Deciphering Post-Stroke Sleep Disorders: Unveiling Neurological Mechanisms in the Realm of Brain Science

Sleep disorders are the most widespread mental disorders after stroke and hurt survivors' functional prognosis, response to restoration, and quality of life. This review will address an overview of the progress of research on the biological mechanisms associated with stroke-complicating sleep disorders. Extensive research has investigated the negative impact of stroke on sleep. However, a bidirectional association between sleep disorders and stroke exists; while stroke elevates the risk of sleep disorders, these disorders also independently contribute as a risk factor for stroke. This review aims to elucidate the mechanisms of stroke-induced sleep disorders. Possible influences were examined, including functional changes in brain regions, cerebrovascular hemodynamics, neurological deficits, sleep ion regulation, neurotransmitters, and inflammation. The results provide valuable insights into the mechanisms of stroke complicating sleep disorders.

An electroencephalography-based sleep index and supervised machine learning as a suitable tool for automated sleep classification in children

STUDY OBJECTIVES

Although sleep is frequently disrupted in the pediatric intensive care unit, it is currently not possible to perform real-time sleep monitoring at the bedside. In this study, spectral band powers of electroencephalography data are used to derive a simple index for sleep classification.

METHODS

Retrospective study at Erasmus MC Sophia Children's Hospital, using hospital-based polysomnography recordings obtained in non-critically ill children between 2017 and 2021. Six age categories were defined: 6-12 months, 1-3 years, 3-5 years, 5-9 years, 9-13 years, and 13-18 years. Candidate index measures were derived by calculating spectral band powers in different frequent frequency bands of smoothed electroencephalography. With the best performing index, sleep classification models were developed for two, three, and four states via decision tree and five-fold nested cross-validation. Model performance was assessed across age categories and electroencephalography channels.

RESULTS

In total 90 patients with polysomnography were included, with a mean (standard deviation) recording length of 10.3 (1.1) hours. The best performance was obtained with the gamma to delta spectral power ratio of the F4-A1 and F3-A1 channels with smoothing. Balanced accuracy was 0.88, 0.74, and 0.57 for two-, three-, and four-state classification. Across age categories, balanced accuracy ranged between 0.83 and 0.92 and 0.72 and 0.77 for two- and three-state classification, respectively.

CONCLUSIONS

We propose an interpretable and generalizable sleep index derived from single-channel electroencephalography for automated sleep monitoring at the bedside in non-critically ill children ages 6 months to 18 years, with good performance for two- and three-state classification.

CITATION

van Twist E, Hiemstra FW, Cramer ABG, et al. An electroencephalography-based sleep index and supervised machine learning as a suitable tool for automated sleep classification in children. J Clin Sleep Med. 2024;20(3):389-397.

Multicentre sleep-stage scoring agreement in the Sleep Revolution project

Determining sleep stages accurately is an important part of the diagnostic process for numerous sleep disorders. However, as the sleep stage scoring is done manually following visual scoring rules there can be considerable variation in the sleep staging between different scorers. Thus, this study aimed to comprehensively evaluate the inter-rater agreement in sleep staging. A total of 50 polysomnography recordings were manually scored by 10 independent scorers from seven different sleep centres. We used the 10 scorings to calculate a majority score by taking the sleep stage that was the most scored stage for each epoch. The overall agreement for sleep staging was κ = 0.71 and the mean agreement with the majority score was 0.86. The scorers were in perfect agreement in 48% of all scored epochs. The agreement was highest in rapid eye movement sleep (κ = 0.86) and lowest in N1 sleep (κ = 0.41). The agreement with the majority scoring varied between the scorers from 81% to 91%, with large variations between the scorers in sleep stage-specific agreements. Scorers from the same sleep centres had the highest pairwise agreements at κ = 0.79, κ = 0.85, and κ = 0.78, while the lowest pairwise agreement between the scorers was κ = 0.58. We also found a moderate negative correlation between sleep staging agreement and the apnea-hypopnea index, as well as the rate of sleep stage transitions. In conclusion, although the overall agreement was high, several areas of low agreement were also found, mainly between non-rapid eye movement stages.

Developing a deep learning model for sleep stage prediction in obstructive sleep apnea cohort using 60 GHz frequency-modulated continuous-wave radar

Given the significant impact of sleep on overall health, radar technology offers a promising, non-invasive, and cost-effective avenue for the early detection of sleep disorders, even prior to relying on polysomnography (PSG)-based classification. In this study, we employed an attention-based bidirectional long short-term memory (Attention Bi-LSTM) model to accurately predict sleep stages using 60 GHz frequency-modulated continuous-wave (FMCW) radar. Our dataset comprised 78 participants from an ongoing obstructive sleep apnea (OSA) cohort, recruited between July 2021 and November 2022, who underwent overnight polysomnography alongside radar sensor monitoring. The dataset encompasses comprehensive polysomnography recordings, spanning both sleep and wakefulness states. The predictions achieved a Cohen's kappa coefficient of 0.746 and an overall accuracy of 85.2% in classifying wakefulness, rapid-eye-movement (REM) sleep, and non-REM (NREM) sleep (N1 + N2 + N3). The results demonstrated that the models incorporating both Radar 1 and Radar 2 data consistently outperformed those using only Radar 1 data, indicating the potential benefits of utilising multiple radars for sleep stage classification. Although the performance of the models tended to decline with increasing OSA severity, the addition of Radar 2 data notably improved the classification accuracy. These findings demonstrate the potential of radar technology as a valuable screening tool for sleep stage classification.

Few-shot EEG sleep staging based on transductive prototype optimization network

Electroencephalography (EEG) is a commonly used technology for monitoring brain activities and diagnosing sleep disorders. Clinically, doctors need to manually stage sleep based on EEG signals, which is a time-consuming and laborious task. In this study, we propose a few-shot EEG sleep staging termed transductive prototype optimization network (TPON) method, which aims to improve the performance of EEG sleep staging. Compared with traditional deep learning methods, TPON uses a meta-learning algorithm, which generalizes the classifier to new classes that are not visible in the training set, and only have a few examples for each new class. We learn the prototypes of existing objects through meta-training, and capture the sleep features of new objects through the "learn to learn" method of meta-learning. The prototype distribution of the class is optimized and captured by using support set and unlabeled high confidence samples to increase the authenticity of the prototype. Compared with traditional prototype networks, TPON can effectively solve too few samples in few-shot learning and improve the matching degree of prototypes in prototype network. The experimental results on the public SleepEDF-2013 dataset show that the proposed algorithm outperform than most advanced algorithms in the overall performance. In addition, we experimentally demonstrate the feasibility of cross-channel recognition, which indicates that there are many similar sleep EEG features between different channels. In future research, we can further explore the common features among different channels and investigate the combination of universal features in sleep EEG. Overall, our method achieves high accuracy in sleep stage classification, demonstrating the effectiveness of this approach and its potential applications in other medical fields.

Accuracy of 11 Wearable, Nearable, and Airable Consumer Sleep Trackers: Prospective Multicenter Validation Study

BACKGROUND

Consumer sleep trackers (CSTs) have gained significant popularity because they enable individuals to conveniently monitor and analyze their sleep. However, limited studies have comprehensively validated the performance of widely used CSTs. Our study therefore investigated popular CSTs based on various biosignals and algorithms by assessing the agreement with polysomnography.

OBJECTIVE

This study aimed to validate the accuracy of various types of CSTs through a comparison with in-lab polysomnography. Additionally, by including widely used CSTs and conducting a multicenter study with a large sample size, this study seeks to provide comprehensive insights into the performance and applicability of these CSTs for sleep monitoring in a hospital environment.

METHODS

The study analyzed 11 commercially available CSTs, including 5 wearables (Google Pixel Watch, Galaxy Watch 5, Fitbit Sense 2, Apple Watch 8, and Oura Ring 3), 3 nearables (Withings Sleep Tracking Mat, Google Nest Hub 2, and Amazon Halo Rise), and 3 airables (SleepRoutine, SleepScore, and Pillow). The 11 CSTs were divided into 2 groups, ensuring maximum inclusion while avoiding interference between the CSTs within each group. Each group (comprising 8 CSTs) was also compared via polysomnography.

RESULTS

The study enrolled 75 participants from a tertiary hospital and a primary sleep-specialized clinic in Korea. Across the 2 centers, we collected a total of 3890 hours of sleep sessions based on 11 CSTs, along with 543 hours of polysomnography recordings. Each CST sleep recording covered an average of 353 hours. We analyzed a total of 349,114 epochs from the 11 CSTs compared with polysomnography, where epoch-by-epoch agreement in sleep stage classification showed substantial performance variation. More specifically, the highest macro F1 score was 0.69, while the lowest macro F1 score was 0.26. Various sleep trackers exhibited diverse performances across sleep stages, with SleepRoutine excelling in the wake and rapid eye movement stages, and wearables like Google Pixel Watch and Fitbit Sense 2 showing superiority in the deep stage. There was a distinct trend in sleep measure estimation according to the type of device. Wearables showed high proportional bias in sleep efficiency, while nearables exhibited high proportional bias in sleep latency. Subgroup analyses of sleep trackers revealed variations in macro F1 scores based on factors, such as BMI, sleep efficiency, and apnea-hypopnea index, while the differences between male and female subgroups were minimal.

CONCLUSIONS

Our study showed that among the 11 CSTs examined, specific CSTs showed substantial agreement with polysomnography, indicating their potential application in sleep monitoring, while other CSTs were partially consistent with polysomnography. This study offers insights into the strengths of CSTs within the 3 different classes for individuals interested in wellness who wish to understand and proactively manage their own sleep.

Threshold distribution of equal states for quantitative amplitude fluctuations

Objective. The distribution of equal states (DES) quantifies amplitude fluctuations in biomedical signals. However, under certain conditions, such as a high resolution of data collection or special signal processing techniques, equal states may be very rare, whereupon the DES fails to measure the amplitude fluctuations.Approach. To address this problem, we develop a novel threshold DES (tDES) that measures the distribution of differential states within a threshold. To evaluate the proposed tDES, we first analyze five sets of synthetic signals generated in different frequency bands. We then analyze sleep electroencephalography (EEG) datasets taken from the public PhysioNet.Main results. Synthetic signals and detrend-filtered sleep EEGs have no neighboring equal values; however, tDES can effectively measure the amplitude fluctuations within these data. The tDES of EEG data increases significantly as the sleep stage increases, even with datasets covering very short periods, indicating decreased amplitude fluctuations in sleep EEGs. Generally speaking, the presence of more low-frequency components in a physiological series reflects smaller amplitude fluctuations and larger DES.Significance. The tDES provides a reliable computing method for quantifying amplitude fluctuations, exhibiting the characteristics of conceptual simplicity and computational robustness. Our findings broaden the application of quantitative amplitude fluctuations and contribute to the classification of sleep stages based on EEG data.

Sleep CLIP: A Multimodal Sleep Staging Model Based on Sleep Signals and Sleep Staging Labels

Since the release of the contrastive language-image pre-training (CLIP) model designed by the OpenAI team, it has been applied in several fields owing to its high accuracy. Sleep staging is an important method of diagnosing sleep disorders, and the completion of sleep staging tasks with high accuracy has always remained the main goal of sleep staging algorithm designers. This study is aimed at designing a multimodal model based on the CLIP model that is more suitable for sleep staging tasks using sleep signals and labels. The pre-training efforts of the model involve five different training sets. Finally, the proposed method is tested on two training sets (EDF-39 and EDF-153), with accuracies of 87.3 and 85.4%, respectively.

Soccer, Sleep, Repeat: Effects of Training Characteristics on Sleep Quantity and Sleep Architecture

Due to the high demands of competitive sports, the sleep architecture of adolescent athletes may be influenced by their regular training. To date, there is no clear evidence on how training characteristics (intensity, time of day, number of sessions) influence sleep quality and quantity. 53 male soccer players (M = 14.36 years, SD = 0.55) of Austrian U15 (n = 45) and U16 elite teams (n = 8) were tested on at least three consecutive days following their habitual training schedules. Participants completed daily sleep protocols (7 a.m., 8 p.m.) and questionnaires assessing sleep quality (PSQI), chronotype (D-MEQ), competition anxiety (WAI-T), and stress/recovery (RESTQ). Electrocardiography (ECG) and actigraphy devices measured sleep. Using sleep protocols and an ECG-based multi-resolution convolutional neural network (MCNN), we found that higher training intensity leads to more wake time, that later training causes longer sleep duration, and that one training session per day was most advantageous for sleep quality. In addition, somatic complaints assessed by the WAI-T negatively affected adolescent athletes' sleep. Individual training loads and longer recovery times after late training sessions during the day should be considered in training schedules, especially for adolescent athletes. MCNN modeling based on ECG data seems promising for efficient sleep analysis in athletes.

DynamicSleepNet: a multi-exit neural network with adaptive inference time for sleep stage classification

Sleep is an essential human physiological behavior, and the quality of sleep directly affects a person's physical and mental state. In clinical medicine, sleep stage is an important basis for doctors to diagnose and treat sleep disorders. The traditional method of classifying sleep stages requires sleep experts to classify them manually, and the whole process is time-consuming and laborious. In recent years, with the help of deep learning, automatic sleep stage classification has made great progress, especially networks using multi-modal electrophysiological signals, which have greatly improved in terms of accuracy. However, we found that the existing multimodal networks have a large number of redundant calculations in the process of using multiple electrophysiological signals, and the networks become heavier due to the use of multiple signals, and difficult to be used in small devices. To solve these two problems, this paper proposes DynamicSleepNet, a network that can maximize the use of multiple electrophysiological signals and can dynamically adjust between accuracy and efficiency. DynamicSleepNet consists of three effective feature extraction modules (EFEMs) and three classifier modules, each EFEM is connected to a classifier. Each EFEM is able to extract signal features while making the effective features more prominent and the invalid features are suppressed. The samples processed by the EFEM are given to the corresponding classifier for classification, and if the classifier considers the uncertainty of the sample to be below the threshold we set, the sample can be output early without going through the whole network. We validated our model on four datasets. The results show that the highest accuracy of our model outperforms all baselines. With accuracy close to baselines, our model is faster than the baselines by a factor of several to several tens, and the number of parameters of the model is lower or close. The implementation code is available at: https://github.com/Quinella7291/A-Multi-exit-Neural-Network-with-Adaptive-Inference-Time-for-Sleep-Stage-Classification/.

Exploration of sleep function connection and classification strategies based on sub-period sleep stages

Background

As a medium for developing brain-computer interface systems, EEG signals are complex and difficult to identify due to their complexity, weakness, and differences between subjects. At present, most of the current research on sleep EEG signals are single-channel and dual-channel, ignoring the research on the relationship between different brain regions. Brain functional connectivity is considered to be closely related to brain activity and can be used to study the interaction relationship between brain areas.

Methods

Phase-locked value (PLV) is used to construct a functional connection network. The connection network is used to analyze the connection mechanism and brain interaction in different sleep stages. Firstly, the entire EEG signal is divided into multiple sub-periods. Secondly, Phase-locked value is used for feature extraction on the sub-periods. Thirdly, the PLV of multiple sub-periods is used for feature fusion. Fourthly, the classification performance optimization strategy is used to discuss the impact of different frequency bands on sleep stage classification performance and to find the optimal frequency band. Finally, the brain function network is constructed by using the average value of the fusion features to analyze the interaction of brain regions in different frequency bands during sleep stages.

Results

The experimental results have shown that when the number of sub-periods is 30, the α (8-13 Hz) frequency band has the best classification effect, The classification result after 10-fold cross-validation reaches 92.59%.

Conclusion

The proposed algorithm has good sleep staging performance, which can effectively promote the development and application of an EEG sleep staging system.

Effects of Spinal Cord Injury Site on Cardiac Autonomic Regulation: Insight from Analysis of Cardiovascular Beat by Beat Variability during Sleep and Orthostatic Challenge

The goal of this study on Spinal Cord Injury (SCI) patients with cervical or thoracic lesion was to assess whether disturbances of ANS control, according to location, might differently affect vagal and sympatho-vagal markers during sleep and orthostatic challenge. We analyzed with linear and nonlinear techniques beat-by-beat RR and arterial pressure (and respiration) variability signals, extracted from a polysomnographic study and a rest-tilt test. We considered spontaneous or induced sympathetic excitation, as obtained shifting from non-REM to REM sleep or from rest to passive tilt. We obtained evidence of ANS cardiac (dys)regulation, of greater importance for gradually proximal location (i.e., cervical) SCI, compatible with a progressive loss of modulatory role of sympathetic afferents to the spinal cord. Furthermore, in accordance with the dual, vagal and sympathetic bidirectional innervation, the results suggest that vagally mediated negative feedback baroreflexes were substantially maintained in all cases. Conversely, the LF and HF balance (expressed specifically by normalized units) appeared to be negatively affected by SCI, particularly in the case of cervical lesion (group p = 0.006, interaction p = 0.011). Multivariate analysis of cardiovascular variability may be a convenient technique to assess autonomic responsiveness and alteration of functionality in patients with SCI addressing selectively vagal or sympathetic alterations and injury location. This contention requires confirmatory studies with a larger population.

Staging study of single-channel sleep EEG signals based on data augmentation

Introduction

Accurate sleep staging is an essential basis for sleep quality assessment and plays an important role in sleep quality research. However, the occupancy of different sleep stages is unbalanced throughout the sleep process, which makes the EEG datasets of different sleep stages have a class imbalance, which will eventually affect the automatic assessment of sleep stages.

Method

In this paper, we propose a Residual Dense Block and Deep Convolutional Generative Adversarial Network (RDB-DCGAN) data augmentation model based on the DCGAN and RDB, which takes two-dimensional continuous wavelet time-frequency maps as input, expands the minority class of sleep EEG data and later performs sleep staging by Convolutional Neural Network (CNN).

Results and discussion

The results of the CNN classification comparison test with the publicly available dataset Sleep-EDF show that the overall sleep staging accuracy of each stage after data augmentation is improved by 6%, especially the N1 stage, which has low classification accuracy due to less original data, also has a significant improvement of 19%. It is fully verified that data augmentation by improving the DCGAN model can effectively improve the classification problem of the class imbalance sleep dataset.

Sleep Quality and Electroencephalogram Delta Power

Delta activity on electroencephalogram (EEG) is considered a biomarker of homeostatic sleep drive. Delta power is often associated with sleep duration and intensity. Here, we reviewed the literature to explore how sleep quality was influenced by changes in delta power. However, we found that both the decrease and increase in delta power could indicate a higher sleep quality due to the various factors below. First, the differences in changes in delta power in patients whose sleep quality is lower than that of the healthy controls may be related to the different diseases they suffered from. We found that the patients mainly suffered from borderline personality disorder, and Rett syndrome may have a higher delta power than healthy individuals. Meanwhile, patients who are affected by Asperger syndrome, respiratory failure, chronic fatigue, and post-traumatic stress disorder have lower delta power. Second, if the insomnia patients received the therapy, the difference may be caused by the treatment method. Cognitive or music therapy shows that a better therapeutic effect is associated with decreased delta power, whereas in drug treatment, there is an opposite change in delta power. Last, for healthy people, the difference in delta change may be related to sleep stages. The higher sleep quality is associated with increased delta power during the NREM period, whereas a deceased delta change accompanies higher sleep quality during the REM period. Our work summarizes the effect of changes in delta power on sleep quality and may positively impact the monitoring and intervention of sleep quality.

Automatic Detection of K-Complexes Using the Cohen Class Recursiveness and Reallocation Method and Deep Neural Networks with EEG Signals

Evoked and spontaneous K-complexes are thought to be involved in sleep protection, but their role as biomarkers is still under debate. K-complexes have two major functions: first, they suppress cortical arousal in response to stimuli that the sleeping brain evaluates to avoid signaling danger; and second, they help strengthen memory. K-complexes also play an important role in the analysis of sleep quality, in the detection of diseases associated with sleep disorders, and as biomarkers for the detection of Alzheimer’s and Parkinson’s diseases. Detecting K-complexes is relatively difficult, as reliable methods of identifying this complex cannot be found in the literature. In this paper, we propose a new method for the automatic detection of K-complexes combining the method of recursion and reallocation of the Cohen class and the deep neural networks, obtaining a recursive strategy aimed at increasing the percentage of classification and reducing the computation time required to detect K-complexes by applying the proposed methods.

Impact of methylphenidate on sleep problems in adults with ADHD: a pilot polysomnography study

BACKGROUND

Attention-deficit hyperactivity disorder (ADHD) is associated with disrupted sleep and circadian rhythm. Medication for ADHD may have side effects aggravating sleep-disturbances, however beneficial effects on ADHD may contribute to improve sleep.

AIMS

This pilot study aims to examine outcomes of first time stimulant treatment on objective and subjective sleep characteristics, and psychiatric symptoms, in adult ADHD patients with pretreatment sleep problems, but without any primary sleep disorder.

METHODS

In total, 9 previously unmedicated adult ADHD subjects who reported pretreatment sleep problems, completed polysomnography (PSG) and questionnaires on subjective sleep disturbances and psychiatric symptoms. Data was collected before and after 6 weeks on first time medication with immediate-release methylphenidate (MPH-IR), mean daily dose 43 mg.

RESULTS

Subjects on-medication showed an increased percentage of Stage 2 sleep compared to their non-treated baseline (46.6% versus 55.2%, p = .011). Otherwise, there were no significant changes in PSG variables. There were no firm changes in daytime sleepiness or symptoms of sleep disturbances.

CONCLUSIONS

These findings should be interpreted cautiously given the open-label design and small sample size, and should be examined in larger studies with more rigorous study designs.

Automatic Sleep-Stage Scoring in Healthy and Sleep Disorder Patients Using Optimal Wavelet Filter Bank Technique with EEG Signals

Sleep stage classification plays a pivotal role in effective diagnosis and treatment of sleep related disorders. Traditionally, sleep scoring is done manually by trained sleep scorers. The analysis of electroencephalogram (EEG) signals recorded during sleep by clinicians is tedious, time-consuming and prone to human errors. Therefore, it is clinically important to score sleep stages using machine learning techniques to get accurate diagnosis. Several studies have been proposed for automated detection of sleep stages. However, these studies have employed only healthy normal subjects (good sleepers). The proposed study focuses on the automated sleep-stage scoring of subjects suffering from seven different kind of sleep disorders such as insomnia, bruxism, narcolepsy, nocturnal frontal lobe epilepsy (NFLE), periodic leg movement (PLM), rapid eye movement (REM) behavioural disorder and sleep-disordered breathing as well as normal subjects. The open source physionet's cyclic alternating pattern (CAP) sleep database is used for this study. The EEG epochs are decomposed into sub-bands using a new class of optimized wavelet filters. Two EEG channels, namely F4-C4 and C4-A1, combined are used for this work as they can provide more insights into the changes in EEG signals during sleep. The norm features are computed from six sub-bands coefficients of optimal wavelet filter bank and fed to various supervised machine learning classifiers. We have obtained the highest classification performance using an ensemble of bagged tree (EBT) classifier with 10-fold cross validation. The CAP database comprising of 80 subjects is divided into ten different subsets and then ten different sleep-stage scoring tasks are performed. Since, the CAP database is unbalanced with different duration of sleep stages, the balanced dataset also has been created using over-sampling and under-sampling techniques. The highest average accuracy of 85.3% and Cohen's Kappa coefficient of 0.786 and accuracy of 92.8% and Cohen's Kappa coefficient of 0.915 are obtained for unbalanced and balanced databases, respectively. The proposed method can reliably classify the sleep stages using single or dual channel EEG epochs of 30 s duration instead of using multimodal polysomnography (PSG) which are generally used for sleep-stage scoring. Our developed automated system is ready to be tested with more sleep EEG data and can be employed in various sleep laboratories to evaluate the quality of sleep in various sleep disorder patients and normal subjects.

A Persistent Homology Approach to Heart Rate Variability Analysis With an Application to Sleep-Wake Classification

Persistent homology is a recently developed theory in the field of algebraic topology to study shapes of datasets. It is an effective data analysis tool that is robust to noise and has been widely applied. We demonstrate a general pipeline to apply persistent homology to study time series, particularly the instantaneous heart rate time series for the heart rate variability (HRV) analysis. The first step is capturing the shapes of time series from two different aspects—the persistent homologies and hence persistence diagrams of its sub-level set and Taken's lag map. Second, we propose a systematic and computationally efficient approach to summarize persistence diagrams, which we coined persistence statistics. To demonstrate our proposed method, we apply these tools to the HRV analysis and the sleep-wake, REM-NREM (rapid eyeball movement and non rapid eyeball movement) and sleep-REM-NREM classification problems. The proposed algorithm is evaluated on three different datasets via the cross-database validation scheme. The performance of our approach is better than the state-of-the-art algorithms, and the result is consistent throughout different datasets.

Heart rate variability and obstructive sleep apnea: Current perspectives and novel technologies

Obstructive sleep apnea (OSA) is a highly prevalent condition, resulting in recurrent hypoxic events, sleep arousal, and daytime sleepiness. Patients with OSA are at an increased risk of cardiovascular morbidity and mortality. The mechanisms underlying the development of cardiovascular disease in OSA are multifactorial and cause a cascade of events. The primary contributing factor is sympathetic overactivity. Heart rate variability (HRV) can be used to evaluate shifts in the autonomic nervous system, during sleep and in response to treatment in patients with OSA. Newer technologies are aimed at improving HRV analysis to accelerate processing time, improve the diagnosis of OSA, and detection of cardiovascular risk. The present review will present contemporary understandings and uses for HRV, specifically in the realms of physiology, technology, and clinical management.

Automatic Sleep Stage Classification of Children with Sleep-Disordered Breathing Using the Modularized Network

PURPOSE

To develop an automatic sleep stage analysis model for children and evaluate the effect of the model on the diagnosis of sleep-disordered breathing (SDB).

PATIENTS AND METHODS

Three hundred and forty-four SDB patients aged between 2 to 18 years who completed polysomnography (PSG) to assess the severity of the disease were enrolled in this study. We developed deep neural networks to stage sleep from electroencephalography (EEG), electrooculography (EOG) and electromyogram (EMG). The model performance was estimated by accuracy, precision, recall, F1-score, and Cohen's Kappa coefficient (ĸ). And we compared the difference in calculation of sleep parameters among the technicians, the model ensemble, and the single-channel EEG model.

RESULTS

The numbers of raw data divided into training, validation, and testing were 240, 36, and 68, respectively. The best performance appeared in the model ensemble of which the accuracy was 83.36% (ĸ=0.7817) in 5-stages, and the accuracy was 96.76% (ĸ=0.8236) in 2-stages. The single-channel EEG model showed the classification satisfyingly as well. There was no significant difference in TST, SE, SOL, time in W, time in N1+N2, time in N3, and OAHI between technician and the model (P>0.05). On the datasets from sleep-EDF-13 and sleep-EDF-18, the average classification accuracies achieved were 92.76% and 91.94% in 5-stages by using the proposed method, respectively.

CONCLUSION

This research established the model for pediatric automatic sleep stage classification with satisfying reliability and generalizability. In addition, it could be applied for calculating quantitative sleep parameters and evaluating the severity of SDB.

An Automatic Sleep Stage Classification Algorithm Using Improved Model Based Essence Features

The automatic sleep stage classification technique can facilitate the diagnosis of sleep disorders and release the medical expert from labor-consumption work. In this paper, novel improved model based essence features (IMBEFs) were proposed combining locality energy (LE) and dual state space models (DSSMs) for automatic sleep stage detection on single-channel electroencephalograph (EEG) signals. Firstly, each EEG epoch is decomposed into low-level sub-bands (LSBs) and high-level sub-bands (HSBs) by wavelet packet decomposition (WPD), separately. Then, the DSSMs are estimated by the LSBs and the LE calculation is carried out on HSBs. Thirdly, the IMBEFs extracted from the DSSM and LE are fed into the appropriate classifier for sleep stage classification. The performance of the proposed method was evaluated on three public sleep databases. The experimental results show that under the Rechtschaffen's and Kale's (R&K) standard, the sleep stage classification accuracies of six classes on the Sleep EDF database and the Dreams Subjects database are 92.04% and 78.92%, respectively. Under the American Academy of Sleep Medicine (AASM) standard, the classification accuracies of five classes in the Dreams Subjects database and the ISRUC database reached 79.90% and 81.65%. The proposed method can be used for reliable sleep stage classification with high accuracy compared with state-of-the-art methods.

Relationships between objective sleep parameters and inflammatory biomarkers in pregnancy

We examined the relationships between sleep and inflammatory biomarkers during late pregnancy. Seventy-four women underwent an overnight sleep assessment by polysomnography. Blood samples were collected before bedtime and again within 1 h upon awakening to measure C-reactive protein (CRP), interleukin (IL)-6, and IL-6 soluble receptor. Sleep parameters included variables characterizing sleep architecture and sleep continuity. The participants were 32.2 (SD = 4.1) years old, and the average gestational age was 32.8 (3.5) weeks. Controlling for covariates, evening CRP was negatively associated with N3 sleep (β = -0.30, P = 0.010). N3 sleep was also negatively associated with morning CRP (β = -0.26, P = 0.036), with a higher percentage of N3 sleep associated with a lower level of morning CRP. Contrarily, there was a tendency for a positive association between stage N2 sleep and morning CRP (β = 0.23, P = 0.065). Stage N1 sleep was associated with morning IL-6 (β = 0.28, P = 0.021), with a higher percentage of N1 sleep associated with a higher morning IL-6. No significant associations were found between morning inflammatory biomarkers and sleep continuity parameters. In conclusion, increased light sleep was associated with increased inflammatory biomarkers, whereas more deep sleep was associated with decreased inflammatory biomarkers. These findings further support the interactions between sleep and the immune system during late pregnancy.

A Novel Microwave Treatment for Sleep Disorders and Classification of Sleep Stages Using Multi-Scale Entropy

The aim of this study was to develop an integrated system of non-contact sleep stage detection and sleep disorder treatment for health monitoring. Hence, a method of brain activity detection based on microwave scattering technology instead of scalp electroencephalogram was developed to evaluate the sleep stage. First, microwaves at a specific frequency were used to penetrate the functional sites of the brain in patients with sleep disorders to change the firing frequency of the activated areas of the brain and analyze and evaluate statistically the effects on sleep improvement. Then, a wavelet packet algorithm was used to decompose the microwave transmission signal, the refined composite multiscale sample entropy, the refined composite multiscale fluctuation-based dispersion entropy and multivariate multiscale weighted permutation entropy were obtained as features from the wavelet packet coefficient. Finally, the mutual information-principal component analysis feature selection method was used to optimize the feature set and random forest was used to classify and evaluate the sleep stage. The results show that after four times of microwave modulation treatment, sleep efficiency improved continuously, the overall maintenance was above 80%, and the insomnia rate was reduced gradually. The overall classification accuracy of the four sleep stages was 86.4%. The results indicate that the microwaves with a certain frequency can treat sleep disorders and detect abnormal brain activity. Therefore, the microwave scattering method is of great significance in the development of a new brain disease treatment, diagnosis and clinical application system.

Very low frequency component of heart rate variability as a marker for therapeutic efficacy in patients with obstructive sleep apnea: Preliminary study

Background:

Although positive airway pressure (PAP) therapy is effective for treating obstructive sleep apnea (OSA), some patients with severe OSA are intolerable to this treatment, which may lead to an increase in the mortality and morbidity of cardiovascular diseases. We investigated the relationship between heart rate variability (HRV) and sleep parameters during natural sleep and treatment of patients with OSA.

Materials and Methods:

This was the cross-sectional observation study. Patients were 17 males with severe OSA who were unable to accept continuous PAP. Standard polysomnography was performed for two consecutive nights, i.e., during natural sleep and following night with bilevel PAP (BiPAP) treatment. Time-dependent responses of the amplitudes of low frequency (LF), very low frequency (VLF), and high frequency components of HRV were assessed with the technique of complex demodulation.

Results:

Apnea–hypopnea index, oxygen desaturation time, and percentage of stage 1 sleep were significantly reduced, whereas the percentages of rapid eye movement and stages 3 + 4 sleep were increased, by BiPAP treatment. Therapy also reduced the amplitudes of VLF and LF components of HRV. Difference in amplitudes of VLF during natural sleep and treatment with BiPAP was significantly correlated with difference in percentages of stage 1 and stages 3 + 4 sleep.

Conclusion:

Therapy-induced amelioration of OSA and sleep quality was accompanied by decrease in the amplitudes of VLF components of HRV. The VLF component may thus reflect physiological changes in both autonomic activity and sleep structure and serve as an objective marker for therapeutic efficacy in patients with severe OSA.

Comparison of support vector machines based on particle swarm optimization and genetic algorithm in sleep staging

BACKGROUND:

Heart rate variability (HRV) can reflect the relationship between heart rhythm and sleep structure.

OBJECTIVE:

In order to study the effect of support vector machine (SVM) on the results of automatic sleep staging and improve the effectiveness of heart rate variability (HRV) as a sleep structure biomarker, thereby realize long term and non-contact monitoring of sleep quality.

METHODS:

Two kinds of parameter optimization methods are applied to stage sleep experiments when the known SVM can be used for automatic sleep staging. By factor analysis of the time domain, frequency domain, and nonlinear dynamic characteristics of subjects’ HRV signals, the accuracy of the cross-validation method (K-CV) is used as the fitness function value in genetic algorithm (GA) and particle swarm optimization (PSO). Furthermore, GA and PSO are used to optimize the SVM parameters.

RESULTS:

The results show that the accuracy rate of sleep stage is 64.44% when parameters are not optimized, the accuracy rate based on PSO is improved to 78.89% and the accuracy rate based on GA is improved to 84.44%.

CONCLUSION:

Both optimization algorithms can improve the accuracy of SVM for sleep staging and better results based on GA in the experiment.

Development of a novel transformation algorithm of electromyography and actigraphy signals based on electrical and kinetic energy: An application for sleep healthcare

BACKGROUND

Although actigraphy is widely used to measure sleep quality, few studies directly compared actigraphy data with polysomnography data, especially electromyography data.

OBJECTIVE

We developed an algorithm which transforms actigraphy and electromyography signals to verify the interchangeability between them and tested the utility of this algorithm in sleep healthcare.

METHODS

Thirty-eight subjects underwent polysomnography and actigraphy. We transformed electromyography signals extracted from polysomnography as integrated electromyography (IEMG) and actigraphy signals as integrated acceleration (IACC) using their physical properties. We compared receiver operating characteristic (ROC) curves obtained from transformed datasets with those of raw datasets in distinguishing REM and non-REM sleep.

RESULTS

There was no significant correlation between raw electromyography and raw actigraphy data (r= 0.001, p= 0.124). After applying our transformation algorithm, significant correlation between IEMG and IACC was shown (r= 0.392, p< 0.001). In order to overcome small adjusted R2 from simple regression model (adjusted R=2 0.153, p< 0.001), we used panel data regression model to correct individual variances (adjusted R=2 0.542, p< 0.001). In ROC curve for distinguishing REM and non-REM sleep, AUCs were 0.536, 0.735 and 0.729 in raw data, IEMG and IACC respectively.

CONCLUSIONS

The transformation algorithm revealed the relationship between electromyography and actigraphy data, and also yielded improved sleep staging ability.

Comparison of EMG power during sleep from the submental and frontalis muscles

OBJECTIVE

Submentalis electromyography (sEMG) and frontalis electromyography (fEMG) muscle activities have been used to assist in the staging of sleep and detection of disruptions in sleep. This study was designed to assess the concordance between sEMG and fEMG power, by and across sleep stages.

METHODS

Forty-three records with simultaneous acquisition of differential signals from the submental and frontalis muscles were evaluated. Sleep stages were assigned using the poly-somnography signals based on majority agreement of five technicians. The sEMG and fEMG signals were identically filtered and aligned prior to cross-correlation analysis.

RESULTS

A strong concordance between sEMG and fEMG power was observed, with 95% of the records exhibiting at least moderate agreement. During rapid eye movement (REM) sleep, sEMG power was significantly less than fEMG power, but exhibited four times greater across-subject variability. fEMG power during wake and non-REM (NREM) sleep was greater than sEMG power, but with 50% less variability. Differences in wake and N1 mean power and between the other sleep stages were more distinct in the fEMG recordings. Relative changes in sEMG and fEMG power across wake, NREM, and REM stages were essentially identical with median by-subject cross correlations of 0.98 and interquartile ranges of 0.97 and 0.99, respectively.

CONCLUSION

The fEMG and sEMG power values were similar during wakefulness and sleep; however, the frontalis exhibits substantially less between-subject variability. This study established face validity for the use of fEMG in the detection of wake and stages of sleep, and for future applications toward assessment of quantitative REM sleep muscle activity in REM sleep behavior disorder.

A Novel Insight of Effects of a 3-Hz Binaural Beat on Sleep Stages During Sleep

The dichotic presentation of two almost equivalent pure tones with slightly different frequencies leads to virtual beat perception by the brain. In this phenomenon, the so-called binaural beat has a frequency equaling the difference of the frequencies of the two pure tones. The binaural beat can entrain neural activities to synchronize with the beat frequency and induce behavioral states related to the neural activities. This study aimed to investigate the effect of a 3-Hz binaural beat on sleep stages, which is considered a behavioral state. Twenty-four participants were allocated to experimental and control groups. The experimental period was three consecutive nights consisting of an adaptation night, a baseline night, and an experimental night. Participants in both groups underwent the same procedures, but only the experimental group was exposed to the 3-Hz binaural beat on the experimental night. The stimulus was initiated when the first epoch of the N2 sleep stage was detected and stopped when the first epoch of the N3 sleep stage detected. For the control group, a silent sham stimulus was used. However, the participants were blinded to their stimulus group. The results showed that the N3 duration of the experimental group was longer than that of the control group, and the N2 duration of the experimental group was shorter than that of the control group. Moreover, the N3 latency of the experimental group was shorter.

Sleep Stage Transition Dynamics Reveal Specific Stage 2 Vulnerability in Insomnia

Study Objectives

Objective sleep impairments in insomnia disorder (ID) are insufficiently understood. The present study evaluated whether whole-night sleep stage dynamics derived from polysomnography (PSG) differ between people with ID and matched controls and whether sleep stage dynamic features discriminate them better than conventional sleep parameters.

Methods

Eighty-eight participants aged 21-70 years, including 46 with ID and 42 age- and sex-matched controls without sleep complaints, were recruited through www.sleepregistry.nl and completed two nights of laboratory PSG. Data of 100 people with ID and 100 age- and sex-matched controls from a previously reported study were used to validate the generalizability of findings. The second night was used to obtain, in addition to conventional sleep parameters, probabilities of transitions between stages and bout duration distributions of each stage. Group differences were evaluated with nonparametric tests.

Results

People with ID showed higher empirical probabilities to transition from stage N2 to the lighter sleep stage N1 or wakefulness and a faster decaying stage N2 bout survival function. The increased transition probability from stage N2 to stage N1 discriminated people with ID better than any of their deviations in conventional sleep parameters, including less total sleep time, less sleep efficiency, more stage N1, and more wake after sleep onset. Moreover, adding this transition probability significantly improved the discriminating power of a multiple logistic regression model based on conventional sleep parameters.

Conclusions

Quantification of sleep stage dynamics revealed a particular vulnerability of stage N2 in insomnia. The feature characterizes insomnia better than-and independently of-any conventional sleep parameter.

Noncontact Sleep Study by Multi-Modal Sensor Fusion

Polysomnography (PSG) is considered as the gold standard for determining sleep stages, but due to the obtrusiveness of its sensor attachments, sleep stage classification algorithms using noninvasive sensors have been developed throughout the years. However, the previous studies have not yet been proven reliable. In addition, most of the products are designed for healthy customers rather than for patients with sleep disorder. We present a novel approach to classify sleep stages via low cost and noncontact multi-modal sensor fusion, which extracts sleep-related vital signals from radar signals and a sound-based context-awareness technique. This work is uniquely designed based on the PSG data of sleep disorder patients, which were received and certified by professionals at Hanyang University Hospital. The proposed algorithm further incorporates medical/statistical knowledge to determine personal-adjusted thresholds and devise post-processing. The efficiency of the proposed algorithm is highlighted by contrasting sleep stage classification performance between single sensor and sensor-fusion algorithms. To validate the possibility of commercializing this work, the classification results of this algorithm were compared with the commercialized sleep monitoring device, ResMed S+. The proposed algorithm was investigated with random patients following PSG examination, and results show a promising novel approach for determining sleep stages in a low cost and unobtrusive manner.

Gastrointestinal motility during sleep assessed by tracking of telemetric capsules combined with polysomnography - a pilot study

Studies of gastrointestinal function during sleep are hampered by lack of applicable techniques. Recent development of a novel ambulatory telemetric capsule system, which can be used in conjunction with polysomnography, offers a solution to this problem. The 3D-Transit system consists of ingestible electromagnetic capsules traceable through a portable extracorporeal receiver while traversing the gut. During sleep monitored by polysomnography, gastrointestinal motility was concurrently investigated using 3D-Transit in nine healthy subjects. Overall, the amplitude of gastric contractions decreased with depth of sleep (light sleep, N2 versus deep sleep, N3; P<0.05). Progression through the small intestine did not change with depth of sleep (Kruskal–Wallis probability =0.1), and there was no association between nocturnal awakenings or arousals and the occurrence of colonic or small intestinal propagating movements. Basal colonic activity was suppressed during both deep sleep (P<0.05) and light sleep (P<0.05) when compared with nocturnal wake periods. In conclusion, the novel ambulatory 3D-Transit system combined with polysomnography allows minimally invasive and completely ambulatory investigation of associations between sleep patterns and gastrointestinal motility.

Respiratory cycle-related electroencephalographic changes during sleep in healthy children and in children with sleep disordered breathing

STUDY OBJECTIVE

To investigate respiratory cycle-related electroencephalographic changes (RCREC) in healthy children and in children with sleep disordered breathing (SDB) during scored event-free (SEF) breathing periods of sleep.

DESIGN

Interventional case-control repeated measurements design.

SETTING

Paediatric sleep laboratory in a hospital setting.

PARTICIPANTS

Forty children with SDB and 40 healthy, age- and sex-matched children.

INTERVENTIONS

Adenotonsillectomy in children with SDB and no intervention in controls.

MEASUREMENTS AND RESULTS

Overnight polysomnography; electroencephalography (EEG) power variations within SEF respiratory cycles in the overall and frequency band-specific EEG within stage 2 nonrapid eye movement (NREM) sleep, slow wave sleep (SWS), and rapid eye movement (REM) sleep. Within both groups there was a decrease in EEG power during inspiration compared to expiration across all sleep stages. Compared to controls, RCREC in children with SDB in the overall EEG were significantly higher during REM and frequency band specific RCRECs were higher in the theta band of stage 2 and REM sleep, alpha band of SWS and REM sleep, and sigma band of REM sleep. This between-group difference was not significant postadenotonsillectomy.

CONCLUSION

The presence of nonrandom respiratory cycle-related electroencephalographic changes (RCREC) in both healthy children and in children with sleep disordered breathing (SDB) during NREM and REM sleep has been demonstrated. The RCREC values were higher in children with SDB, predominantly in REM sleep and this difference reduced after adenotonsillectomy.

CITATION

Immanuel SA, Pamula Y, Kohler M, Martin J, Kennedy D, Saint DA, Baumert M. Respiratory cycle-related electroencephalographic changes during sleep in healthy children and in children with sleep disordered breathing.

The relationship between mood and sleep in different female reproductive states

BACKGROUND

Sleep is disrupted in depressed subjects, but it also deteriorates with age and possibly with the transition to menopause. The nature of interaction between mood, sleep, age and reproductive state is not well-defined. The aim of this study was to evaluate the relationship between mood and sleep among healthy women in different reproductive states.

METHODS

We analyzed data from 11 younger (20-26 years), 21 perimenopausal (43-51 years) and 29 postmenopausal (58-71 years) healthy women who participated in a study on menopause, sleep and cognition. The 21-item Beck Depression Inventory (BDI) was administered to assess mood. Subjective sleep quality was assessed with the Basic Nordic Sleep Questionnaire (BNSQ). Objective sleep was measured with all-night polysomnography (PSG) recordings. Perimenopausal and younger women were examined during the first days of their menstrual cycle at the follicular phase.

RESULTS

Among younger women, less arousals associated with higher BDI total scores (p = 0.026), and higher SWS percentages with more dissatisfaction (p = 0.001) and depressive-somatic symptoms (p = 0.025), but with less depressive-emotional symptoms (p = 0.001). In specific, less awakenings either from REM sleep or SWS, respectively, associated with more punishment (p = 0.005; p = 0.036), more dissatisfaction (p < 0.001; p = 0.001) and more depressive-somatic symptoms (p = 0.001; p = 0.009), but with less depressive-emotional symptoms (p = 0.002; p = 0.003). In perimenopausal women, higher BNSQ insomnia scores (p = 0.005), lower sleep efficiencies (p = 0.022) and shorter total sleep times (p = 0.024) associated with higher BDI scores, longer sleep latencies with more depressive-somatic symptoms (p = 0.032) and longer REM latencies with more dissatisfaction (p = 0.017). In postmenopausal women, higher REM percentages associated with higher BDI total scores (p = 0.019) and more depressive-somatic symptoms (p = 0.005), and longer SWS latencies with more depressive-somatic symptoms (p = 0.030).

CONCLUSIONS

Depressive symptoms measured with the total BDI scores associated with sleep impairment in both perimenopausal and postmenopausal women. In younger women, specific BDI factors revealed minor associations, suggesting that the type of sleep impairment can vary in relation to different depressive features. Our data indicate that associations between sleep and depressed mood may change in conjunction with hormonal milestones.

Periodic leg movements are associated with reduced sleep quality in older men: the MrOS Sleep Study

STUDY OBJECTIVES

Periodic limb movements in sleep (PLMS) are common in the elderly. A previous large polysomnographic (PSG) study examining the relationship of PLMS to sleep architecture and arousals from sleep in women found that leg movements were common in elderly women, and PLMS which were associated with EEG arousals had a strong and consistent association with markers of disturbed sleep. Since sleep differs in men and women, we now investigate the association between PLMS and PSG indices of sleep quality in a large community-based sample of older men.

DESIGN

Observational study, cross-sectional analyses.

SETTING

Six clinical sites participating in the Osteoporotic Fractures in Men (MrOS) Study.

PARTICIPANTS

2,872 older community-dwelling men (mean age 76.4 years) who completed in-home PSG from 2003-2005.

INTERVENTIONS

N/A.

MEASUREMENTS AND RESULTS

In-home PSG was performed which included bilateral measurement of leg movements. The total number of leg movements per hour of sleep (PLMI) and the number of leg movements causing EEG-documented arousals per hour of sleep (PLMA) were computed. A PLMI ≥ 5 (70.8%) and PLMA ≥ 5 (27.4%) were both prevalent. Linear regression models were used to examine the relationship between PLMS as predictors and sleep architecture, arousal index, and sleep efficiency as outcomes. The highest quintiles of PLMI (≥ 65.1) and PLMA (≥ 6.8) showed the largest association with indices of sleep architecture; PLMA showed a larger magnitude of effect. After multivariate adjustment, participants with a higher PLMA had a small but significantly higher arousal index, lower sleep efficiency, higher percentages of stages 1 and 2 sleep, and lower percentages of stage 3-4 and REM sleep (p < 0.01). An increased PLMI was similarly associated with a higher arousal index, higher percentage of stage 2 sleep, and lower percentage of stage 3-4 (p < 0.0001), but not with an increase in stage 1, REM sleep, or sleep efficiency. Neither PLMI nor PMLA was associated with subjective sleepiness measured by the Epworth Sleepiness Scale.

CONCLUSIONS

This study demonstrated that periodic leg movements are very common in older community-dwelling men and regardless of associated arousals, are associated with evidence of lighter and more fragmented sleep.

Factors that Differentially Affect Daytime and Nighttime Sleep in Drosophila melanogaster

Rest in the fruit fly Drosophila melanogaster has key characteristics of mammalian sleep and is thus considered as a fly version of sleep. Drosophila sleep has been studied extensively, with the aim of gaining fundamental insights into the evolutionarily conserved functions of sleep as well as the mechanisms that regulate it. An interesting question that has not yet been addressed is whether fly sleep can be classified into distinct sleep types, each having particular biological roles – like rapid eye movement (REM) and non-REM sleep in birds and mammals. Typically, Drosophila sleep displays a bimodal pattern, consisting of distinct daytime and nighttime components. Notably, daytime and nighttime sleep differ with respect to several qualities, such as sleep-bout lengths and arousal thresholds. In this short review, we describe several genetic and environmental factors that differentially affect daytime and nighttime sleep, highlighting the observations suggesting the notion that these temporally distinct components of Drosophila sleep may have unique biological functions and be regulated by different homeostatic regulatory mechanisms.

Marked reduction in obstructive sleep apnea severity in slow wave sleep

INTRODUCTION

Obstructive sleep apnea (OSA) is widely accepted to improve during slow wave sleep (SWS) compared to lighter stages of NREM sleep. However, supporting data to establish the magnitude and prevalence of this effect is lacking. Consequently, we examined this phenomenon, controlling for posture, in a large group of patients investigated for OSA at an academic clinical sleep service.

METHODS

A detailed retrospective analysis was conducted on data obtained from each 30-sec epoch of sleep in 253 consecutive full-night diagnostic polysomnography studies performed over a 3-month period. Respiratory and arousal event rates were calculated within each stage of sleep, in the supine and lateral postures, and across the whole night, with OSA patients classified on the basis of an overall apnea-hypopnea index (AHI) > or =15 events/h. Central sleep apnea (CSA) patients were defined by a central apnea index > 5/h. Sleep latency and time, and respiratory and arousal event rates in OSA, CSA, and non-OSA patients were compared between sleep stages and postures using linear mixed model analysis. The numbers of patients achieving reduced event rates in SWS and in the lateral posture were also examined.

RESULTS

There were 171 patients with OSA, 14 with CSA, and 68 non-OSA patients. OSA patients took significantly longer to achieve slow wave and REM sleep (p < 0.001) than non-OSA patients and had less stage 4 sleep (p = 0.037). There were striking improvements in AHI and arousal index (Al) from stage 1 to 4 NREM sleep (p <0.001), with intermediate levels in REM sleep. AHI and Al were also markedly reduced in lateral versus supine sleep in all sleep stages (p < 0.001), with an effect size comparable to that of the slow wave sleep effect. The majority of OSA patients achieved low respiratory event rates in SWS. Eighty-two percent of patients achieved an AHI <15 and 57% < 5 events/hour during stage 4 sleep.

CONCLUSION

Although OSA patients demonstrate both a delayed and reduced proportion of SWS compared to non-OSA subjects, once they achieved SWS, AHI, and Al markedly improved in most patients.

Variability of QT interval duration in obstructive sleep apnea: an indicator of disease severity

STUDY OBJECTIVE

To determine OSA-related changes in variability of QT interval duration and in heart rate variability (HRV), and to evaluate the relationship of these parameters to disease severity.

DESIGN

Retrospective analysis of diagnostic sleep records.

SETTINGS

Clinical sleep laboratory in a hospital setting.

PATIENTS

Twenty patients (12 males and 8 females) without significant comorbidities who were undergoing polysomnography were studied.

MEASUREMENTS AND RESULTS

Standard heart rate variability measures and QT variability (Berger algorithm) were computed over consecutive 5-minute ECG epochs throughout the night. The effect of sleep stage and the relationship between these parameters and the severity of OSA as determined by the respiratory disturbance index (RDI) were explored. Further, a linear regression model of QT variability was developed. Severity of OSA (RDI) was 49 +/- 28 (range from 17-107) events/ hr. QT variability was the only ECG measure significantly correlated with RDI (both log-transformed; r = 0.6, P = 0.006). Further, QT variability was correlated with the minimum oxygen saturation (r = -0.55, P = 0.01). Sleep stage showed a significant effect on HRV, but not on QT variability. In the regression model, RDI was the strongest predictor of QT variability (R2 increase 38%), followed by high and low frequency power of HRV (R2 increase 10% each).

CONCLUSION

Obstructive sleep apnea is associated with changes in QT interval variability during sleep. The variance of beat-to-beat QT intervals correlates more strongly with the severity of OSA (as determined by RDI) than standard measures of heart rate variability, and is correlated with blood oxygenation, but not sleep stage.

Approximate entropy in the electroencephalogram during wake and sleep

Entropy measurement can discriminate among complex systems, including deterministic, stochastic and composite systems. We evaluated the changes of approximate entropy (ApEn) in signals of the electroencephalogram (EEG) during sleep. EEG signals were recorded from eight healthy volunteers during nightly sleep. We estimated the values of ApEn in EEG signals in each sleep stage. The ApEn values for EEG signals (mean +/- SD) were 0.896 +/- 0.264 during eyes-closed waking state, 0.738 +/- 0.089 during Stage I, 0.615 +/- 0.107 during Stage II, 0.487 +/- 0.101 during Stage II, 0.397 +/- 0.078 during Stage IV and 0.789 +/- 0.182 during REM sleep. The ApEn values were found to differ with statistical significance among the six different stages of consciousness (ANOVA, p<0.001). ApEn of EEG was statistically significantly lower during Stage IV and higher during wake and REM sleep. We conclude that ApEn measurement can be useful to estimate sleep stages and the complexity in brain activity.