoA vs. SHE should be reappraised.

Obstructive Sleep Apnea and Its Treatment in Aging: Effects on Alzheimer's disease Biomarkers, Cognition, Brain Structure and Neurophysiology

Here we review the impact of obstructive sleep apnea (OSA) on biomarkers of Alzheimer's disease (AD) pathogenesis, neuroanatomy, cognition and neurophysiology, and present the research investigating the effects of continuous positive airway pressure (CPAP) therapy. OSA is associated with an increase in AD markers amyloid-β and tau measured in cerebrospinal fluid (CSF), by Positron Emission Tomography (PET) and in blood serum. There is some evidence suggesting CPAP therapy normalizes AD biomarkers in CSF but since mechanisms for amyloid-β and tau production/clearance in humans are not completely understood, these findings remain preliminary. Deficits in the cognitive domains of attention, vigilance, memory and executive functioning are observed in OSA patients with the magnitude of impairment appearing stronger in younger people from clinical settings than in older community samples. Cognition improves with varying degrees after CPAP use, with the greatest effect seen for attention in middle age adults with more severe OSA and sleepiness. Paradigms in which encoding and retrieval of information are separated by periods of sleep with or without OSA have been done only rarely, but perhaps offer a better chance to understand cognitive effects of OSA than isolated daytime testing. In cognitively normal individuals, changes in EEG microstructure during sleep, particularly slow oscillations and spindles, are associated with biomarkers of AD, and measures of cognition and memory. Similar changes in EEG activity are reported in AD and OSA, such as "EEG slowing" during wake and REM sleep, and a degradation of NREM EEG microstructure. There is evidence that CPAP therapy partially reverses these changes but large longitudinal studies demonstrating this are lacking. A diagnostic definition of OSA relying solely on the Apnea Hypopnea Index (AHI) does not assist in understanding the high degree of inter-individual variation in daytime impairments related to OSA or response to CPAP therapy. We conclude by discussing conceptual challenges to a clinical trial of OSA treatment for AD prevention, including inclusion criteria for age, OSA severity, and associated symptoms, the need for a potentially long trial, defining relevant primary outcomes, and which treatments to target to optimize treatment adherence.

Variation in Sleep Stability with Differences in Severity of Sleep-Disordered Breathing in Children

OBJECTIVES

The aim of this study was to analyze the association between obstructive sleep apnea (OSA) severity and various cardiopulmonary coupling (CPC) parameters in children with OSA.

STUDY DESIGN

Retrospective cross-sectional study.

METHODS

A cross-sectional study was conducted among 117 children (aged 7.96 ± 3.54 years, 86 male) who underwent both full-night polysomnography (PSG) and CPC for suspicion of sleep-disordered breathing (SDB). We analyzed the association between various CPC and PSG findings.

RESULTS

The apnea-hypopnea index (AHI) was negatively correlated with high frequency coupling (HFC, r = -0.374, P < .001) and very low frequency coupling (VLFC, r = -0.192, P = .038) and positively correlated with low frequency coupling (LFC, r = 0.503, P < .001), elevated low frequency coupling (e-LFC, r = 0.475, P < .001), and narrow and broad band e-LFC (e-LFC_NB and e-LFC_BB ; r = 0.221, P = .016 and r = 0.468, P < .001, respectively). The arousal index was negatively correlated with HFC (r = - 0.466, P < .001) and positively correlated with LFC, e-LFC, e-LFC_NB , and e-LFC_BB (r = 0.543, r = 0.460, r = 0.239, and r = 0.445, respectively; all P < .001). In addition, we also found a significant difference in various CPC values according to OSA severity.

CONCLUSION

CPC parameters accurately reflect sleep fragmentation and OSA severity in children. Thus, we can verify objective sleep quality using CPC analysis, which is a simple method of analyzing sleep stability in children with SDB.

LEVEL OF EVIDENCE

4 Laryngoscope, 131:435-439, 2021.

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.

Significance of sleep stability using cardiopulmonary coupling in sleep disordered breathing

OBJECTIVES

The aim of this study was to assess the sleep quality and sleep stability according to the severity of obstructive sleep apnea (OSA) by using questionnaires and cardiopulmonary coupling (CPC) analysis.

METHODS

Two hundred and twenty-one adults were evaluated using subjective sleep questionnaires, CPC parameters, and respiratory parameters measured during full-night polysomnography. We measured the differences in the CPC parameters of each OSA group and the correlation between the apnea-hypopnea index (AHI) and CPC parameters.

RESULTS

AHI and CPC parameters were not associated with sleep questionnaires. AHI is negatively correlated with high frequency coupling (HFC, r = -0.725, P < 0.001) and very low frequency coupling (VLFC, r = -0.475, P < 0.001), but it is positively correlated with low frequency coupling (LFC, r = 0.786, P < 0.001) and narrow- and broadband-elevated low frequency coupling (e-LFC_NB and e-LFC_BB ; r = 0.522, P < 0.001 and r = 0.668, P < 0.001, respectively). We also found similar results regarding the correlation between the arousal index and CPC parameters. In addition, there were significant differences in HFC, LFC, VLFC, e-LFC_NB , and e-LFC_BB (all P < 0.001) between the severe OSA group and all other groups.

CONCLUSION

We found that CPC parameters significantly correlated with AHI. In addition, sleep stability-related parameters differ significantly based on OSA severity, and apneic parameters in the severe OSA group are significantly different from those in the other groups.

LEVEL OF EVIDENCE

4 Laryngoscope, 130: 2069-2075, 2020.

Sleep Macrostructure and NREM Sleep Instability Analysis in Pediatric Developmental Coordination Disorder

Developmental Coordination Disorder (DCD) is considered to be abnormal motor skills learning, identified by clumsiness, slowness, and/or motor inaccuracy impairing the daily-life activities in all ages of life, in the absence of sensory, cognitive, or neurological deficits impairment. The present research focuses on studying DCD sleep structure and Cyclic Alternating Pattern (CAP) parameters with a full overnight polysomnography and to study the putative correlations between sleep architecture and CAP parameters with motor coordination skills. The study was a cross-sectional design involving 42 children (26M/16F; mean age 10.12 ± 1.98) selected as a DCD group compared with 79 children (49M/30F; mean age 9.94 ± 2.84) identified as typical (no-DCD) for motor ability and sleep macrostructural parameters according to the MABC-2 and polysomnographic (PSG) evaluations. The two groups (DCD and non-DCD) were similar for age (p = 0.715) and gender (p = 0.854). More significant differences in sleep architecture and CAP parameters were found between two groups and significant correlations were identified between sleep parameters and motor coordination skills in the study population. In conclusion, our data show relevant abnormalities in sleep structure of DCD children and suggest a role for rapid components of A phases on motor coordination development

Cyclic Alternating Pattern in Obstructive Sleep Apnea Patients with versus without Excessive Sleepiness

Background

One of the main hypotheses on the development of daytime sleepiness (ES) is increased arousal in obstructive sleep apnea (OSA). Cyclic alternating pattern (CAP) is considered to be the main expression of sleep microstructure rather than arousal. Therefore, we aimed to investigate whether there is any difference between OSA patients with versus without ES in terms of the parameters of sleep macro- and microstructure and which variables are associated with Epworth Sleepiness Scale (ESS) score.

Methods

Thirty-eight male patients with moderate to severe OSA were divided into two subgroups by having been used to ESS as ES or non-ES.

Results

There was no difference between two groups in clinical characteristics and macrostructure parameters of sleep. However, ES group had significantly higher CAP rate, CAP duration, number of CAP cycles, and duration and rate of the subtypes A2 (p = 0.033, 0.019, 0.013, and 0.019, respectively) and lower mean phase B duration (p = 0.028) compared with non-ES group. In correlation analysis, ESS score was not correlated with any CAP measure.

Conclusions

OSA patients with ES have increased CAP measures rather than those without ES.

Sleep microstructure dynamics and neurocognitive performance in obstructive sleep apnea syndrome patients

The present study examined the relationship between the increment in cyclic alternating patterns (CAPs) in sleep electroencephalography and neurocognitive decline in obstructive Sleep Apnea Syndrome (OSAS) patients through source localization of the phase-A of CAPs. All-night polysomnographic recordings of 10 OSAS patients and 4 control subjects along with their cognitive profile using the Addenbrooke's Cognitive Examination (ACE) test were acquired. The neuropsychological assessment involved five key domains including attention and orientation, verbal fluency, memory, language and visuo-spatial skills. The standardized low-resolution brain electromagnetic tomography (sLORETA) tool was used to source-localize the phase-A of CAPs in sleep EEG aiming to investigate the correlation between CAP phase-A and cognitive functions. Our findings suggested a significant increase in CAP rates among OSAS subjects versus control subjects. Moreover, sLORETA revealed that CAP phase-A is mostly activated in frontoparietal cortices. As CAP rate increases, the activity of phase-A in such areas is dramatically enhanced leading to arousal instability, lower sleep efficiency and a possibly impaired cortical capacity to consolidate cognitive inputs in frontal and parietal areas during sleep. As such, cognitive domains including verbal fluency, memory and visuo-spatial skills which predominantly relate to frontoparietal areas tend to be affected. Based on our findings, CAP activity may possibly be considered as a predictor of cognitive decline among OSAS patients.

Identifying Phase-Amplitude Coupling in Cyclic Alternating Pattern using Masking Signals

Judiciously classifying phase-A subtypes in cyclic alternating pattern (CAP) is critical for investigating sleep dynamics. Phase-amplitude coupling (PAC), one of the representative forms of neural rhythmic interaction, is defined as the amplitude of high-frequency activities modulated by the phase of low-frequency oscillations. To examine PACs under more or less synchronized conditions, we propose a nonlinear approach, named the masking phase-amplitude coupling (MPAC), to quantify physiological interactions between high (α/lowβ) and low (δ) frequency bands. The results reveal that the coupling intensity is generally the highest in subtype A1 and lowest in A3. MPACs among various physiological conditions/disorders (p < 0.0001) and sleep stages (p < 0.0001 except S4) are tested. MPACs are found significantly stronger in light sleep than deep sleep (p < 0.0001). Physiological conditions/disorders show similar order in MPACs. Phase-amplitude dependence between δ and α/lowβ oscillations are examined as well. δ phase tent to phase-locked to α/lowβ amplitude in subtype A1 more than the rest. These results suggest that an elevated δ-α/lowβ MPACs can reflect some synchronization in CAP. Therefore, MPAC can be a potential tool to investigate neural interactions between different time scales, and δ-α/lowβ MPAC can serve as a feasible biomarker for sleep microstructure.

Sleep apnea: a review of diagnostic sensors, algorithms, and therapies

While public awareness of sleep related disorders is growing, sleep apnea syndrome (SAS) remains a public health and economic challenge. Over the last two decades, extensive controlled epidemiologic research has clarified the incidence, risk factors including the obesity epidemic, and global prevalence of obstructive sleep apnea (OSA), as well as establishing a growing body of literature linking OSA with cardiovascular morbidity, mortality, metabolic dysregulation, and neurocognitive impairment. The US Institute of Medicine Committee on Sleep Medicine estimates that 50-70 million US adults have sleep or wakefulness disorders. Furthermore, the American Academy of Sleep Medicine (AASM) estimates that more than 29 million US adults suffer from moderate to severe OSA, with an estimated 80% of those individuals living unaware and undiagnosed, contributing to more than $149.6 billion in healthcare and other costs in 2015. Although various devices have been used to measure physiological signals, detect apneic events, and help treat sleep apnea, significant opportunities remain to improve the quality, efficiency, and affordability of sleep apnea care. As our understanding of respiratory and neurophysiological signals and sleep apnea physiological mechanisms continues to grow, and our ability to detect and process biomedical signals improves, novel diagnostic and treatment modalities emerge.

OBJECTIVE

This article reviews the current engineering approaches for the detection and treatment of sleep apnea.

APPROACH

It discusses signal acquisition and processing, highlights the current nonsurgical and nonpharmacological treatments, and discusses potential new therapeutic approaches.

MAIN RESULTS

This work has led to an array of validated signal and sensor modalities for acquiring, storing and viewing sleep data; a broad class of computational and signal processing approaches to detect and classify SAS disease patterns; and a set of distinctive therapeutic technologies whose use cases span the continuum of disease severity.

SIGNIFICANCE

This review provides a current perspective of the classes of tools at hand, along with a sense of their relative strengths and areas for further improvement.

Quantitative sleep EEG and polysomnographic predictors of driving simulator performance in obstructive sleep apnea

OBJECTIVES

To improve identification of obstructive sleep apnea (OSA) patients at risk of driving impairment, this study explored predictors of driving performance impairment in untreated OSA patients using clinical PSG metrics, sleepiness questionnaires and quantitative EEG markers from routine sleep studies.

METHODS

Seventy-six OSA patients completed sleepiness questionnaires and driving simulator tests in the evening of their diagnostic sleep study. All sleep EEGs were subjected to quantitative power spectral analysis. Correlation and multivariate linear regression were used to identify the strongest predictors of driving simulator performance.

RESULTS

Absolute EEG spectral power across all frequencies (0.5-32 Hz) throughout the entire sleep period and separately in REM and NREM sleep, (r range 0.239-0.473, all p<0.05), as well as sleep onset latency (r=0.273, p<0.017) positively correlated with driving simulator steering deviation. Regression models revealed that amongst clinical and qEEG variables, the significant predictors of worse steering deviation were greater total EEG power during NREM and REM sleep, greater beta EEG power in NREM and greater delta EEG power in REM (range of variance explained 5-17%, t range 2.29-4.0, all p<0.05) and sleep onset latency (range of variance explained 4-9%, t range 2.15-2.5, all p<0.05).

CONCLUSIONS

In OSA patients, increased EEG power, especially in the faster frequency (beta) range during NREM sleep and slower frequency (delta) range in REM sleep were associated with worse driving performance, while no relationships were observed with clinical metrics e.g. apnea, arousal or oxygen indices.

SIGNIFICANCE

Quantitative EEG analysis in OSA may provide useful markers of driving impairment risk. Future studies are necessary to confirm these findings and assess the clinical significance of quantitative EEG as predictors of driving impairment in OSA.

An electrocardiogram-based analysis evaluating sleep quality in patients with obstructive sleep apnea

OBJECTIVE

The study compares polysomnography (PSG) and cardiopulmonary coupling (CPC) sleep quality variables in patients with (1) obstructive sleep apnea (OSA) and (2) successful and unsuccessful continuous positive airway pressure (CPAP) response.

PATIENTS/METHODS

PSGs from 50 subjects (32 F/18 M; mean age 48.4 ± 12.29 years; BMI 34.28 ± 9.33) were evaluated. OSA patients were grouped by no (n = 16), mild (n = 13), and moderate to severe (n = 20) OSA (apnea-hypopnea index (AHI) ≤ 5, >5-15, >15 events/h, respectively). Outcome sleep quality variables were sleep stages in non-rapid eye movement, rapid eye movement sleep, and high (HFC), low (LFC), very low-frequency coupling (VLFC), and elevated LFC broad band (e-LFCBB). An AHI ≤ 5 events/h and HFC ≥ 50 % indicated a successful CPAP response. CPC analysis extracts heart rate variability and QRS amplitude change that corresponds to respiration. CPC-generated spectrograms represent sleep dynamics from calculated coherence product and cross-power of both time series datasets.

RESULTS

T tests differentiated no and moderate to severe OSA groups by REM % (p = 0.003), HFC (p = 0.007), VLFC (p = 0.007), and LFC/HFC ratio (p = 0.038) variables. The successful CPAP therapy group (n = 16) had more HFC (p = 0.003), less LFC (p = 0.003), and e-LFCBB (p = 0.029) compared to the unsuccessful CPAP therapy group (n = 8). PSG sleep quality measures, except the higher arousal index (p = 0.038) in the unsuccessful CPAP group, did not differ between the successful and unsuccessful CPAP groups. HFC ≥ 50 % showed high sensitivity (77.8 %) and specificity (88.9 %) in identifying successful CPAP therapy.

CONCLUSIONS

PSG and CPC measures differentiated no from moderate to severe OSA groups and HFC ≥ 50 % discriminated successful from unsuccessful CPAP therapy. The HFC ≥ 50 % cutoff showed clinical value in identifying sleep quality disturbance among CPAP users.

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.

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.