The cyclic alternating pattern plays a gate-control on periodic limb movements during non-rapid eye movement sleep

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.

Restless Legs Syndrome and Restless Sleep Disorder in Children

Restless legs syndrome (RLS) affects 2% of children presenting with symptoms of insomnia, restless sleep, decreased quality of life, and effects on cognition and behavior. The International RLS Study Group and the American Academy of Sleep Medicine have published guidelines for the diagnosis and treatment of RLS in children. Restless sleep disorder has been recently identified in children and presents with frequent movements during sleep and daytime symptoms with polysomnography findings of at least 5 large muscle movements at night. Treatment options for both disorders include iron supplementation, either oral or intravenous with improvement in nighttime and daytime symptoms.

Association between psychotropic medication and sleep microstructure: evidence from large population studies

STUDY OBJECTIVES

To assess the association between psychotropic medications and sleep microstructure in large community-based cohorts of older people.

METHODS

We analyzed overnight polysomnograms of 381 women from the Study of Osteoporotic Fractures (SOF) and 2,657 men from the Osteoporotic Fractures in Men Sleep Study (MrOS), who either used no psychotropic medication (n = 2,819), only benzodiazepines (n = 112), or only selective serotonin reuptake inhibitors (SSRI) (n = 107). Sleep microstructure (cyclic alternating pattern, CAP) was compared between the no medication group and psychotropic medication groups using the Mann-Whitney U test. Significant differences were investigated using multivariable linear regression adjusted for confounders.

RESULTS

CAP rate, arousal index, apnea-hypopnea index, and the frequency of slow, low-amplitude electroencephalography activation phases were significantly lower in MrOS participants using benzodiazepines than participants not taking psychotropic medication. SSRI users in MrOS experienced no altered sleep microstructure compared to those with no psychotropic use. SOF participants using benzodiazepines did not show similar associations with sleep microstructure. However, SSRI users from SOF had a significantly higher frequency of rapid, high-amplitude electroencephalography activation phases (A2 + 3) and periodic limb-movement index than participants not taking psychotropic medication. Multivariable linear regression adjusted for demographic, lifestyle, mood disorders, and health variables indicated additional significant associations between benzodiazepine usage and CAP rate and A2 + 3 index, respectively, in older men, and between CAP rate and SSRI usage in older women.

CONCLUSIONS

We identified significant associations between sleep microstructure and psychotropic drugs in MrOS and SOF, highlighting the importance of comprehensive sleep analysis, including CAP. Our results may improve understanding of the differences in sleep-wake mechanisms based on psychotropic usage.

CLINICAL TRIAL REGISTRATION

Registry: ClinicalTrials.gov; Title: Outcomes of Sleep Disorders in Older Men; Identifier: NCT00070681; URL: https://clinicaltrials.gov/ct2/show/record/NCT00070681.

CITATION

Hartmann S, Parrino L, Ensrud K, et al. Association between psychotropic medication and sleep microstructure: evidence from large population studies. J Clin Sleep Med. 2023;19(3):581-589.

MRASleepNet: a multi-resolution attention network for sleep stage classification using single-channel EEG

Objective. Computerized classification of sleep stages based on single-lead electroencephalography (EEG) signals is important, but still challenging. In this paper, we proposed a deep neural network called MRASleepNet for automatic sleep stage classification using single-channel EEG signals.Approach. The proposed MRASleepNet model consisted of a feature extraction (FE) module, a multi-resolution attention (MRA) module, and a gated multilayer perceptron (gMLP) module, as well as a direct pathway for computing statistical features. The FE, MRA, and gMLP modules were used to extract features, establish feature attention, and obtain temporal relationships between features, respectively. EEG signals were normalized and cut into 30 s segments, and enhanced by incorporating contextual information from adjacent data segments. After data enhancement, the 40 s data segments were input to the MRASleepNet model. The model was evaluated on the SleepEDF and the cyclic alternating pattern (CAP) databases, using such metrics as the accuracy, Kappa, and macro-F1 (MF1).Main results.For the SleepEDF-20 database, the proposed model had an accuracy of 84.5%, an MF1 of 0.789, and a Kappa of 0.786. For the SleepEDF-78 database, the model had an accuracy of 81.4%, an MF1 of 0.754, and a Kappa of 0.743. For the CAP database, the model had an accuracy of 74.3%, an MF1 of 0.656, and a Kappa of 0.652. The proposed model achieved satisfactory performance in automatic sleep stage classification tasks.Significance. The time- and frequency-domain features extracted by the FE module and filtered by the MRA module, together with the temporal features extracted by the gMLP module and the statistical features extracted by the statistical highway, enabled the proposed model to obtain a satisfying performance in sleep staging. The proposed MRASleepNet model may be used as a new deep learning method for automatic sleep stage classification. The code of MRASleepNet will be made available publicly onhttps://github.com/YuRui8879/.

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.

Restless Sleep Disorder (RSD): a New Sleep Disorder in Children. A Rapid Review

PURPOSE OF REVIEW

Restless sleep disorder (RSD) is a recently identified pediatric sleep disorder characterized by frequent movements during sleep associated with daytime symptoms. In this review we summarize the expanding evidence of the clinical presentation of RSD, potential pathophysiology, associated comorbidities, and current treatment options that will help the pediatrician identify children with RSD in a timely manner.

RECENT FINDINGS

RSD is diagnosed in 7.7% of children referred evaluated in a pediatric sleep center. Children with RSD present with frequent nightly movements during sleep for at least 3 months, and have daytime symptoms related to poor sleep quality including excessive sleepiness, hyperactivity, irritability among other symptoms. Current evidence shows an increased sympathetic predominance, increased NREM sleep instability, and iron deficiency, as well as increased prevalence in parasomnias and attention deficit hyperactivity disorder. Consensus diagnostic criteria were recently published to diagnose RSD and emergent evidence suggests that iron supplementation improves its nighttime and daytime symptoms.

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.

Automatic Cyclic Alternating Pattern (CAP) analysis: Local and multi-trace approaches

The Cyclic Alternating Pattern (CAP) is composed of cycles of two different electroencephalographic features: an activation A-phase followed by a B-phase representing the background activity. CAP is considered a physiological marker of sleep instability. Despite its informative nature, the clinical applications remain limited as CAP analysis is a time-consuming activity. In order to overcome this limit, several automatic detection methods were recently developed. In this paper, two new dimensions were investigated in the attempt to optimize novel, efficient and automatic detection algorithms: 1) many electroencephalographic leads were compared to identify the best local performance, and 2) the global contribution of the concurrent detection across several derivations to CAP identification. The developed algorithms were tested on 41 polysomnographic recordings from normal (n = 8) and pathological (n = 33) subjects. In comparison with the visual CAP analysis as the gold standard, the performance of each algorithm was evaluated. Locally, the detection on the F4-C4 derivation showed the best performance in comparison with all other leads, providing practical suggestions of electrode montage when a lean and minimally invasive approach is preferable. A further improvement in the detection was achieved by a multi-trace method, the Global Analysis-Common Events, to be applied when several recording derivations are available. Moreover, CAP time and CAP rate obtained with these algorithms positively correlated with the ones identified by the scorer. These preliminary findings support efficient automated ways for the evaluation of the sleep instability, generalizable to both normal and pathological subjects affected by different sleep disorders.

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.

A Review of Methods for Sleep Arousal Detection Using Polysomnographic Signals

Multiple types of sleep arousal account for a large proportion of the causes of sleep disorders. The detection of sleep arousals is very important for diagnosing sleep disorders and reducing the risk of further complications including heart disease and cognitive impairment. Sleep arousal scoring is manually completed by sleep experts by checking the recordings of several periods of sleep polysomnography (PSG), which is a time-consuming and tedious work. Therefore, the development of efficient, fast, and reliable automatic sleep arousal detection system from PSG may provide powerful help for clinicians. This paper reviews the automatic arousal detection methods in recent years, which are based on statistical rules and deep learning methods. For statistical detection methods, three important processes are typically involved, including preprocessing, feature extraction and classifier selection. For deep learning methods, different models are discussed by now, including convolution neural network (CNN), recurrent neural network (RNN), long-term and short-term memory neural network (LSTM), residual neural network (ResNet), and the combinations of these neural networks. The prediction results of these neural network models are close to the judgments of human experts, and these methods have shown robust generalization capabilities on different data sets. Therefore, we conclude that the deep neural network will be the main research method of automatic arousal detection in the future.

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.

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.

Polysomnographic nighttime features of narcolepsy: A systematic review and meta-analysis

Polysomnographic studies have been conducted to explore nighttime sleep features in narcolepsy, but their relationship to narcolepsy is still imperfectly understood. We conducted a systematic review of the literature exploring polysomnographic differences between narcolepsy patients and healthy controls (HCs) in EMBASE, MEDLINE, All EBM databases, CINAHL, and PsycINFO. 108 studies were identified for this review, 105 of which were used for meta-analysis. Meta-analyses revealed significant reductions in sleep latency, sleep efficiency, slow wave sleep percentage, rapid eye movement sleep (REM) latency, cyclic alternating pattern rate, and increases in total sleep time, wake time after sleep onset (WASO), awakening numbers (AWN) per hour, stage shift (SS) per hour, N1 percentage, apnea hypopnea index, and periodic limb movement index in narcolepsy patients compared with HCs. Furthermore, narcolepsy type 1 patients showed more disturbed nighttime sleep compared with narcolepsy type 2 patients. Children and adolescent narcolepsy patients show increased WASO, AWN, and SS compared with adult patients. Macro- and micro-structurally, our study suggests that narcolepsy patients have poor nighttime sleep. Sex, age, body mass index, disease duration, disease type, medication status, and adaptation night are demographic, clinical and methodological factors that contribute to heterogeneity between studies.

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

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

Sleep deprivation and its association with diseases- a review

Sleep deprivation, a consequence of multiple health problems or a cause of many major health risks, is a significant public health concern in this era. In the recent years, numerous reports have been added to the literature to provide explanation and to answer previously unanswered questions on this important topic but comprehensive updates and reviews in this aspect remain scarce. The present study identified 135 papers that investigated the association between sleep deprivation and health risks, including cardiovascular, respiratory, neurological, gastrointestinal, immunology, dermatology, endocrine, and reproductive health. In this review, we aimed to provide insight into the association between sleep deprivation and the development of diseases. We reviewed the latest updates available in the literature and particular attention was paid to reports that detailed all possible causal relationships involving both extrinsic and intrinsic factors that may be relevant to this topic. Various mechanisms by which sleep deprivation may affect health were presented and discussed, and this review hopes to serve as a platform for ideas generation for future research.

Reinventing polysomnography in the age of precision medicine

For almost 50 years, sleep laboratories around the world have been collecting massive amounts of polysomnographic (PSG) physiological data to diagnose sleep disorders, the majority of which are not utilized in the clinical setting. Only a small fraction of the information available within these signals is utilized to generate indices. For example, the apnea-hypopnea index (AHI) remains the primary tool for diagnostic and therapeutic decision-making for obstructive sleep apnea (OSA) despite repeated studies showing it to be inadequate in predicting clinical consequences. Today, there are many novel approaches to PSG signals, making it possible to extract more complex metrics and analyses that are potentially more clinically relevant for individual patients. However, the pathway to implement novel PSG metrics/analyses into routine clinical practice is unclear. Our goal with this review is to highlight some of the novel PSG metrics/analyses that are becoming available. We suggest that stronger academic-industry relationships would facilitate the development of state-of-the-art clinical research to establish the value of novel PSG metrics/analyses in clinical sleep medicine. Collectively, as a sleep community, it is time to reinvent how we utilize the polysomnography to move us towards Precision Sleep Medicine.

Polysomnographically measured sleep changes in idiopathic REM sleep behavior disorder: A systematic review and meta-analysis

Polysomnographic studies conducted to explore sleep changes in idiopathic rapid eye movement sleep behavior disorder (iRBD) have not established clear relationships between sleep disturbances and iRBD. To explore the polysomnographic differences between iRBD patients and healthy controls and their associated factors, an electronic literature search was conducted in EMBASE, MEDLINE, All EBM databases, CINAHL, and PsycINFO inception to December 2019.34 studies were identified for systematic review, 33 of which were used for meta-analysis. Meta-analyses revealed significant reductions in total sleep time (SMD = -0.212, 95%CI: -0.378 to -0.046), sleep efficiency (SMD = -0.194, 95%CI: -0.369 to -0.018), apnea hypopnea index (SMD = -0.440, 95%CI: -0.780 to -0.101), and increases in sleep latency (SMD = 0.340, 95%CI: 0.074 to 0.606), and slow wave sleep (SMD = 0.294, 95%CI: 0.064 to 0.523) in iRBD patients compared with controls. Furthermore, electroencephalogram frequency components during REM sleep were altered in iRBD patients compared with controls; however, the specific changes could not be determined. Our findings suggest that polysomnographic sleep is abnormal in iRBD patients. Further studies are needed on underlying mechanisms and associations with neurodegenerative diseases.

Improvement of Parasomnias After Treatment of Restless Leg Syndrome/ Periodic Limb Movement Disorder in Children

STUDY OBJECTIVES

Previous studies have shown that non-rapid eye movement (NREM) sleep parasomnias commonly coexist with restless legs syndrome (RLS) and periodic limb movement disorder (PLMD) in children, leading to speculation that RLS/PLMD may precipitate or worsen parasomnias. However, there are limited data about the effect of the treatment of RLS/PLMD on parasomnias in children. Hence, we performed this study to determine whether the treatment of RLS/PLMD with oral iron therapy is associated with improvement of parasomnias in children.

METHODS

A retrospective database was created for children with RLS/PLMD who were treated with iron therapy. These participants were followed for at least 1 year at Cincinnati Children's Hospital Medical Center. All participants had ferritin level testing and were treated with iron therapy. In addition, all participants underwent polysomnography before starting iron therapy for RLS/PLMD except for one participant who was already on iron but required a higher dose. Most participants underwent polysomnography after iron therapy.

RESULTS

A total of 226 participants were identified with the diagnosis of RLS/PLMD. Of these, 50 had parasomnias and 30 of them were treated with iron therapy. Of the 30 participants, RLS symptoms improved in 15 participants (50%) and resolution of parasomnias was noted in 12 participants (40%) participants after iron therapy. Repeat polysomnography after iron therapy was performed in 21 participants (70%). After iron therapy, there was a significant decrease in periodic limb movement index (17.2 ± 8.8 [before] versus 6.7 ± 7.3 [after] events/h, P < .001). In addition, there were significant decreases in PLMS (24.52 ± 9.42 [before] versus 7.50 ± 7.18 [after] events/h, P < .0001), PLMS-related arousals (4.71 ± 1.81 [before] versus 1.35 ± 1.43 [after] events/h, P < .0001), and total arousals (11.65 ± 5.49 [before] versus 8.94 ± 3.65 [after] events/h, P < .01) after iron therapy.

CONCLUSIONS

Parasomnias are common in our cohort of children with RLS/PLMD. Iron therapy was associated with a significant improvement in periodic limb movement index, RLS symptoms, and resolution of a significant proportion of NREM sleep parasomnias, suggesting that RLS/PLMD may precipitate NREM sleep parasomnia.

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.

EEG Functional Connectivity Prior to Sleepwalking: Evidence of Interplay Between Sleep and Wakefulness

Study Objectives

Although sleepwalking (somnambulism) affects up to 4% of adults, its pathophysiology remains poorly understood. Sleepwalking can be preceded by fluctuations in slow-wave sleep EEG signals, but the significance of these pre-episode changes remains unknown and methods based on EEG functional connectivity have yet to be used to better comprehend the disorder.

Methods

We investigated the sleep EEG of 27 adult sleepwalkers (mean age: 29 ± 7.6 years) who experienced a somnambulistic episode during slow-wave sleep. The 20-second segment of sleep EEG immediately preceding each patient's episode was compared with the 20-second segment occurring 2 minutes prior to episode onset.

Results

Results from spectral analyses revealed increased delta and theta spectral power in the 20 seconds preceding the episodes' onset as compared to the 20 seconds occurring 2 minutes before the episodes. The imaginary part of the coherence immediately prior to episode onset revealed (1) decreased delta EEG functional connectivity in parietal and occipital regions, (2) increased alpha connectivity over a fronto-parietal network, and (3) increased beta connectivity involving symmetric inter-hemispheric networks implicating frontotemporal, parietal and occipital areas.

Conclusions

Taken together, these modifications in EEG functional connectivity suggest that somnambulistic episodes are preceded by brain processes characterized by the co-existence of arousal and deep sleep.

Age and severity matched comparison of gender differences in the prevalence of periodic limb movements during sleep in patients with obstructive sleep apnea

PURPOSE

The aim of this study was to investigate gender differences of periodic limb movements during sleep (PLMS) in patients with obstructive sleep apnea (OSA).

METHODS

This was a case-control study recruiting 364 patients with OSA (182 men, 182 women) matched for age and apnea-hypopnea index (AHI). All participants underwent overnight polysomnography (PSG), followed by the multiple sleep latency test (MSLT) and the Epworth Sleepiness Scale (ESS).

RESULTS

Women with OSA had a significantly higher prevalence of PLMS than men (24.2 vs. 15.9 %, p < 0.05). Women with OSA showed an increased prevalence of PLMS compared to men in the younger group aged ≤55 years (23.0 vs. 10.6 %, p < 0.05), but not in the older groups >55 years (25.3 vs. 21.6 %, p > 0.05). Binary linear regression analysis in OSA patients confirmed that women were more likely to have PLMS than men (OR 1.71, 95 % CI 1.00-2.92), particularly in patients with age ≤55 years old (OR 2.48, 95 % CI 1.06-5.79), after adjusting for age, BMI, AHI, and habits of smoking and drinking.

CONCLUSIONS

The results demonstrate that, for patients with OSA, young women had significantly increased prevalence of PLMS compared to young men, but there was no difference in prevalence of PLMS between the men and women in the older age group.

An Evidence-based Analysis of the Association between Periodic Leg Movements during Sleep and Arousals in Restless Legs Syndrome

STUDY OBJECTIVES

To analyze statistically the association between periodic leg movements during sleep (PLMS) and arousals, in order to eventually support or challenge the current scoring rules and to further understand their reciprocal influence.

SETTING

Sleep research center.

PATIENTS

Twenty untreated consecutive patients with restless legs syndrome (RLS) (13 women and 7 males, mean age 60.9 y).

METHODS

In each recording, we selected all PLMS/arousal pairs that met the following inclusion criteria: (a) PLMS events that were separated from another PLMS event (preceding or following) by at least 10 s of EMG inactivity; (b) arousal events separated from another arousal event (preceding or following) by at least 10 s of stable EEG baseline activity; (c) PLMS/arousal pairs were then selected among events identified according to the previous two criteria, when PLMS and arousals were separated (offset-to-onset) by no more than 10 s, regardless of which was first.

MEASUREMENTS AND RESULTS

We selected a mean of 46.1 (SD 25.55) PLMS/arousal pairs per subject; in these pairs, average PLMS duration was 3.2 s (0.65) and average arousal duration was 6.5 s (0.92). Within these event pairs, the great majority (on average 98.4%, SD 3.88) was separated by less than 0.5 s (i.e., between the end of one event and the onset of the other, regardless of which was first). Arousal onsets preceded PLMS onset in 41.2% of pairs, while the opposite was true for the remaining 58.8% of pairs. A significant correlation between PLMS duration and arousal duration was also found (r = 0.447, P < 0.000001).

CONCLUSION

The results of this study support the current rule for the definition of the association between periodic leg movements during sleep (PLMS) and arousals. The tight time relationship between PLMS and arousals and their correlated durations seem to indicate that both events might be regulated by a complex mechanism, rather than being connected by a simple reciprocal cause/effect relationship.

Distinctive patterns of cortical excitability to transcranial magnetic stimulation in obstructive sleep apnea syndrome, restless legs syndrome, insomnia, and sleep deprivation

Altered responses to transcranial magnetic stimulation (TMS) in obstructive sleep apnea syndrome (OSAS), restless legs syndrome (RLS), insomnia, and sleep-deprived healthy subjects have been reported. We have reviewed the relevant literature in order to identify eventual distinctive electrocortical profiles based on single and paired-pulse TMS, sensorimotor modulation, plasticity-related and repetitive TMS measures. Although obtained from heterogeneous studies, the detected changes might be the result of the different pathophysiological substrates underlying OSAS, RLS, insomnia and sleep deprivation rather than reflect the general effect of non-specific sleep loss and instability. OSAS tends to exhibit an increased motor cortex inhibition, which is reduced in RLS; intracortical excitability seems to be in favor of an "activating" profile in chronic insomnia and in sleep-deprived healthy individuals. Abnormal plasticity-related TMS phenomena have been demonstrated in OSAS and RLS. This review provides a perspective of TMS techniques by further understanding the role of neurotransmission pathways and plastic remodeling of neuronal networks involved in common sleep disorders. TMS might be considered a valuable tool in the assessment of sleep disorders, the evaluation of the effect of therapy and the design of non-pharmacological approaches.

Louis EK, Duwell EJ, Timm PC, Sandness DJ, Boeve BF, Silber MH. Diagnostic thresholds for quantitative REM sleep phasic burst duration, phasic and tonic muscle activity, and REM atonia index in REM sleep behavior disorder with and without comorbid obstructive sleep apnea

OBJECTIVES

We aimed to determine whether phasic burst duration and conventional REM sleep without atonia (RSWA) methods could accurately diagnose REM sleep behavior disorder (RBD) patients with comorbid OSA.

DESIGN

We visually analyzed RSWA phasic burst durations, phasic, "any," and tonic muscle activity by 3-s mini-epochs, phasic activity by 30-s (AASM rules) epochs, and conducted automated REM atonia index (RAI) analysis. Group RSWA metrics were analyzed and regression models fit, with receiver operating characteristic (ROC) curves determining the best diagnostic cutoff thresholds for RBD. Both split-night and full-night polysomnographic studies were analyzed.

SETTING

N/A.

PARTICIPANTS

Parkinson disease (PD)-RBD (n = 20) and matched controls with (n = 20) and without (n = 20) OSA.

INTERVENTIONS

N/A.

MEASUREMENTS AND RESULTS

All mean RSWA phasic burst durations and muscle activities were higher in PD-RBD patients than controls (P < 0.0001), and RSWA associations with PD-RBD remained significant when adjusting for age, gender, and REM AHI (P < 0.0001). RSWA muscle activity (phasic, "any") cutoffs for 3-s mini-epoch scorings were submentalis (SM) (15.5%, 21.6%), anterior tibialis (AT) (30.2%, 30.2%), and combined SM/AT (37.9%, 43.4%). Diagnostic cutoffs for 30-s epochs (AASM criteria) were SM 2.8%, AT 11.3%, and combined SM/AT 34.7%. Tonic muscle activity cutoff of 1.2% was 100% sensitive and specific, while RAI (SM) cutoff was 0.88. Phasic muscle burst duration cutoffs were: SM (0.65) and AT (0.79) seconds. Combining phasic burst durations with RSWA muscle activity improved sensitivity and specificity of RBD diagnosis.

CONCLUSIONS

This study provides evidence for REM sleep without atonia diagnostic thresholds applicable in Parkinson disease-REM sleep behavior disorder (PD-RBD) patient populations with comorbid OSA that may be useful toward distinguishing PD-RBD in typical outpatient populations.

Topiramate-induced periodic limb movement disorder in a patient affected by focal epilepsy

Periodic limb movement disorder (PLMD) is characterized by pathological periodic limb movements during sleep, insomnia and/or diurnal sleepiness, and the absence of another primary sleep disorder. We report a patient with complex partial seizures who developed PLMD while taking topiramate (TPM). He had no evidence of metabolic and/or other conditions inducing PLMD. He also had fragmented sleep and disruptive PLMS on polysomnography, and PLMS subsided with change of antiepileptic drug. Topiramate may modulate the dopaminergic pathway by inhibition of glutamate release, thereby inducing PLMD as observed in our patient. Although a single case does not allow any generalization, PLMD should be considered in patients complaining of insomnia and treated with TPM.

Association between heart rate variability, blood pressure and autonomic activity in cyclic alternating pattern during sleep

STUDY OBJECTIVES

Cyclic alternating pattern (CAP) is frequently followed by changes in heart rate (HR) and blood pressure (BP), but the sequential associations between CAP and autonomic nerve activity have not been studied. The study aimed to reveal the precise changes in heart rate variability (HRV) during phase A of the CAP cycle.

DESIGN

Polysomnography was recorded according to the CAP Atlas (Terzano, 2002), and BP and electrocardiogram were simultaneously recorded. The complex demodulation method was used for analysis of HRV and evaluation of autonomic nerve activity.

SETTING

Academic sleep laboratory.

PARTICIPANTS

Ten healthy males.

MEASUREMENTS AND RESULTS

The increase in HR (median [first quartile - third quartile]) for each subtype was as follows: A1, 0.64 (-0.30 to 1.69), A2, 1.44 (0.02 to 3.79), and A3, 6.24 (2.53 to 10.76) bpm (A1 vs. A2 P < 0.001, A1 vs. A3 P < 0.001, A2 vs. A3 P < 0.001). The increase in BP for each subtype was as follows: A1, 1.23 (-2.04 to 5.75), A2, 1.76 (-1.46 to 9.32), and A3, 12.51 (4.75 to 19.94) mm Hg (A1 vs. A2 P = 0.249, A1 vs. A3 P < 0.001, A2 vs. A3 P < 0.001). In all of phase A, the peak values for HR and BP appeared at 4.2 (3.5 to 5.4) and 8.4 (7.0 to 10.3) seconds, respectively, after the onset of phase A. The area under the curve for low-frequency and high-frequency amplitude significantly increased after the onset of CAP phase A (P < 0.001) and was higher in the order of subtype A3, A2, and A1 (P < 0.001).

CONCLUSIONS

All phase A subtypes were accompanied with increased heart rate variability, and the largest heart rate variability was seen in subtype A3, while a tendency for less heart rate variability was seen in subtype A1.

Change in heart rate variability precedes the occurrence of periodic leg movements during sleep: an observational study

BACKGROUND

Several reports have described that individual periodic leg movements during sleep (PLMS) activities are associated with autonomic nervous system activity occurring shortly before each PLMS activity. Nevertheless, no study has investigated dynamic changes of autonomic nervous system activity before the onset of PLMS. This study detected changes in heart rate variability (HRV) at the onset of the period with PLMS using complex demodulation method.

METHODS

This study enrolled 14 patients diagnosed as having idiopathic PLMS disorder (PLMD). In periods with and without PLMS during sleep stage 2, HRV-related variables and the spectral power of fluctuation of a high frequency (HF) band (FHFB) were analyzed and compared. The changes of those parameters during transition from the period without PLMS to that with PLMS were explored.

RESULTS

Spectral power in the low frequency (LF) band and very low frequency (VLF) band were higher in the period with PLMS. Additionally, the average frequency in FHFB was higher. The frequency in this band fluctuated during the period with PLMS with remarkable elevation of FHFB. Moreover, spectral powers in FHFB, LF, and VLF were remarkably elevated shortly before the beginning of the period with PLMS (FHFB, -65 s; LF, -53 s; and VLF, -45 s).

CONCLUSIONS

Elevation of sympathetic nervous system activity and mean frequency fluctuation in an HF band can occur several tens of seconds before the period with PLMS. Dynamic changes in the autonomic nervous system activity might be related to the vulnerability to PLMS occurrence during the night.

Periodic Limb Movements during Sleep: A New Sleep-Related Cardiovascular Risk Factor?

In recent years, a growing body of evidence suggests that periodic limb movements during sleep (PLMS) are associated with hypertension, cardiovascular, and cerebrovascular risk. However, several non-mutually exclusive mechanisms may determine a higher cardiovascular risk in patients with PLMS and the link between the two remains controversial. Prospective data are scant and the temporal relationship between PLMS and acute vascular events is difficult to ascertain because although PLMS may lead to acute vascular events such as stroke, stroke may also give rise to PLMS. This article describes the clinical and polygraphic features of PLMS and examines the literature evidence linking PLMS with an increased risk for the development and progression of cardiovascular diseases, discussing the possible pathways of this association.

EEG segmentation for improving automatic CAP detection

OBJECTIVE

The aim of this study is to provide an improved method for the automatic classification of the Cyclic Alternating Pattern (CAP) sleep by applying a segmentation technique to the computation of descriptors from the EEG.

METHODS

A dataset of 16 polysomnographic recordings from healthy subjects was employed, and the EEG traces underwent first an automatic isolation of NREM sleep portions by means of an Artificial Neural Network and then a segmentation process based on the Spectral Error Measure. The information content of the descriptors was evaluated by means of ROC curves and compared with that of descriptors obtained without the use of segmentation. Finally, the descriptors were used to train a discriminant function for the automatic classification of CAP phases A.

RESULTS

A significant improvement with respect to previous scoring methods in terms of both information content carried by the descriptors and accuracy of the classification was obtained.

CONCLUSIONS

EEG segmentation proves to be a useful step in the computation of descriptors for CAP scoring.

SIGNIFICANCE

This study provides a complete method for CAP analysis, which is entirely automatic and allows the recognition of A phases with a high accuracy thanks to EEG segmentation.

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.

Prevalence of restless legs syndrome in Japanese patients with chronic liver disease

AIM

  Sleep disturbance is a major complication in patients with chronic liver disease, but causes are unclear. The aim of this study was to clarify the prevalence of restless legs syndrome (RLS) in Japanese chronic liver disease patients and investigate the influence on sleep and quality of life.

METHODS

  The study included 149 consecutive outpatients with chronic liver disease at Nagasaki University Hospital between September 2008 and March 2010. The presence of RLS was evaluated by a written survey using the questionnaire for the epidemiological surveillance of the international RLS research group in 2003. In addition, 89 cases, including all RLS patients, were evaluated for sleep quality and health-related quality of life. Sleep quality was evaluated by using the Japanese version of the Pittsburgh Sleep Quality Index (PSQI), and health-related quality of life was evaluated by the Japanese SF-36 Health Survey.

RESULT

  Twenty-five of the 149 patients (16.8%) fulfilled the diagnostic criteria for RLS. The median global PSQI score of the RLS group was significantly higher than the non-RLS group (9 vs 5, P < 0.01). The number of poor sleepers (global PSQI score, >5) in the RLS group was significantly higher than in the non-RLS group (P < 0.05). In SF-36, the mental component summary score of the RLS group was 43.8 ± 10.8, which was significantly lower than the non-RSL group (49.8 ± 10.5; P < 0.05).

CONCLUSION

  This is the first report that clarifies the prevalence of RLS in Japanese chronic liver disease patients. RLS worsens quality of sleep and life in chronic liver disease patients.

Does Restless Legs Syndrome increase cardiovascular risk in Attention-Deficit/Hyperactivity Disorder?

Preliminary evidence suggests a possible association between Attention-Deficit/Hyperactivity Disorder and Restless Legs Syndrome with or without Periodic Limb Movements during Sleep. When comorbid, Restless Legs Syndrome/Periodic Limb Movements during Sleep might aggravate Attention-Deficit/Hyperactivity Disorder symptoms. Pharmacological treatment of Attention-Deficit/Hyperactivity Disorder may be associated, at least in some cases, with adverse cardiovascular events, including clinically significant elevation in heart rate and systemic blood pressure. However, the characteristics of patients with Attention-Deficit/Hyperactivity Disorder at risk for cardiovascular events during pharmacological treatment are poorly understood. Here, we hypothesize that Restless Legs Syndrome and/or Periodic Limb Movements during Sleep comorbid with Attention-Deficit/Hyperactivity Disorder increase cardiovascular risk via imbalance in activity of the autonomic nervous system. Such an imbalance of the could be related to alterations of sleep microarchitecture also detected by cyclic alternating pattern analysis. If empirical studies confirm our hypothesis, the clinician would be advised to systematically screen for and effectively treat Restless Legs Syndrome/Periodic Limb Movements during Sleep even before starting treatment with Attention-Deficit/Hyperactivity Disorder drugs. The management of Restless Legs Syndrome/Periodic Limb Movements during Sleep might reduce cardiovascular risk during pharmacological treatment of Attention-Deficit/Hyperactivity Disorder.

Cyclic alternating pattern is associated with cerebral hemodynamic variation: a near-infrared spectroscopy study of sleep in healthy humans

The cyclic alternating pattern (CAP), that is, cyclic variation of brain activity within non-REM sleep stages, is related to sleep instability and preservation, as well as consolidation of learning. Unlike the well-known electrical activity of CAP, its cerebral hemodynamic counterpart has not been assessed in healthy subjects so far. We recorded scalp and cortical hemodynamics with near-infrared spectroscopy on the forehead and systemic hemodynamics (heart rate and amplitude of the photoplethysmograph) with a finger pulse oximeter during 23 nights in 11 subjects. Electrical CAP activity was recorded with a polysomnogram. CAP was related to changes in scalp, cortical, and systemic hemodynamic signals that resembled the ones seen in arousal. Due to their repetitive nature, CAP sequences manifested as low- and very-low-frequency oscillations in the hemodynamic signals. The subtype A3+B showed the strongest hemodynamic changes. A transient hypoxia occurred during CAP cycles, suggesting that an increased CAP rate, especially with the subtype A3+B, which may result from diseases or fragmented sleep, might have an adverse effect on the cerebral vasculature.

Periodic limb movements and heart rate changes

Periodic limb movements (PLM) during sleep are believed to be under the control of the sympathetic nervous system and may cause interrupted sleep and daytime sleepiness. The present case highlights the close relationship between PLM and significant heart rate changes independent of the presence of arousals. Thus, in addition to the already known deleterious effect on sleep continuity, moderate-severe PLM may also affect cardiovascular health.

Sleep bruxism is associated with a rise in arterial blood pressure

STUDY OBJECTIVES

Sleep bruxism (SB) is a movement disorder identified by grinding of teeth and rhythmic masticatory muscle activity (RMMA). RMMA is associated with body movements and cortical arousals. Increases in autonomic sympathetic activities that characterize sleep cortical arousal precede RMMA/SB. Based on these findings, this study examined whether RMMA/SB episodes are also associated with significant changes in arterial blood pressure (BP).

DESIGN

Participants underwent 3 nights of full polysomnography that included noninvasive beat-to-beat BP recording. Single RMMA/SB episodes and arousal episodes were analyzed in stage 2 sleep and categorized as: (i) RMMA/SB + arousal; (ii) RMMA/SB + body movement; (iii) RMMA/SB + arousal + body movement; or (iv) arousal alone. Sleep and RMMA/SB data were compared to a Non SB group. RMMA/SB clusters (RMMA/SB episodes ≤ 30 sec apart) were also analyzed.

SETTING

Sleep Laboratory at l'Hôpital du Sacré-Coeur de Montréal.

PARTICIPANTS

Ten young, healthy participants with SB (mean age = 26 ± 1.8 years) and 9 without SB (mean age = 29 ± 1.2 years).

INTERVENTIONS

N/A MEASUREMENTS AND RESULTS: BP increased with all RMMA/SB and arousal episodes (P ≤ 0.05). The average maximum BP surges (systolic/diastolic ± SE mm Hg) were: 25.6 ± 3.3/12.6 ± 2.0 for RMMA/SB + arousal; 30.1 ± 1.7/19.1 ± 1.9 for RMMA/SB + body movement; 26.0 ± 2.8/15.1 ± 2.0 for RMMA/SB + arousal + body movement; 19.4 ± 2.3/8.9 ± 1.2 for arousal alone; and for RMMA/SB clusters: Episode: 1: 26.2 ± 8.7/16.4 ± 5.7; Episode 2: 21.1 ± 7.9/12.6 ± 6.4.

CONCLUSION

Rhythmic masticatory muscle activity/sleep bruxism (RMMA/SB) is associated with blood pressure fluctuations during sleep. Arousals and body movements often occur with RMMA/SB and can impact the magnitude of this BP surge.