Automatic analysis of single-channel sleep EEG in a large spectrum of sleep disorders

Inverse association between slow-wave sleep and low-grade inflammation in children and adolescents with major depressive disorder

OBJECTIVE

To investigate the relationship between both self-reported and objective sleep variables and low-grade inflammation in children and adolescents with major depressive disorder (MDD) of moderate to severe symptom severity.

METHODS

In this cross-sectional study, we examined twenty-nine children and adolescents diagnosed with MDD and twenty-nine healthy controls (HC). Following a one-week actigraphy assessment, comprehensive sleep evaluations were conducted, including a one-night sleep EEG measurement and self-reported sleep data. Plasma high-sensitivity C-reactive protein (hsCRP) was employed as a marker to assess low-grade inflammation.

RESULTS

No significant difference in hsCRP levels was observed between participants with MDD and HC. Furthermore, after adjusting for sleep difficulties, hsCRP exhibited no correlation with the severity of depressive symptoms. In HC, levels of hsCRP were not linked to self-reported and objective sleep variables. In contrast, depressed participants showed a significant correlation between hsCRP levels and increased subjective insomnia severity (Insomnia Severity Index; r = 0.41, p < 0.05), increased time spent in the N2 sleep stage (r = 0.47, p < 0.01), and decreased time spent in slow-wave sleep (r = - 0.61, p < 0.001). Upon additional adjustments for body mass index, tobacco use and depression severity, only the inverse association between hsCRP and time spent in slow-wave sleep retained statistical significance. Moderation analysis indicated that group status (MDD vs. HC) significantly moderates the association between slow-wave sleep and hsCRP.

CONCLUSION

Our findings suggest that alterations in the architecture of slow-wave sleep may have a significant influence on modulating low-grade inflammatory processes in children and adolescents with MDD.

Napping and circadian sleep-wake regulation during healthy aging

STUDY OBJECTIVES

Daytime napping is frequently reported among the older population and has attracted increasing attention due to its association with multiple health conditions. Here, we tested whether napping in the aged is associated with altered circadian regulation of sleep, sleepiness, and vigilance performance.

METHODS

Sixty healthy older individuals (mean age: 69 years, 39 women) were recruited with respect to their napping habits (30 nappers, 30 non-nappers). All participants underwent an in-lab 40-hour multiple nap protocol (10 cycles of 80 minutes of sleep opportunity alternating with 160 minutes of wakefulness), preceded and followed by a baseline and recovery sleep period. Saliva samples for melatonin assessment, sleepiness, and vigilance performance were collected during wakefulness and electrophysiological data were recorded to derive sleep parameters during scheduled sleep opportunities.

RESULTS

The circadian amplitude of melatonin secretion was reduced in nappers, compared to non-nappers. Furthermore, nappers were characterized by higher sleep efficiencies and REM sleep proportion during day- compared to nighttime naps. The nap group also presented altered modulation in sleepiness and vigilance performance at specific circadian phases.

DISCUSSION

Our data indicate that napping is associated with an altered circadian sleep-wake propensity rhythm. They thereby contribute to the understanding of the biological correlates underlying napping and/or sleep-wake cycle fragmentation during healthy aging. Altered circadian sleep-wake promotion can lead to a less distinct allocation of sleep into nighttime and/or a reduced wakefulness drive during the day, thereby potentially triggering the need to sleep at adverse circadian phase.

Revisiting the maintenance of wakefulness test: from intra-/inter-scorer agreement to normative values in patients treated for obstructive sleep apnea

The Maintenance of Wakefulness Test is widely used to objectively assess sleepiness and make safety-related decisions, but its interpretation is subjective and normative values remain debated. Our work aimed to determine normative thresholds in non-subjectively sleepy patients with well-treated obstructive sleep apnea, and to assess intra- and inter-scorer variability. We included maintenance of wakefulness tests of 141 consecutive patients with treated obstructive sleep apnea (90% men, mean (SD) age 47.5 (9.2) years, mean (SD) pre-treatment apnea-hypopnea index of 43.8 (20.3) events/h). Sleep onset latencies were independently scored by two experts. Discordant scorings were reviewed to reach a consensus and half of the cohort was double-scored by each scorer. Intra- and inter-scorer variability was assessed using Cohen's kappa for 40, 33, and 19 min mean sleep latency thresholds. Consensual mean sleep latencies were compared between four groups according to subjective sleepiness (Epworth Sleepiness Scale score < versus ≥11) and residual apnea-hypopnea index (< versus ≥15 events/h). In well-treated non-sleepy patients (n = 76), the consensual mean (SD) sleep latency was 38.4 (4.2) min (lower normal limit [mean - 2SD] = 30 min), and 80% of them did not fall asleep. Intra-scorer agreement on mean sleep latency was high but inter-scorer was only fair (Cohen's kappa 0.54 for 33-min threshold, 0.27 for 19-min threshold), resulting in changes in latency category in 4%-12% of patients. A higher sleepiness score but not the residual apnea-hypopnea index was significantly associated with a lower mean sleep latency. Our findings suggest a higher than usually accepted normative threshold (30 min) in this context and emphasise the need for more reproducible scoring approaches.

Comparative analysis of sleep parameters and structures derived from wearable flexible electrode sleep patches and polysomnography in young adults

Polysomnography (PSG) is the gold standard for clinical sleep monitoring, but its cost, discomfort, and limited suitability for continuous use present challenges. The flexible electrode sleep patch (FESP) emerges as an economically viable and patient-friendly solution, offering lightweight, simple operation, and self-applicable. Nevertheless, its utilization in young individuals remains uncertain. The objective of this study was to compare sleep data obtained by FESP and PSG in healthy young individuals and analyze agreement for sleep parameters and structure classification. Overnight monitoring with FESP and PSG recordings in 48 participants (mean age: 23 yr) was done. Correlation analysis, Bland-Altman plots, and Cohen's kappa coefficient assessed consistency. Sensitivity, specificity, and predictive values compared classification against PSG. FESP showed strong correlation and consistency with PSG for sleep monitoring. Bland-Altman plots indicated small errors and high consistency. Kappa values (0.70-0.84) suggested substantial agreement for sleep stage classification. Pearson correlation coefficient values for sleep stages (0.75-0.88) and sleep parameters (0.80-0.96) confirm that FESP has a strong application. Intraclass correlation coefficient yielded values between 0.65 and 0.97. In addition, FESP demonstrated an impressive accuracy range of 84.12-93.47% for sleep stage classification. The FESP also features a wearable self-test program with an error rate of no more than 8% for both deep sleep and wake. In young adults, FESP demonstrated reliable monitoring capabilities comparable to PSG. With its low cost and user-friendly design, FESP is a potential alternative for portable sleep assessment in clinical and research applications. Further studies involving larger populations are needed to validate its diagnostic potential.NEW & NOTEWORTHY By comparison with PSG, this study confirmed the reliability of an efficient, objective, low-cost, and noninvasive portable automatic sleep-monitoring device FESP, which provides effective information for long-term family sleep disorder diagnosis and sleep quality monitoring.

Evaluating reliability in wearable devices for sleep staging

Sleep is crucial for physical and mental health, but traditional sleep quality assessment methods have limitations. This scoping review analyzes 35 articles from the past decade, evaluating 62 wearable setups with varying sensors, algorithms, and features. Our analysis indicates a trend towards combining accelerometer and photoplethysmography (PPG) data for out-of-lab sleep staging. Devices using only accelerometer data are effective for sleep/wake detection but fall short in identifying multiple sleep stages, unlike those incorporating PPG signals. To enhance the reliability of sleep staging wearables, we propose five recommendations: (1) Algorithm validation with equity, diversity, and inclusion considerations, (2) Comparative performance analysis of commercial algorithms across multiple sleep stages, (3) Exploration of feature impacts on algorithm accuracy, (4) Consistent reporting of performance metrics for objective reliability assessment, and (5) Encouragement of open-source classifier and data availability. Implementing these recommendations can improve the accuracy and reliability of sleep staging algorithms in wearables, solidifying their value in research and clinical settings.

Wake and non-rapid eye movement sleep dysfunction is associated with colonic neuropathology in Parkinson's disease

STUDY OBJECTIVES

The body-first Parkinson's disease (PD) hypothesis suggests initial gut Lewy body pathology initially propagates to the pons before reaching the substantia nigra, and subsequently progresses to the diencephalic and cortical levels, a disease course presumed to likely occur in PD with rapid eye movement sleep behavior disorder (RBD). We aimed to explore the potential association between colonic phosphorylated alpha-synuclein histopathology (PASH) and diencephalic or cortical dysfunction evidenced by non-rapid eye movement (NREM) sleep and wakefulness polysomnographic markers.

METHODS

In a study involving 43 patients with PD who underwent clinical examination, rectosigmoidoscopy, and polysomnography, we detected PASH on colonic biopsies using whole-mount immunostaining. We performed a visual semi-quantitative analysis of NREM sleep and wake electroencephalography (EEG), confirmed it with automated quantification of spindle and slow wave features of NREM sleep, and the wake dominant frequency, and then determined probable Arizona PD stage classifications based on sleep and wake EEG features.

RESULTS

The visual analysis aligned with the automated quantified spindle characteristics and the wake dominant frequency. Altered NREM sleep and wake parameters correlated with markers of PD severity, colonic PASH, and RBD diagnosis. Colonic PASH frequency also increased in parallel to probable Arizona PD stage classifications.

CONCLUSIONS

Colonic PASH is strongly associated with widespread brain sleep and wake dysfunction, suggesting an extensive diffusion of the pathologic process in PD. Visual and automated analyses of polysomnography signals provide useful markers to gauge covert brain dysfunction in PD.

CLINICAL TRIAL

Name: SYNAPark, URL: https://clinicaltrials.gov/study/NCT01748409, registration: NCT01748409.

A systematic review of EEG based automated schizophrenia classification through machine learning and deep learning

The electroencephalogram (EEG) serves as an essential tool in exploring brain activity and holds particular importance in the field of mental health research. This review paper examines the application of artificial intelligence (AI), encompassing machine learning (ML) and deep learning (DL), for classifying schizophrenia (SCZ) through EEG. It includes a thorough literature review that addresses the difficulties, methodologies, and discoveries in this field. ML approaches utilize conventional models like Support Vector Machines and Decision Trees, which are interpretable and effective with smaller data sets. In contrast, DL techniques, which use neural networks such as convolutional neural networks (CNNs) and long short-term memory networks (LSTMs), are more adaptable to intricate EEG patterns but require significant data and computational power. Both ML and DL face challenges concerning data quality and ethical issues. This paper underscores the importance of integrating various techniques to enhance schizophrenia diagnosis and highlights AI's potential role in this process. It also acknowledges the necessity for collaborative and ethically informed approaches in the automated classification of SCZ using AI.

Enhanced sleep staging with artificial intelligence: a validation study of new software for sleep scoring

Manual sleep staging (MSS) using polysomnography is a time-consuming task, requires significant training, and can lead to significant variability among scorers. STAGER is a software program based on machine learning algorithms that has been developed by Medibio Limited (Savage, MN, USA) to perform automatic sleep staging using only EEG signals from polysomnography. This study aimed to extensively investigate its agreement with MSS performed during clinical practice and by three additional expert sleep technicians. Forty consecutive polysomnographic recordings of patients referred to three US sleep clinics for sleep evaluation were retrospectively collected and analyzed. Three experienced technicians independently staged the recording using the electroencephalography, electromyography, and electrooculography signals according to the American Academy of Sleep Medicine guidelines. The staging initially performed during clinical practice was also considered. Several agreement statistics between the automatic sleep staging (ASS) and MSS, among the different MSSs, and their differences were calculated. Bootstrap resampling was used to calculate 95% confidence intervals and the statistical significance of the differences. STAGER's ASS was most comparable with, or statistically significantly better than the MSS, except for a partial reduction in the positive percent agreement in the wake stage. These promising results indicate that STAGER software can perform ASS of inpatient polysomnographic recordings accurately in comparison with MSS.

Four State Sleep Staging From a Multilayered Algorithm Using Electrocardiographic and Actigraphic Data

PURPOSE

Sleep studies are important to evaluate sleep and sleep-related disorders. The standard test for evaluating sleep is polysomnography, during which several physiological signals are recorded separately and simultaneously with specialized equipment that requires a technologist. Simpler recordings that can model the results of a polysomnography would provide the benefit of expanding the possibilities of sleep recordings.

METHODS

Using the publicly available sleep data set from the multiethnic study of atherosclerosis and 1769 nights of sleep, we extracted a distinct data subset with engineered features of the biomarkers collected by actigraphic, oxygenation, and electrocardiographic sensors. We then applied scalable models with recurrent neural network and Extreme Gradient Boosting (XGBoost) with a layered approach to produce an algorithm that we then validated with a separate data set of 177 nights.

RESULTS

The algorithm achieved an overall performance of 0.833 accuracy and 0.736 kappa in classifying into four states: wake, light sleep, deep sleep, and rapid eye movement (REM). Using feature analysis, we demonstrated that heart rate variability is the most salient feature, which is similar to prior reports.

CONCLUSIONS

Our results demonstrate the potential benefit of a multilayered algorithm and achieved higher accuracy and kappa than previously described approaches for staging sleep. The results further the possibility of simple, wearable devices for sleep staging. Code is available at https://github.com/NovelaNeuro/nEureka-SleepStaging.

Wake and non-rapid eye movement sleep dysfunction is associated with colonic neuropathology in Parkinson's disease

Study Objectives

The body-first Parkinson's disease (PD) hypothesis suggests initial gut Lewy body pathology that propagates to the pons before reaching the substantia nigra, and subsequently progresses to the diencephalic and cortical levels. This disease course may also be the most likely in PD with rapid eye movement sleep behavior disorder (RBD).

Objectives

We aimed to explore the potential association between colonic phosphorylated alpha-synuclein histopathology (PASH) and diencephalic or cortical dysfunction evidenced by non-rapid eye movement (NREM) sleep and wakefulness polysomnographic markers.

Methods

In a study involving 43 patients with PD who underwent clinical examination, rectosigmoidoscopy, and polysomnography, we detected PASH on colonic biopsies using whole-mount immunostaining. We performed a visual semi-quantitative and automated quantification of spindle and slow wave features of NREM sleep, and the wake dominant frequency, and then determined Braak and Arizona stage classifications for PD severity based on sleep and wake electroencephalographic features.

Results

The visual analysis aligned with the automated quantified spindle characteristics and the wake dominant frequency. Altered NREM sleep and wake parameters correlated with markers of PD severity, colonic PASH, and RBD diagnosis. Colonic PASH frequency also increased in parallel to presumed PD Braak and Arizona stage classifications.

Conclusions

Colonic PASH in PD is strongly associated with widespread brain sleep and wake dysfunction, pointing toward likely extensive diffusion of the pathological process in the presumptive body-first PD phenotype. Visual and automated analyses of polysomnography signals provide useful markers to gauge covert brain dysfunction in PD.

Statement of Significance

The presence of gut synucleinopathy in Parkinson's disease can be linked to the body-first hypothesis in its pathophysiology. This study, performed in a cohort of 43 patients with Parkinson's disease that underwent clinical assessment, rectosigmoidoscopy and polysomnography, provides evidence that colonic neuropathology in Parkinson's disease is associated with widespread brain dysfunction, as evaluated by wake and non-rapid eye movement sleep polysomnographic markers. Our results support the assumption of an extensive diffusion of the pathological process to diencephalic and neocortical structures in the presumptive body-first phenotype. They also suggest the use of routine polysomnography in phenotyping patients with Parkinson's disease. Future studies should investigate the brain diffusion pattern and its sleep markers in the hypothesized brain-first phenotype of Parkinson's disease.

Brain optimization with additional study time: potential brain differences between high- and low-performance college students

This study investigates potential differences in brain function among high-, average-, and low-performance college students using electroencephalography (EEG). We hypothesize that the increased academic engagement of high-performance students will lead to discernible EEG variations due to the brain's structural plasticity. 61 third-year college students from identical majors were divided into high-performance (n = 20), average-performance (n = 21), and low-performance (n = 20) groups based on their academic achievements. We conducted three EEG experiments: resting state, Sternberg working memory task, and Raven progressive matrix task. Comprehensive analyses of the EEG data from the three experiments focused on power spectral density (PSD) and functional connectivity, with coherence (COH) employed as our primary metric for the latter. The results showed that in all experiments, there were no differences in working memory ability and IQ scores among the groups, and there were no significant differences in the power spectral densities of the delta, theta, alpha1, alpha2, beta, and gamma bands among the groups. Notably, on the Raven test, compared to their high-performing peers, low-performing students showed enhanced functional connectivity in the alpha 1 (8-9 Hz) band that connects the frontal and occipital lobes. We explored three potential explanations for this phenomenon: fatigue, anxiety, and greater cognitive effort required for problem-solving due to inefficient self-regulation and increased susceptibility to distraction. In essence, these insights not only deepen our understanding of the neural basis that anchors academic ability, but also hold promise in guiding interventions that address students' diverse academic needs.

Clinical applications of artificial intelligence in sleep medicine: a sleep clinician's perspective

BACKGROUND

The past few years have seen a rapid emergence of artificial intelligence (AI)-enabled technology in the field of sleep medicine. AI refers to the capability of computer systems to perform tasks conventionally considered to require human intelligence, such as speech recognition, decision-making, and visual recognition of patterns and objects. The practice of sleep tracking and measuring physiological signals in sleep is widely practiced. Therefore, sleep monitoring in both the laboratory and ambulatory environments results in the accrual of massive amounts of data that uniquely positions the field of sleep medicine to gain from AI.

METHOD

The purpose of this article is to provide a concise overview of relevant terminology, definitions, and use cases of AI in sleep medicine. This was supplemented by a thorough review of relevant published literature.

RESULTS

Artificial intelligence has several applications in sleep medicine including sleep and respiratory event scoring in the sleep laboratory, diagnosing and managing sleep disorders, and population health. While still in its nascent stage, there are several challenges which preclude AI's generalizability and wide-reaching clinical applications. Overcoming these challenges will help integrate AI seamlessly within sleep medicine and augment clinical practice.

CONCLUSION

Artificial intelligence is a powerful tool in healthcare that may improve patient care, enhance diagnostic abilities, and augment the management of sleep disorders. However, there is a need to regulate and standardize existing machine learning algorithms prior to its inclusion in the sleep clinic.

Frontal grey matter microstructure is associated with sleep slow waves characteristics in late midlife

STUDY OBJECTIVES

The ability to generate slow waves (SW) during non-rapid eye movement (NREM) sleep decreases as early as the 5th decade of life, predominantly over frontal regions. This decrease may concern prominently SW characterized by a fast switch from hyperpolarized to depolarized, or down-to-up, state. Yet, the relationship between these fast and slow switcher SW and cerebral microstructure in ageing is not established.

METHODS

We recorded habitual sleep under EEG in 99 healthy late midlife individuals (mean age = 59.3 ± 5.3 years; 68 women) and extracted SW parameters (density, amplitude, frequency) for all SW as well as according to their switcher type (slow vs. fast). We further used neurite orientation dispersion and density imaging (NODDI) to assess microstructural integrity over a frontal grey matter region of interest (ROI).

RESULTS

In statistical models adjusted for age, sex, and sleep duration, we found that a lower SW density, particularly for fast switcher SW, was associated with a reduced orientation dispersion of neurites in the frontal ROI (p = 0.018, R2β* = 0.06). In addition, overall SW frequency was positively associated with neurite density (p = 0.03, R2β* = 0.05). By contrast, we found no significant relationships between SW amplitude and NODDI metrics.

CONCLUSIONS

Our findings suggest that the complexity of neurite organization contributes specifically to the rate of fast switcher SW occurrence in healthy middle-aged individuals, corroborating slow and fast switcher SW as distinct types of SW. They further suggest that the density of frontal neurites plays a key role for neural synchronization during sleep.

TRIAL REGISTRATION NUMBER

EudraCT 2016-001436-35.

Timely coupling of sleep spindles and slow waves is linked to early amyloid-β burden and predicts memory decline

Sleep alteration is a hallmark of ageing and emerges as a risk factor for Alzheimer’s disease (AD). While the fine-tuned coalescence of sleep microstructure elements may influence age-related cognitive trajectories, its association with AD processes is not fully established. Here, we investigated whether the coupling of spindles and slow waves (SW) is associated with early amyloid-β (Aβ) brain burden, a hallmark of AD neuropathology, and cognitive change over 2 years in 100 healthy individuals in late-midlife (50–70 years; 68 women). We found that, in contrast to other sleep metrics, earlier occurrence of spindles on slow-depolarisation SW is associated with higher medial prefrontal cortex Aβ burden (p=0.014, r²_β*=0.06) and is predictive of greater longitudinal memory decline in a large subsample (p=0.032, r²_β*=0.07, N=66). These findings unravel early links between sleep, AD-related processes, and cognition and suggest that altered coupling of sleep microstructure elements, key to its mnesic function, contributes to poorer brain and cognitive trajectories in ageing.

SleepSEEG: Automatic sleep scoring using intracranial EEG recordings only

OBJECTIVE

To perform automatic sleep scoring based only on intracranial EEG, without the need for scalp electroencephalography (EEG), electrooculography (EOG) and electromyography (EMG), in order to study sleep, epilepsy, and their interaction.

APPROACH

Data from 33 adult patients was used for development and training of the automatic scoring algorithm using both oscillatory and non-oscillatory spectral features. The first step consisted in unsupervised clustering of channels based on feature variability. For each cluster the classification was done in two steps, a multiclass tree followed by binary classification trees to distinguish the more challenging stage N1. The test data consisted in 11 patients, in whom the classification was done independently for each channel and then combined to get a single stage per epoch.

MAIN RESULTS

An overall agreement of 78% was observed in the test set between the sleep scoring of the algorithm and two human experts scoring based on scalp EEG, EOG and EMG. Balanced sensitivity and specificity were obtained for the different sleep stages. The performance was excellent for stages W, N2, and N3, and good for stage R, but with high variability across patients. The performance for the challenging stage N1 was poor, but at a similar level as for published algorithms based on scalp EEG. High confidence epochs in different stages (other than N1) can be identified with median per patient specificity >80%.

SIGNIFICANCE

The automatic algorithm can perform sleep scoring of long term recordings of patients with intracranial electrodes undergoing presurgical evaluation in the absence of scalp EEG, EOG and EMG, which are normally required to define sleep stages but are difficult to use in the context of intracerebral studies. It also constitutes a valuable tool to generate hypotheses regarding local aspects of sleep, and will be significant for sleep evaluation in clinical epileptology and neuroscience research.

EEG Pattern Classification of Picking and Coordination Using Anonymous Random Walks

Tacit coordination games are games where players are trying to select the same solution without any communication between them. Various theories have attempted to predict behavior in tacit coordination games. Until now, research combining tacit coordination games with electrophysiological measures was mainly based on spectral analysis. In contrast, EEG coherence enables the examination of functional and morphological connections between brain regions. Hence, we aimed to differentiate between different cognitive conditions using coherence patterns. Specifically, we have designed a method that predicts the class label of coherence graph patterns extracted out of multi-channel EEG epochs taken from three conditions: a no-task condition and two cognitive tasks, picking and coordination. The classification process was based on a coherence graph extracted out of the EEG record. To assign each graph into its appropriate label, we have constructed a hierarchical classifier. First, we have distinguished between the resting-state condition and the other two cognitive tasks by using a bag of node degrees. Next, to distinguish between the two cognitive tasks, we have implemented an anonymous random walk. Our classification model achieved a total accuracy value of 96.55%.

Automatic sleep staging of EEG signals: recent development, challenges, and future directions

Modern deep learning holds a great potential to transform clinical practice on human sleep. Teaching a machine to carry out routine tasks would be a tremendous reduction in workload for clinicians. Sleep staging, a fundamental step in sleep practice, is a suitable task for this and will be the focus in this article. Recently, automatic sleep staging systems have been trained to mimic manual scoring, leading to similar performance to human sleep experts, at least on scoring of healthy subjects. Despite tremendous progress, we have not seen automatic sleep scoring adopted widely in clinical environments. This review aims to give a shared view of the authors on the most recent state-of-the-art development in automatic sleep staging, the challenges that still need to be addressed, and the future directions for automatic sleep scoring to achieve clinical value.

Heterogeneity in the links between sleep arousals, amyloid-beta and cognition

BACKGROUND

Tight relationships between sleep quality, cognition, and amyloid-β (Aβ) accumulation, a hallmark of Alzheimer’s disease (AD) neuropathology, have been shown. Sleep arousals become more prevalent with aging and are considered to reflect poorer sleep quality. However, heterogeneity in arousals has been suggested while their associations with Aβ and cognition are not established.

METHODS

We recorded undisturbed night-time sleep with EEG in 101 healthy individuals aged 50–70 years, devoid of cognitive and sleep disorders. We classified spontaneous arousals according to their association with muscular tone increase (M+/M–) and sleep stage transition (T+/T–). We assessed cortical Aβ burden over earliest affected regions via PET imaging and assessed cognition via neuropsychological testing.

RESULTS

Arousal types differed in their oscillatory composition in θ (4–8 Hz) and β (16–30 Hz) EEG bands. Furthermore, T+M– arousals, interrupting sleep continuity, were positively linked to Aβ burden (P = 0.0053, R²_β* = 0.08). By contrast, more prevalent T–M+ arousals, upholding sleep continuity, were associated with lower Aβ burden (P = 0.0003, R²_β* = 0.13), and better cognition, particularly over the attentional domain (P < 0.05, R²_β* ≥ 0.04).

CONCLUSION

Contrasting with what is commonly accepted, we provide empirical evidence that arousals are diverse and differently associated with early AD-related neuropathology and cognition. This suggests that sleep arousals, and their coalescence with other brain oscillations during sleep, may actively contribute to the beneficial functions of sleep and constitute markers of favorable brain and cognitive health trajectories.

TRIAL REGISTRATION

EudraCT 2016-001436-35.

FUNDING

FRS-FNRS Belgium (FRSM 3.4516.11), Actions de Recherche Concertées Fédération Wallonie-Bruxelles (SLEEPDEM 17/27-09), ULiège, and European Regional Development Fund (Radiomed Project).

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Variability of sleep stage scoring in late midlife and early old age

Sleep stage scoring can lead to important inter-expert variability. Although likely, whether this issue is amplified in older populations, which show alterations of sleep electrophysiology, has not been thoroughly assessed. Algorithms for automatic sleep stage scoring may appear ideal to eliminate inter-expert variability. Yet, variability between human experts and algorithm sleep stage scoring in healthy older individuals has not been investigated. Here, we aimed to compare stage scoring of older individuals and hypothesized that variability, whether between experts or considering the algorithm, would be higher than usually reported in the literature. Twenty cognitively normal and healthy late midlife individuals' (61 ± 5 years; 10 women) night-time sleep recordings were scored by two experts from different research centres and one algorithm. We computed agreements for the entire night (percentage and Cohen's κ) and each sleep stage. Whole-night pairwise agreements were relatively low and ranged from 67% to 78% (κ, 0.54-0.67). Sensitivity across pairs of scorers proved lowest for stages N1 (8.2%-63.4%) and N3 (44.8%-99.3%). Significant differences between experts and/or algorithm were found for total sleep time, sleep efficiency, time spent in N1/N2/N3 and wake after sleep onset (p ≤ 0.005), but not for sleep onset latency, rapid eye movement (REM) and slow-wave sleep (SWS) duration (N2 + N3). Our results confirm high inter-expert variability in healthy aging. Consensus appears good for REM and SWS, considered as a whole. It seems more difficult for N3, potentially because human raters adapt their interpretation according to overall changes in sleep characteristics. Although the algorithm does not substantially reduce variability, it would favour time-efficient standardization.

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.