The nature of arousal in sleep

State-of-the-art sleep arousal detection evaluated on a comprehensive clinical dataset

Aiming to apply automatic arousal detection to support sleep laboratories, we evaluated an optimized, state-of-the-art approach using data from daily work in our university hospital sleep laboratory. Therefore, a machine learning algorithm was trained and evaluated on 3423 polysomnograms of people with various sleep disorders. The model architecture is a U-net that accepts 50 Hz signals as input. We compared this algorithm with models trained on publicly available datasets, and evaluated these models using our clinical dataset, particularly with regard to the effects of different sleep disorders. In an effort to evaluate clinical relevance, we designed a metric based on the error of the predicted arousal index. Our models achieve an area under the precision recall curve (AUPRC) of up to 0.83 and F1 scores of up to 0.81. The model trained on our data showed no age or gender bias and no significant negative effect regarding sleep disorders on model performance compared to healthy sleep. In contrast, models trained on public datasets showed a small to moderate negative effect (calculated using Cohen's d) of sleep disorders on model performance. Therefore, we conclude that state-of-the-art arousal detection on our clinical data is possible with our model architecture. Thus, our results support the general recommendation to use a clinical dataset for training if the model is to be applied to clinical data.

Consciousness and sleep

Sleep is a universal, essential biological process. It is also an invaluable window on consciousness. It tells us that consciousness can be lost but also that it can be regained, in all its richness, when we are disconnected from the environment and unable to reflect. By considering the neurophysiological differences between dreaming and dreamless sleep, we can learn about the substrate of consciousness and understand why it vanishes. We also learn that the ongoing state of the substrate of consciousness determines the way each experience feels regardless of how it is triggered-endogenously or exogenously. Dreaming consciousness is also a window on sleep and its functions. Dreams tell us that the sleeping brain is remarkably lively, recombining intrinsic activation patterns from a vast repertoire, freed from the requirements of ongoing behavior and cognitive control.

Temporal dynamics of awakenings from slow-wave sleep in non-rapid eye movement parasomnia

Non-rapid eye movement parasomnia disorders, also called disorders of arousal, are characterized by abnormal nocturnal behaviours, such as confusional arousals or sleep walking. Their pathophysiology is not yet fully understood, and objective diagnostic criteria are lacking. It is known, however, that behavioural episodes occur mostly in the beginning of the night, after an increase in slow-wave activity during slow-wave sleep. A better understanding of the prospect of such episodes may lead to new insights in the underlying mechanisms and eventually facilitate objective diagnosis. We investigated temporal dynamics of transitions from slow-wave sleep of 52 patients and 79 controls. Within the patient group, behavioural and non-behavioural N3 awakenings were distinguished. Patients showed a higher probability to wake up after an N3 bout ended than controls, and this probability increased with N3 bout duration. Bouts longer than 15 min resulted in an awakening in 73% and 34% of the time in patients and controls, respectively. Behavioural episodes reduced over sleep cycles due to a reduction in N3 sleep and a reducing ratio between behavioural and non-behavioural awakenings. In the first two cycles, N3 bouts prior to non-behavioural awakenings were significantly shorter than N3 bouts advancing behavioural awakenings in patients, and N3 awakenings in controls. Our findings provide insights in the timing and prospect of both behavioural and non-behavioural awakenings from N3, which may result in prediction and potentially prevention of behavioural episodes. This work, moreover, leads to a more complete characterization of a prototypical hypnogram of parasomnias, which could facilitate diagnosis.

From physiological awakening to pathological sleep inertia: Neurophysiological and behavioural characteristics of the sleep-to-wake transition

Sleep inertia refers to the transient physiological state of hypoarousal upon awakening, associated with various degrees of impaired neurobehavioral performance, confusion, a desire to return to sleep and often a negative emotional state. Scalp and intracranial electro-encephalography as well as functional imaging studies have provided evidence that the sleep inertia phenomenon is underpinned by an heterogenous cerebral state mixing local sleep and local wake patterns of activity, at the neuronal and network levels. Sleep inertia is modulated by homeostasis and circadian processes, sleep stage upon awakening, and individual factors; this translates into a huge variability in its intensity even under physiological conditions. In sleep disorders, especially in hypersomnolence disorders such as idiopathic hypersomnia, sleep inertia may be a daily, serious and long-lasting symptom leading to severe impairment. To date, few tools have been developed to assess sleep inertia in clinical practice. They include mainly questionnaires and behavioral tests such as the psychomotor vigilance task. Only one neurophysiological protocol has been evaluated in hypersomnia, the forced awakening test which is based on an event-related potentials paradigm upon awakening. This contrasts with the major functional consequences of sleep inertia and its potentially dangerous consequences in subjects required to perform safety-critical tasks soon after awakening. There is a great need to identify reproducible biomarkers correlated with sleep inertia-associated cognitive and behavioral impairment. These biomarkers will aim at better understanding and measuring sleep inertia in physiological and pathological conditions, as well as objectively evaluating wake-promoting treatments or non-pharmacological countermeasures to reduce this phenomenon.

Association Between EEG Microarousal During Nocturnal Sleep and Next-Day Selective Attention in Mild Sleep-Restricted Healthy Undergraduates

Purpose

To explore whether sleep electroencephalogram (EEG) microarousals of different standard durations predict daytime mood and attention performance in healthy individuals after mild sleep restriction.

Participants and Methods

Sixteen (nine female) healthy college students were recruited to examine the correlations between nocturnal EEG microarousals of different standard durations (≥3 s, ≥5 s, ≥7 s, ≥9 s) under mild sleep restriction (1.5 h) and the following morning's subjective alertness, mood, sustained attention, and selective attention task performance.

Results

Results revealed that mild sleep restriction significantly reduced subjective alertness and positive mood, while having no significant effect on negative mood, sustained attention and selective attention performance. The number of microarousals (≥5 s) was negatively associated with positive mood at 6:30. The number of microarousals was significantly and positively correlated with the response time difference value of disengagement component of the selective attention task at around 7:30 (≥5 s and ≥7 s) and 9:00 (≥5 s). The number of microarousals (≥7 s) was significantly and positively correlated with the inaccuracy difference value of orientation component of the selective attention task at around 9:00.

Conclusion

The number of EEG microarousals during sleep in healthy adults with mild sleep restriction was significantly and negatively related to their daytime positive affect while positively associated with the deterioration of disengagement and orientation of selective attention performance, but this link is dependent on the standard duration of microarousals, test time and the type of task.

Fundamentals of sleep regulation: Model and benchmark values for fractal and oscillatory neurodynamics

Homeostatic, circadian and ultradian mechanisms play crucial roles in the regulation of sleep. Evidence suggests that ratios of low-to-high frequency power in the electroencephalogram (EEG) spectrum indicate the instantaneous level of sleep pressure, influenced by factors such as individual sleep-wake history, current sleep stage, age-related differences and brain topography characteristics. These effects are well captured and reflected in the spectral exponent, a composite measure of the constant low-to-high frequency ratio in the periodogram, which is scale-free and exhibits lower interindividual variability compared to slow wave activity, potentially serving as a suitable standardization and reference measure. Here we propose an index of sleep homeostasis based on the spectral exponent, reflecting the level of membrane hyperpolarization and/or network bistability in the central nervous system in humans. In addition, we advance the idea that the U-shaped overnight deceleration of oscillatory slow and fast sleep spindle frequencies marks the biological night, providing somnologists with an EEG-index of circadian sleep regulation. Evidence supporting this assertion comes from studies based on sleep replacement, forced desynchrony protocols and high-resolution analyses of sleep spindles. Finally, ultradian sleep regulatory mechanisms are indicated by the recurrent, abrupt shifts in dominant oscillatory frequencies, with spindle ranges signifying non-rapid eye movement and non-spindle oscillations - rapid eye movement phases of the sleep cycles. Reconsidering the indicators of fundamental sleep regulatory processes in the framework of the new Fractal and Oscillatory Adjustment Model (FOAM) offers an appealing opportunity to bridge the gap between the two-process model of sleep regulation and clinical somnology.

Oral biosciences: The annual review 2023

BACKGROUND

The Journal of Oral Biosciences is dedicated to advancing and disseminating fundamental knowledge with regard to every aspect of oral biosciences. This review features review articles in the fields of "bone regeneration," "periodontitis," "periodontal diseases," "salivary glands," "sleep bruxism," and "Sjögren's syndrome."

HIGHLIGHT

This review focuses on human demineralized dentin and cementum matrices for bone regeneration, oxidized low-density lipoprotein in periodontal disease and systemic conditions, the relationship between inflammatory mediators in migraine and periodontitis, phosphoinositide signaling molecules in the salivary glands, and the pathophysiologies of sleep bruxism and Sjögren's syndrome.

CONCLUSION

The review articles featured in the Journal of Oral Biosciences have broadened the knowledge of readers regarding various aspects of oral biosciences. The current editorial review discusses the findings and significance of these review articles.

Sleep: How stress keeps you up at night

Stress disrupts sleep, but the neural mechanisms underlying this relationship remain unclear. Novel findings in mice reveal a hypothalamic circuit that fragments sleep and promotes arousal after stress.

Regulation of stress-induced sleep fragmentation by preoptic glutamatergic neurons

Sleep disturbances are detrimental to our behavioral and emotional well-being. Stressful events disrupt sleep, in particular by inducing brief awakenings (microarousals, MAs), resulting in sleep fragmentation. The preoptic area of the hypothalamus (POA) is crucial for sleep control. However, how POA neurons contribute to the regulation of MAs and thereby impact sleep quality is unknown. Using fiber photometry in mice, we examine the activity of genetically defined POA subpopulations during sleep. We find that POA glutamatergic neurons are rhythmically activated in synchrony with an infraslow rhythm in the spindle band of the electroencephalogram during non-rapid eye movement sleep (NREMs) and are transiently activated during MAs. Optogenetic stimulation of these neurons promotes MAs and wakefulness. Exposure to acute social defeat stress fragments NREMs and significantly increases the number of transients in the calcium activity of POA glutamatergic neurons during NREMs. By reducing MAs, optogenetic inhibition during spontaneous sleep and after stress consolidates NREMs. Monosynaptically restricted rabies tracing reveals that POA glutamatergic neurons are innervated by brain regions regulating stress and sleep. In particular, presynaptic glutamatergic neurons in the lateral hypothalamus become activated after stress, and stimulating their projections to the POA promotes MAs and wakefulness. Our findings uncover a novel circuit mechanism by which POA excitatory neurons regulate sleep quality after stress.

Development of the sleep-wake switch in rats during the P2-P21 early infancy period

In early infancy, rats randomly alternate between the sleeping and waking states-from postnatal day 2-10 (P2-P10), sleep and wake bouts are both exponentially distributed with increasing means, while from P10-P21 sleep and wake bout means continue to increase, though there is a striking qualitative shift in the distribution of wake bouts from exponential to power law. The behavioral states of sleep and wakefulness correspond to the activity of sleep-active and wake-active neuronal brainstem populations, with reciprocal inhibition between the two ensuring that only one population is active at a time. The locus coeruleus (LC) forms a third component of this circuit that rises in prominence during the P10-P21 period, as experimental evidence shows that an as-of-yet undeciphered interaction of the LC with sleep-active and wake-active populations is responsible for the transformation of the wake bout distribution from exponential to power law. Interestingly, the LC undergoes remarkable physiological changes during the P10-P21 period-gap junctions within the LC are pruned and network-wide oscillatory synchrony declines and vanishes. In this work, we discuss a series of models of sleep-active, wake-active, and the LC populations, and we use these models to postulate the nature of the interaction between these three populations and how these interactions explain empirical observations of sleep and wake bout dynamics. We hypothesize a circuit in which there is reciprocal excitation between the LC and wake-active population with inhibition from the sleep-active population to the LC that suppresses the LC during sleep bouts. During the P2-P10 period, we argue that a noise-based switching mechanism between the sleep-active and wake-active populations provides a simple and natural way to account for exponential bout distributions, and that the locked oscillatory state of the LC prevents it from impacting bout distributions. From P10-P21, we use our models to postulate that, as the LC gradually shifts from a state of synchronized oscillations to a state of continuous firing, reciprocal excitation between the LC and the wake-active population is able to gradually transform the wake bout distribution from exponential to power law.

Correlations between sleep architecture and sleep-related masseter muscle activity in children with sleep bruxism

BACKGROUND

Sleep bruxism (SB) occurring during No-REM (nREM) sleep and increase in microarousals per hour have been described in adults, but not in children.

OBJECTIVE

To assess the correlation between sleep architecture and masseter muscle activity related to sleep bruxism (SB/MMA) in children.

MATERIALS AND METHODS

Forty-three children aged 7-12 years (mean age: 9.4 ± 1.3) with confirmed SB underwent a two-night polysomnographic (PSG) study in a sleep laboratory, for accommodation (first night) and data collection (second night). Data on sleep architecture (total sleep duration (TSD), sleep efficiency (SE), sleep onset latency (SOL), REM and nREM sleep duration and proportion and microarousals/hour during REM and nREM sleep) and episodes/hour of SB/MMA were recorded. Single and multiple-variable linear regression analyses were performed to assess the correlation between data on sleep architecture (predictors) and SB/MMA (dependent variable).

RESULTS

Shorter TSD, REM and nREM stage 1 sleep duration, longer SOL and more microarousals/hour during REM and nREM sleep were found to be positive predictors of SB/MMA in children in the multiple-variable regression analysis (R2  = 0.511).

CONCLUSION

Within the limitations of this study, it can be concluded that SB/MMA is correlated with altered sleep architecture in children (shorter total sleep duration (TSD), shorter nREM and REM sleep and higher microarousals during REM and nREM sleep). Nevertheless, the clinical significance of these findings need to be demonstrated in future studies.

[The role of cerebral activation in the sleep-wake cycle]

The development of views on cerebral activation in the wakefulness-sleep cycle, starting with the work of Constantin von Economo, is considered. The emphasis is on the cyclic activation of high-amplitude discharges in sleep, which, with known assumptions, can include K-complexes, as well as patterns of delta-like waves. Considering the participation of the peripheral nervous system in this, the integrative role of cyclic activation of high-amplitude discharges in the organization of the sleep-wake cycle is discussed.

A simple and fast ANN-based method of studying slow-wave sleep microstructure in freely moving rats

Electroencephalography (EEG) is a common technique for measuring brain activity. Artificial Neuronal Networks (ANNs) can provide valuable insights into the brain dynamics of humans and animals. We built a simple and fast shallow ANN-based solution for sleep recognition in EEGs recorded in freely moving rats. The ANN was constructed using open-source software and truncated to one formula with empirically defined weight coefficients. The optimization of the ANN model's performance (i.e., post-processing) relied on a probability-related approach to sleep microstructure. This approach could be a good way to analyze large datasets. In the current dataset, the slow-wave sleep was recognized with the sensitivity of 0.91 and the specificity of 0.98. The optimal model performance achieved with minimum sleep duration of 80-90 s and sleep interruption of 14-18 s. Our results suggest the following fundamental issues. First, 14-18 s sleep interruptions might be the archetypal micro-arousals in rats. Second, slow-wave sleep in rats might be built up of a set of sleep "building blocks" lasting 80-90 s.

Sleep apnea pathophysiology

OBJECTIVE

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

BACKGROUND

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

METHOD

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

RESULTS

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

CONCLUSIONS

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

Sleep tight! Adolescent sleep quality across three distinct sleep ecologies

Background and objectives

Good sleep quality, associated with few arousals, no daytime sleepiness and self-satisfaction with one's sleep, is pivotal for adolescent growth, maturation, cognition and overall health. This article aims to identify what ecological factors impact adolescent sleep quality across three distinct sleep ecologies representing a gradient of dense urbanity to small, rural environments with scarce artificial lighting and no Internet.

Methodology

We analyze variation of sleep efficiency, a quantitative measure of sleep quality-defined as the ratio of total time spent asleep to total time dedicated to sleep-in two agricultural indigenous populations and one post-industrial group in Mexico (Campeche = 44, Puebla = 51, Mexico City = 50, respectively). Data collection included actigraphy, sleep diaries, questionnaires, interviews and ethnographic observations. We fit linear models to examine sleep efficiency variation within and between groups.

Results

We found that sleep efficiency varied significantly across sites, being highest in Mexico City (88%) and lowest in Campeche (75%). We found that variation in sleep efficiency was significantly associated with nightly exposure to light and social sleep practices.

Conclusions and implications

Our findings point toward contextual cost-benefits of sleep disruption in adolescence. We highlight the need to prioritize research on adolescent sleep quality across distinct developmental ecologies and its impact on health to improve adolescent wellbeing through evidence-based health practices.

Beyond pulsed inhibition: Alpha oscillations modulate attenuation and amplification of neural activity in the awake resting state

Alpha oscillations are a distinctive feature of the awake resting state of the human brain. However, their functional role in resting-state neuronal dynamics remains poorly understood. Here we show that, during resting wakefulness, alpha oscillations drive an alternation of attenuation and amplification bouts in neural activity. Our analysis indicates that inhibition is activated in pulses that last for a single alpha cycle and gradually suppress neural activity, while excitation is successively enhanced over a few alpha cycles to amplify neural activity. Furthermore, we show that long-term alpha amplitude fluctuations-the "waxing and waning" phenomenon-are an attenuation-amplification mechanism described by a power-law decay of the activity rate in the "waning" phase. Importantly, we do not observe such dynamics during non-rapid eye movement (NREM) sleep with marginal alpha oscillations. The results suggest that alpha oscillations modulate neural activity not only through pulses of inhibition (pulsed inhibition hypothesis) but also by timely enhancement of excitation (or disinhibition).

Criticality of neuronal avalanches in human sleep and their relationship with sleep macro- and micro-architecture

Sleep plays a key role in preserving brain function, keeping brain networks in a state that ensures optimal computation. Empirical evidence indicates that this state is consistent with criticality, where scale-free neuronal avalanches emerge. However, the connection between sleep architecture and brain tuning to criticality remains poorly understood. Here, we characterize the critical behavior of avalanches and study their relationship with sleep macro- and micro-architectures, in particular, the cyclic alternating pattern (CAP). We show that avalanches exhibit robust scaling behaviors, with exponents obeying scaling relations consistent with the mean-field directed percolation universality class. We demonstrate that avalanche dynamics is modulated by the NREM-REM cycles and that, within NREM sleep, avalanche occurrence correlates with CAP activation phases-indicating a potential link between CAP and brain tuning to criticality. The results open new perspectives on the collective dynamics underlying CAP function, and on the relationship between sleep architecture, avalanches, and self-organization to criticality.

Multi-task learning for arousal and sleep stage detection using fully convolutional networks

Objective.Sleep is a critical physiological process that plays a vital role in maintaining physical and mental health. Accurate detection of arousals and sleep stages is essential for the diagnosis of sleep disorders, as frequent and excessive occurrences of arousals disrupt sleep stage patterns and lead to poor sleep quality, negatively impacting physical and mental health. Polysomnography is a traditional method for arousal and sleep stage detection that is time-consuming and prone to high variability among experts.Approach. In this paper, we propose a novel multi-task learning approach for arousal and sleep stage detection using fully convolutional neural networks. Our model, FullSleepNet, accepts a full-night single-channel EEG signal as input and produces segmentation masks for arousal and sleep stage labels. FullSleepNet comprises four modules: a convolutional module to extract local features, a recurrent module to capture long-range dependencies, an attention mechanism to focus on relevant parts of the input, and a segmentation module to output final predictions.Main results.By unifying the two interrelated tasks as segmentation problems and employing a multi-task learning approach, FullSleepNet achieves state-of-the-art performance for arousal detection with an area under the precision-recall curve of 0.70 on Sleep Heart Health Study and Multi-Ethnic Study of Atherosclerosis datasets. For sleep stage classification, FullSleepNet obtains comparable performance on both datasets, achieving an accuracy of 0.88 and an F1-score of 0.80 on the former and an accuracy of 0.83 and an F1-score of 0.76 on the latter.Significance. Our results demonstrate that FullSleepNet offers improved practicality, efficiency, and accuracy for the detection of arousal and classification of sleep stages using raw EEG signals as input.

The Association of Nocturnal Seizures and Interictal Cardiac/Central Autonomic Function in Frontal Lobe Epilepsy: Heart Rate Variability and Central Autonomic Network Analysis

Purpose

Patients with epilepsy frequently experience autonomic dysfunction, closely related to sudden unexplained death in epilepsy (SUDEP). SUDEP occurs most often at night or during sleep, and frequent nocturnal seizures are an established risk factor. This study investigated the influence of nocturnal seizures on autonomic dysfunction in epilepsy.

Patients and Methods

This retrospective study enrolled frontal lobe epilepsy (FLE) patients who performed 24-hour EEG monitoring. All participants were divided into nocturnal FLE (NFLE, > 90% of seizures occurring during sleep) or diurnal FLE (DFLE) groups. EEG and ECG signals were simultaneously obtained during waking and sleep stages. EEG current density source and connectivity analysis of the autonomic network were performed. ECG was analyzed across time and frequency domains heart rate variability (HRV) analysis method was used. The obtained parameters were compared between the NFLE and DFLE groups.

Results

Fifteen NFLE and 16 DFLE patients were enrolled with no significant difference in age, sex, disease duration, seizure frequency, or the number of anti-seizure medications between the two groups. During sleep, a decrease in HRV parameters and an increase of the beta-1 (13-22 Hz) current source density power in the bilateral paracentral lobule (BA4,5,6), precuneus (BA7), and cingulate (BA31) were observed in the NFLE group compared to DFLE group. The NFLE group also showed hyperconnectivity in the central autonomic (12 edges distributed over 10 nodes), sympathetic (2 edges distributed over 3 nodes), and parasympathetic (4 edges distributed over 6 nodes) beta-1 frequency band networks during sleep. During wakefulness, central and cardiac autonomic variables were not significantly different between the NFLE and DFLE groups.

Conclusion

Interictal cardiac and central autonomic dysfunction occurred simultaneously and can be attributed to the brain-heart autonomic axis. Our findings suggest that nocturnal seizures may contribute to interictal autonomic dysfunction during sleep in people with epilepsy.

How we sleep: From brain states to processes

All our lives, we alternate between wakefulness and sleep with direct consequences on our ability to interact with our environment, the dynamics and contents of our subjective experience, and our brain activity. Consequently, sleep has been extensively characterised in terms of behavioural, phenomenological, and physiological changes, the latter constituting the gold standard of sleep research. The common view is thus that sleep represents a collection of discrete states with distinct neurophysiological signatures. However, recent findings challenge such a monolithic view of sleep. Indeed, there can be sharp discrepancies in time and space in the activity displayed by different brain regions or networks, making it difficult to assign a global vigilance state to such a mosaic of contrasted dynamics. Viewing sleep as a multidimensional continuum rather than a succession of non-overlapping and mutually exclusive states could account for these local aspects of sleep. Moving away from the focus on sleep states, sleep can also be investigated through the brain processes that are present in sleep, if not necessarily specific to sleep. This focus on processes rather than states allows to see sleep for what it does rather than what it is, avoiding some of the limitations of the state perspective and providing a powerful heuristic to understand sleep. Indeed, what is sleep if not a process itself that makes up wake up every morning with a brain cleaner, leaner and less cluttered.

MSED: A Multi-Modal Sleep Event Detection Model for Clinical Sleep Analysis

Clinical sleep analysis require manual analysis of sleep patterns for correct diagnosis of sleep disorders. However, several studies have shown significant variability in manual scoring of clinically relevant discrete sleep events, such as arousals, leg movements, and sleep disordered breathing (apneas and hypopneas). We investigated whether an automatic method could be used for event detection and if a model trained on all events (joint model) performed better than corresponding event-specific models (single-event models). We trained a deep neural network event detection model on 1653 individual recordings and tested the optimized model on 1000 separate hold-out recordings. F1 scores for the optimized joint detection model were 0.70, 0.63, and 0.62 for arousals, leg movements, and sleep disordered breathing, respectively, compared to 0.65, 0.61, and 0.60 for the optimized single-event models. Index values computed from detected events correlated positively with manual annotations (r2 = 0.73, r2 = 0.77, r2 = 0.78, respectively). We furthermore quantified model accuracy based on temporal difference metrics, which improved overall by using the joint model compared to single-event models. Our automatic model jointly detects arousals, leg movements and sleep disordered breathing events with high correlation with human annotations. Finally, we benchmark against previous state-of-the-art multi-event detection models and found an overall increase in F1 score with our proposed model despite a 97.5% reduction in model size.

The REM microarousal and REM duration as the potential indicator in paradoxical insomnia

OBJECTIVE

Although paradoxical insomnia is a prevalent subtype of chronic insomnia, the etiology of it is unclear. Contrary to complaints of little or no sleep, polysomnography (PSG) findings show that paradoxical insomnia patients have near normal sleep macrostructure. The purpose of this study is to determine the changes of microstructure and explore the etiology of paradoxical insomnia.

METHODS

The PSG findings of 89 paradoxical insomnia patients were compared with those of 41 gender balanced healthy controls without sleep complaints. All subjects underwent nocturnal PSG recordings. Conventional PSG measures and microarousals were quantified and statistically analyzed. Receiver operating characteristic curve and correlation analysis were used to evaluate the potential of REM sleep microarousals and REM duration as indicators of paradoxical insomnia.

RESULTS

Compared with the controls, paradoxical insomnia patients had no significant differences in sleep macrostructures. Statistical analysis showed that non-rapid eye movement (NREM) microarousals revealed no significant differences between paradoxical insomnia patients and controls. Noticeably, more spontaneous microarousals appeared in rapid eye movement (REM) stage for paradoxical insomnia patients. Based on receiver operating characteristic curve (ROC), the optimal cutoff value of REM sleep microarousals could predict paradoxical insomnia. Furthermore, a positive correlation between microarousals in REM sleep and the duration of REM sleep was presented in paradoxical insomnia patients.

CONCLUSIONS

The frequency of REM microarousals and the duration of REM sleep could reflect the real sleep state of paradoxical insomnia patients. That suggested PSG investigation extended to microarousal could be helpful to understand the etiology in paradoxical insomnia.

Towards automatic EEG cyclic alternating pattern analysis: a systematic review

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

Towards an objective measurement of sleep quality in non-human animals: using the horse as a model species for the creation of sleep quality indices

Sleep disturbance is observed across species, resulting in neurocognitive dysfunction, poor impulse control and poor regulation of negative emotion. Understanding animal sleep disturbance is thus important to understand how environmental factors influence animal sleep and day-to-day welfare. Self-reporting tools for sleep disturbance commonly used in human research to determine sleep quality cannot be transferred to non-verbal animal species research. Human research has, however, successfully used frequency of awakenings to create an objective measurement of sleep quality. The aim of this study was to use a novel sleep-quality scoring system for a non-human mammalian species. Five separate sleep quality indices calculations were developed, using frequency of awakenings, total sleep time and total time spent in different sleep states. These indices were applied to a pre-existing data set of equine sleep behaviour taken from a study investigating the effects of environmental change (lighting and bedding) on the duration of time in different sleep states. Significant treatment effects for index scores both differed and aligned with the original sleep quantity results, thus sleep quality may be a useful alternative measurement of sleep disturbance that could be used to investigate impactful (emotional, cognitive) effects on the animal.

Sleep and vigilance states: Embracing spatiotemporal dynamics

The classic view of sleep and vigilance states is a global stationary perspective driven by the interaction between neuromodulators and thalamocortical systems. However, recent data are challenging this view by demonstrating that vigilance states are highly dynamic and regionally complex. Spatially, sleep- and wake-like states often co-occur across distinct brain regions, as in unihemispheric sleep, local sleep in wakefulness, and during development. Temporally, dynamic switching prevails around state transitions, during extended wakefulness, and in fragmented sleep. This knowledge, together with methods monitoring brain activity across multiple regions simultaneously at millisecond resolution with cell-type specificity, is rapidly shifting how we consider vigilance states. A new perspective incorporating multiple spatial and temporal scales may have important implications for considering the governing neuromodulatory mechanisms, the functional roles of vigilance states, and their behavioral manifestations. A modular and dynamic view highlights novel avenues for finer spatiotemporal interventions to improve sleep function.

Real-Time Detection of Sleep Arousals with a Head-Mounted Accelerometer

Wearable electroencephalography (EEG) enables real-time interactions with the sleeping brain in real-life settings. An important parameter to monitor during these interactions are sleep arousals, i.e. temporary increases in EEG frequency, that compose sleep dynamics, but are challenging to detect without delay. We describe the development of an EEG- and accelerometer(ACC)-based sensing approach to detect arousals in real-time. We investigated the ability of these sensing modalities to timely and accurately detect arousals. When evaluated on 6 nights of mobile recordings, ACC had a median real-time delay of 2 s and was therefore better suited for an early detection of arousals than EEG (4.7 s). The detection performance was independent of sleep stages, but worked better on longer arousals. Our results demonstrate that a head-mounted ACC might be a cost-effective and easy-to-integrate solution for arousal detection where short delays are important or EEG signals are not available.

Number, Duration, and Distribution of Wake Bouts in Patients with Insomnia Disorder: Effect of Daridorexant and Zolpidem

BACKGROUND

Daridorexant, a dual orexin receptor antagonist approved in early 2022, reduces wake after sleep onset without reducing the number of awakenings in patients with insomnia. The objective of this post hoc analysis was to explore the effect of daridorexant on the number, duration, and distribution of night-time wake bouts, and their correlation with daytime functioning.

METHODS

Adults with insomnia disorder were randomized 1:1:1:1:1:1 to placebo, zolpidem 10 mg, or daridorexant 5, 10, 25, or 50 mg in a phase II dose-finding study, and 1:1:1 to placebo or daridorexant 25 or 50 mg in a pivotal phase III study. We analyzed polysomnography data for daridorexant 25 and 50 mg, zolpidem 10 mg, and placebo groups. Polysomnography was conducted at baseline, then on Days 1/2, 15/16, and 28/29 in the phase II study, and Months 1 and 3 in the phase III study. The number, duration, and distribution of wake bouts (≥ 0.5 min) were assessed.

RESULTS

Data from 1111 patients (phase II study: daridorexant 50 mg [n = 61], zolpidem 10 mg [n = 60], placebo [n = 60]; phase III study: daridorexant 25 mg [n = 310], daridorexant 50 mg [n = 310], placebo [n = 310]) were analyzed. Long wake bouts were defined as > 6 min. Compared with placebo, daridorexant 50 mg reduced overall wake time (p < 0.05; all time points, both studies), the odds of experiencing long wake bouts (p < 0.001; Months 1 and 3, phase III study), and the cumulative duration of long wake bouts (p < 0.01; all time points, both studies). Reductions in long wake bouts were sustained through the second half of the night and correlated with improvements in daytime functioning. An increase in the cumulative duration of short wake bouts was observed with daridorexant 50 mg (p < 0.01 vs placebo, Months 1 and 3, phase III study); this was uncorrelated with daytime functioning.

CONCLUSION

Daridorexant reduced the number and duration of longer wake bouts throughout the night compared with placebo, corresponding with improved daytime functioning.

CLINICAL TRIALS

Clinicaltrials.gov NCT02839200 (registered July 20, 2016), NCT03545191 (registered June 4, 2018).

Preliminary Development of a Brainwave Model for K1 Kickboxers Using Quantitative Electroencephalography (QEEG) with Open Eyes

K1 kickboxing fighting is characterised by high injury rates due to the low restrictions of fighting rules. In recent years, much attention has been paid to research on changes in brain function among athletes, including those in combat sports. One of the tools that are likely to help diagnose and assess brain function is quantitative electroencephalography (QEEG). Therefore, the aim of the present study was an attempt to develop a brainwave model using quantitative electroencephalography in competitive K1 kickboxers. A total of thirty-six male individuals were purposefully selected and then comparatively divided into two groups. The first group consisted of specialised K1 kickboxing athletes exhibiting a high level of sports performance (experimental group, n = 18, mean age: 29.83 ± 3.43), while the second group comprised healthy individuals not training competitively (control group, n = 18, mean age: 26.72 ± 1.77). Body composition assessment was performed in all participants before the main measurement process. Measurements were taken for kickboxers during the de-training period, after the sports competition phase. Quantitative electroencephalography of Delta, Theta, Alpha, sensimotor rhytm (SMR), Beta1 and Beta2 waves was performed using electrodes placed on nine measurement points (frontal: FzF3F4, central: CzC3C4, and parietal: PzP3P4) with open eyes. In the course of the analyses, it was found that the level of brain activity among the study population significantly differentiated the K1 formula competitors compared with the reference standards and the control group in selected measurement areas. For kickboxers, all results of the Delta amplitude activity in the area of the frontal lobe were significantly above the normative values for this wave. The highest value was recorded for the average value of the F3 electrode (left frontal lobe), exceeding the norm by 95.65%, for F4 by 74.45% and Fz by 50.6%, respectively. In addition, the Alpha wave standard value for the F4 electrode was exceeded by 14.6%. Normative values were found for the remaining wave amplitudes. Statistically significant differentiation of results, with a strong effect (d = 1.52-8.41), was shown for the activity of Delta waves of the frontal area and the central part of the parietal area (Fz,F3,F4,Cz-p < 0.001), Theta for the frontal area as well as the central and left parietal lobes (Fz,F3,F4-p < 0.001, Cz-p = 0.001, C3-p = 0.018; d = 1.05-3.18), Alpha for the frontal, parietal and occipital areas (for: Fz,F3-p < 0.001, F4-p = 0.036, Cz-p < 0.001, C3-p = 0.001, C4-p = 0.025, Pz-p = 0.010, P3-p < 0.001, P4-p = 0.038; d = 0.90-1.66), SMR for the central parietal and left occipital lobes (Cz-p = 0.043; d = 0.69, P3-p < 0.001; d = 1.62), Beta for the frontal area, occipital and central lobes and left parietal segment (Fz,F3-p < 0.001, F4-p = 0.008, Cz, C3, Pz, P3,P4-p < 0.001; d = 1.27-2.85) and Beta 2 for all measurement areas (Fz, F3, F4, Cz, C3, C4, Pz, P3, P4-p < 0.001; d = 1.90-3.35) among the study groups. Significantly higher results were shown in the kickboxer group compared to the control. In addition to problems with concentration or over-stimulation of neural structures, high Delta waves, with elevated Alpha, Theta and Beta 2 waves, can cause disorders in the limbic system and problems in the cerebral cortex.

Understanding the pathophysiology of sleep bruxism based on human and animal studies: A narrative review

BACKGROUND

Sleep bruxism (SB) is a common sleep disorder that affects approximately 20% of children and 10% of adults. It may cause orodental problems, such as tooth wear, jaw pain, and temporal headaches. However, the pathophysiological mechanisms underlying SB remain largely unknown, and a definitive treatment has not yet been established.

HIGHLIGHT

Human studies involving polysomnography have shown that rhythmic masticatory muscle activity (RMMA) is more frequent in otherwise healthy individuals with SB than in normal individuals. RMMA occurs during light non-rapid eye movement (non-REM) sleep in association with transient arousals and cyclic sleep processes. To further elucidate the neurophysiological mechanisms of SB, jaw motor activities have been investigated in naturally sleeping animals. These animals exhibit various contractions of masticatory muscles, including episodes of rhythmic and repetitive masticatory muscle bursts that occurred during non-REM sleep in association with cortical and cardiac activation, similar to those found in humans. Electrical microstimulation of corticobulbar tracts may also induce rhythmic masticatory muscle contractions during non-REM sleep, suggesting that the masticatory motor system is activated during non-REM sleep via excitatory inputs to the masticatory central pattern generator.

CONCLUSION

This review article summarizes the pathophysiology of SB and putative origin of RMMA in both human and animal studies. Physiological factors contributing to RMMA in SB have been identified in human studies and may also be present in animal models. Further research is required to integrate the findings between human and animal studies to better understand the mechanisms underlying SB.

Serum Ferritin Levels in Severe Obstructive Sleep Apnea

Obstructive sleep apnea (OSA) has been associated with various acute and chronic inflammatory diseases, as has serum ferritin, an intracellular iron storage protein. Little is known about the relationship between severity of OSA and serum ferritin levels in otherwise healthy subjects. In this study, all polysomnographic recordings, serum levels of ferritin, C-reactive protein (CRP), and hemoglobin, as well as patient files from 90 consecutive, otherwise healthy individuals with suspected OSA who presented to a tertiary sleep medical center were retrospectively analyzed. For comparison, three groups were formed based on apnea-hypopnea index (AHI; none or mild OSA: <15/h vs. moderate OSA: 15-30/h vs. severe OSA: >30/h). Serum ferritin levels were significantly positively correlated with AHI (r = 0.3240, p = 0.0020). A clear trend of higher serum ferritin levels was found when patients with severe OSA were compared to those without or with mild OSA. Serum CRP and serum hemoglobin levels did not differ significantly among OSA severity groups. Age and body-mass index (BMI) tended to be higher with increasing OSA severity. The BMI was significant higher in patients with severe OSA compared to those without or with mild (p < 0.001). Therefore, serum ferritin levels may provide a biochemical surrogate marker for OSA severity.

Cortical neuronal assemblies coordinate with EEG microstate dynamics during resting wakefulness

The disruption of cortical assembly activity has been associated with anesthesia-induced loss of consciousness. However, the relationship between cortical assembly activity and the variations in consciousness associated with natural vigilance states remains unclear. Here, we address this by performing vigilance state-specific clustering analysis on 2-photon calcium imaging data from the sensorimotor cortex in combination with global electroencephalogram (EEG) microstate analysis derived from multi-EEG signals obtained over widespread cortical locations. We report no difference in the structure of assembly activity during quiet wakefulness (QW), non-rapid eye movement sleep (NREMs), or REMs, despite the latter two vigilance states being associated with significantly reduced levels of consciousness relative to QW. However, we describe a significant coordination between global EEG microstate dynamics and general local cortical assembly activity during periods of QW, but not sleep. These results suggest that the coordination of cortical assembly activity with global brain dynamics could be a key factor of sustained conscious experience.

A Novel Approach for Sleep Arousal Disorder Detection Based on the Interaction of Physiological Signals and Metaheuristic Learning

The vast majority of sleep disturbances are caused by various types of sleep arousal. To diagnose sleep disorders and prevent health problems such as cardiovascular disease and cognitive impairment, sleep arousals must be accurately detected. Consequently, sleep specialists must spend considerable time and effort analyzing polysomnography (PSG) recordings to determine the level of arousal during sleep. The development of an automated sleep arousal detection system based on PSG would considerably benefit clinicians. We quantify the EEG-ECG by using Lyapunov exponents, fractals, and wavelet transforms to identify sleep stages and arousal disorders. In this paper, an efficient hybrid-learning method is introduced for the first time to detect and assess arousal incidents. Modified drone squadron optimization (mDSO) algorithm is used to optimize the support vector machine (SVM) with radial basis function (RBF) kernel. EEG-ECG signals are preprocessed samples from the SHHS sleep dataset and the PhysioBank challenge 2018. In comparison to other traditional methods for identifying sleep disorders, our physiological signals correlation innovation is much better than similar approaches. Based on the proposed model, the average error rate was less than 2%-7%, respectively, for two-class and four-class issues. Additionally, the proper classification of the five sleep stages is determined to be accurate 92.3% of the time. In clinical trials of sleep disorders, the hybrid-learning model technique based on EEG-ECG signal correlation features is effective in detecting arousals.

Transcutaneous Auricular Vagus Nerve Stimulation Facilitates Cortical Arousal and Alertness

Transcutaneous auricular vagus nerve stimulation (taVNS) is a promising noninvasive technique with potential beneficial effects on human emotion and cognition, including cortical arousal and alertness. However, it remains unclear how taVNS could improve cortical arousal and alertness, which are crucial for consciousness and daily task performance. Here, we aimed to estimate the modulatory effect of taVNS on cortical arousal and alertness and to reveal its underlying neural mechanisms. Sixty subjects were recruited and randomly assigned to either the taVNS group (receiving taVNS for 20 min) or the control group (receiving taVNS for 30 s). The effects of taVNS were evaluated behaviorally using a cue-target pattern task, and neurologically using a resting-state electroencephalogram (EEG). We found that taVNS facilitated the reaction time for the targets requiring right-hand responses and attenuated high-frequency alpha oscillations under the close-eye resting state. Importantly, taVNS-modulated alpha oscillations were positively correlated with the facilitated target detection performance, i.e., reduced reaction time. Furthermore, microstate analysis of the resting-state EEG when the eyes were closed illustrated that taVNS reduced the mean duration of microstate C, which has been proven to be associated with alertness. Altogether, this work provided novel evidence suggesting that taVNS could be an enhancer of both cortical arousal and alertness.

A-phase index: an alternative view for sleep stability analysis based on automatic detection of the A-phases from the cyclic alternating pattern

STUDY OBJECTIVES

Sleep stability can be studied by evaluating the cyclic alternating pattern (CAP) in electroencephalogram (EEG) signals. The present study presents a novel approach for assessing sleep stability, developing an index based on the CAP A-phase characteristics to display a sleep stability profile for a whole night's sleep.

METHODS

Two ensemble classifiers were developed to automatically score the signals, one for "A-phase" and the other for "non-rapid eye movement" estimation. Both were based on three one-dimension convolutional neural networks. Six different inputs were produced from the EEG signal to feed the ensembles' classifiers. A proposed heuristic-oriented search algorithm individually tuned the classifiers' structures. The outputs of the two ensembles were combined to estimate the A-phase index (API). The models can also assess the A-phase subtypes, their API, and the CAP cycles and rate.

RESULTS

Four dataset variations were considered, examining healthy and sleep-disordered subjects. The A-phase average estimation's accuracy, sensitivity, and specificity range was 82%-87%, 72%-80%, and 82%-88%, respectively. A similar performance was attained for the A-phase subtype's assessments, with an accuracy range of 82%-88%. Furthermore, in the examined dataset's variations, the API metric's average error varied from 0.15 to 0.25 (with a median range of 0.11-0.24). These results were attained without manually removing wake or rapid eye movement periods, leading to a methodology suitable to produce a fully automatic CAP scoring algorithm.

CONCLUSIONS

Metrics based on API can be understood as a new view for CAP analysis, where the goal is to produce and examine a sleep stability profile.

Associations between sleep-related heart rate variability and both sleep and symptoms of depression and anxiety: A systematic review

There is a bidirectional relationship between poor sleep and both mood- and anxiety-related disorders, which are among leading global health concerns. Additionally, both disordered sleep and these psychiatric disorders appear to be independently associated with altered autonomic nervous system (ANS) function. We hypothesise that ANS dysregulation during sleep may explain part of the relationship between poor sleep and mood- and anxiety-related disorders. Heart rate variability (HRV) is a frequently used marker of ANS function and gives an indication of ANS input to the heart - in particular, of the relative contributions of sympathetic and parasympathetic activity. A systematic review of PubMed, Scopus and Web of Science yielded 41 studies dealing with sleep, mood- and anxiety-related disorders and sleep-related HRV. Hyperarousal during sleep, reflecting a predominance of sympathetic activation and indicative of ANS dysregulation, may be an important factor in the association between poor sleep and mood-related disorders. Longitudinal studies and mediation analyses are necessary to further understand the potential mediating role of ANS dysregulation on the relationship between poor sleep and mood- and anxiety-related disorders.

[The phenomenon of awakening from sleep and underlying neurophysiological and autonomic mechanisms]

Current research has shown that sleep is not a global process evenly covering the entire brain. The heterogeneity of wakefulness levels in different parts of the brain and differences in their activation thresholds are especially pronounced during the transitions between sleep and wakefulness. During awakening, subcortical brain structures activate first, followed by sensory and motor cortical regions, whereas the associative cortex wakes up much later. Awakening, unlike falling asleep, is not a smooth process. It begins with a short-term sharp activation of the autonomic nervous system and some wake-promoting brain regions. The amplitude of this activity burst is out of proportion to obvious physiological needs and exceeds that observed in later periods of quiet wakefulness. The review discusses the similarities and differences between awakening from sleep and hibernation in hibernating rodents. Data on some clinical consequences of impaired awakening mechanisms are also provided.

Predictive coding, multisensory integration, and attentional control: A multicomponent framework for lucid dreaming

Lucid dreaming (LD) is a mental state in which we realize not being awake but are dreaming while asleep. It often involves vivid, perceptually intense dream images as well as peculiar kinesthetic sensations, such as flying, levitating, or out-of-body experiences. LD is in the cross-spotlight of cognitive neuroscience and sleep research as a particular case to study consciousness, cognition, and the neural background of dream experiences. Here, we present a multicomponent framework for the study and understanding of neurocognitive mechanisms and phenomenological aspects of LD. We propose that LD is associated with prediction error signals arising during sleep and occurring at higher or lower levels of the processing hierarchy. Prediction errors are resolved by generating a superordinate self-model able to integrate ambiguous stimuli arriving from sensory periphery and higher-order cortical regions. While multisensory integration enables lucidity maintenance and contributes to peculiar kinesthetic experiences, attentional control facilitates multisensory integration by dynamically regulating the balance between the influence of top-down mental models and the precision weighting of bottom-up sensory inputs. Our novel framework aims to link neural correlates of LD with current concepts of sleep and arousal regulation and provide testable predictions on interindividual differences in LD as well as neurocognitive mechanisms inducing lucid dreams.

Infra-Low Frequency Neurofeedback rapidly ameliorates schizophrenia symptoms: A case report of the first session

A 38-year-old army officer started therapy in 2020 with a four-year history of auditory hallucinations and delusions of reference, persecution and grandeur, symptoms that were resistant to traditional antipsychotic medications. He follows an integrative psychotherapy program that aims to reduce his anxiety, continues his antipsychotic medications, and has Infra-Low Frequency Neurofeedback. After his initial assessment he had a 40 min session of Infra-Low Frequency Neurofeedback before any other kind of intervention. Before and immediately after the session he completed the SCL-90 scale and the Visual Analog Scale covering 20 aspects of his psychological and physical state as well as his schizophrenic symptoms. This first Neurofeedback session had dramatic effects on his psychotic symptoms, levels of anxiety and psychosomatic condition, before his first psychotherapy session and/or any changes in his antipsychotic medication. The above results have great importance due to the severity and chronicity of schizophrenia. Informed consent was obtained from the participant for the publication of this case report (including all data and images).

A temporal sequence of thalamic activity unfolds at transitions in behavioral arousal state

Awakening from sleep reflects a profound transformation in neural activity and behavior. The thalamus is a key controller of arousal state, but whether its diverse nuclei exhibit coordinated or distinct activity at transitions in behavioral arousal state is unknown. Using fast fMRI at ultra-high field (7 Tesla), we measured sub-second activity across thalamocortical networks and within nine thalamic nuclei to delineate these dynamics during spontaneous transitions in behavioral arousal state. We discovered a stereotyped sequence of activity across thalamic nuclei and cingulate cortex that preceded behavioral arousal after a period of inactivity, followed by widespread deactivation. These thalamic dynamics were linked to whether participants subsequently fell back into unresponsiveness, with unified thalamic activation reflecting maintenance of behavior. These results provide an outline of the complex interactions across thalamocortical circuits that orchestrate behavioral arousal state transitions, and additionally, demonstrate that fast fMRI can resolve sub-second subcortical dynamics in the human brain.

Comparing consumer grade sleep trackers for research purposes: A field study

Sleep tracking has been rapidly developing alongside wearable technologies and digital trackers are increasingly being used in research, replacing diaries and other more laborious methods. In this work, we describe the user expectations and experiences of four different sleep tracking devices used simultaneously during week-long field deployment. The sensor-based data collection was supplemented with qualitative data from a 2-week long daily questionnaire period which overlapped with device usage for a period of 1 week. We compare the sleep data on each of the tracking nights between all four devices, and showcase that while each device has been validated with the polysomnography (PSG) gold standard, the devices show highly varying results in everyday use. Differences between devices for measuring sleep duration or sleep stages on a single night can be up to an average of 1 h 36 min. Study participants provided their expectations and experiences with the devices, and provided qualitative insights into their usage throughout the daily questionnaires. The participants assessed each device according to ease of use, functionality and reliability, and comfortability and effect on sleep disturbances. We conclude the work with lessons learned and recommendations for researchers who wish to conduct field studies using digital sleep trackers, and how to mitigate potential challenges and problems that might arise regarding data validity and technical issues.

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

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

A Review of Equine Sleep: Implications for Equine Welfare

Sleep is a significant biological requirement for all living mammals due to its restorative properties and its cognitive role in memory consolidation. Sleep is ubiquitous amongst all mammals but sleep profiles differ between species dependent upon a range of biological and environmental factors. Given the functional importance of sleep, it is important to understand these differences in order to ensure good physical and psychological wellbeing for domesticated animals. This review focuses specifically on the domestic horse and aims to consolidate current information on equine sleep, in relation to other species, in order to (a) identify both quantitatively and qualitatively what constitutes normal sleep in the horse, (b) identify optimal methods to measure equine sleep (logistically and in terms of accuracy), (c) determine whether changes in equine sleep quantity and quality reflect changes in the animal's welfare, and (d) recognize the primary factors that affect the quantity and quality of equine sleep. The review then discusses gaps in current knowledge and uses this information to identify and set the direction of future equine sleep research with the ultimate aim of improving equine performance and welfare. The conclusions from this review are also contextualized within the current discussions around the “social license” of horse use from a welfare perspective.

Sleep stability in isolated rapid eye movement sleep behavior disorder, Parkinson's disease, and dementia with Lewy bodies

BACKGROUND

Non-REM sleep symptoms remain poorly understood in alpha-synucleinopathies.

AIMS

The aims of the study were to compare sleep stability and transitions, arousals, and sleep cycle structure between isolated rapid eye movement (REM) sleep behavior disorder (iRBD), Parkinson's disease (PD), and dementia with Lewy Bodies (DLB).

MATERIALS AND METHODS

Sleep transition and stability measures were assessed in one-night video-polysomnography records. Transition measures were the number of shifts between Wake and REM, Wake and NREM, and REM and NREM. Stability measures were the number of passages within the same sleep stage. We assessed arousals, the number/duration of sleep cycles (defined as a sequence of any NREM stage to REM), and the duration of N3 and REM sleep in each cycle. These variables were compared between two sets of groups (PD vs. DLB vs. iRBD and RDB+ vs. RBD-).

RESULTS

We assessed 54 PD, 24 DLB, and 21 iRBD patients (54 RBD+, 22 RBD-). There were no significant differences regarding sleep stability measures. Arousal indices in N1 and N2 stages were significantly higher in PD compared with iRBD. 24% of the sample did not have any sleep cycle. PD had significantly fewer cycles than iRBD. Differences became non-significant when adjusting for medication. There was no effect of group or time of night in REM or N3 duration. There were no significant differences between RBD+ and RBD-.

DISCUSSION

There were no significant differences in stability/transition measures. Arousals and disturbance in sleep cycling were higher in PD, but the difference was no longer significant after adjusting for medication.

CONCLUSION

Different alpha-synucleinopathies have a similar degree of non-REM sleep instability, but medication could worsen symptoms in PD.

Temporal relations between peripheral and central arousals in good and poor sleepers

Good sleepers and patients with insomnia symptoms (poor sleepers) were tracked with two measures of arousal; conventional polysomnography (PSG) for electroencephalogram (EEG) assessed cortical arousals, and a peripheral arterial tonometry device was used for the detection of peripheral nervous system (PNS) arousals associated with vasoconstrictions. The relationship between central (cortical) and peripheral (autonomic) arousals was examined by evaluating their close temporal dynamics. Cortical arousals almost invariably were preceded and followed by peripheral activations, while large peripheral autonomic arousals were followed by cortical arousals only half of the time. The temporal contiguity of these two types of arousals was altered in poor sleepers, and poor sleepers displayed a higher number of cortical and peripheral arousals compared with good sleepers. Given the difference in the number of peripheral autonomic arousals between good and poor sleepers, an evaluation of such arousals could become a means of physiologically distinguishing poor sleepers.

Heuristic Optimization of Deep and Shallow Classifiers: An Application for Electroencephalogram Cyclic Alternating Pattern Detection

Methodologies for automatic non-rapid eye movement and cyclic alternating pattern analysis were proposed to examine the signal from one electroencephalogram monopolar derivation for the A phase, cyclic alternating pattern cycles, and cyclic alternating pattern rate assessments. A population composed of subjects free of neurological disorders and subjects diagnosed with sleep-disordered breathing was studied. Parallel classifications were performed for non-rapid eye movement and A phase estimations, examining a one-dimension convolutional neural network (fed with the electroencephalogram signal), a long short-term memory (fed with the electroencephalogram signal or with proposed features), and a feed-forward neural network (fed with proposed features), along with a finite state machine for the cyclic alternating pattern cycle scoring. Two hyper-parameter tuning algorithms were developed to optimize the classifiers. The model with long short-term memory fed with proposed features was found to be the best, with accuracy and area under the receiver operating characteristic curve of 83% and 0.88, respectively, for the A phase classification, while for the non-rapid eye movement estimation, the results were 88% and 0.95, respectively. The cyclic alternating pattern cycle classification accuracy was 79% for the same model, while the cyclic alternating pattern rate percentage error was 22%.

Spike-Wave Seizures, NREM Sleep and Micro-Arousals in WAG/Rij Rats with Genetic Predisposition to Absence Epilepsy: Developmental Aspects

The current study was done in Wistar Albino Glaxo Rijswijk (WAG/Rij) rats, which are genetically prone to develop spontaneous spike-wave discharges (SWDs) and are widely used as a genetic model of absence epilepsy. Here, we examined functional links between sleep and spike-wave epilepsy in aging WAG/Rij rats using advanced techniques of EEG analysis. SWDs, periods of NREM sleep and micro-arousals were automatically detected in three-channel epidural EEG recorded in freely moving WAG/Rij rats consequently at the age 5, 7 and 9 months. We characterized the developmental profile of spike-wave epilepsy in drug-naïve WAG/Rij rats and defined three epi-phenotypes—severe, mild and minor epilepsy. Age-related changes of SWDs were associated with changes in NREM sleep. Several signs of NREM sleep fragmentation were defined in epileptic WAG/Rij rats. It seems that spike-wave epilepsy per se promotes micro-arousals during NREM sleep. However, subjects with a higher number of micro-arousals (and NREM sleep episodes) at the age of 5 months were characterized by a reduction of SWDs between 5 and 7 months of age.

DeepSleep 2.0: Automated Sleep Arousal Segmentation via Deep Learning

DeepSleep 2.0 is a compact version of DeepSleep, a state-of-the-art, U-Net-inspired, fully convolutional deep neural network, which achieved the highest unofficial score in the 2018 PhysioNet Computing Challenge. The proposed network architecture has a compact encoder/decoder structure containing only 740,551 trainable parameters. The input to the network is a full-length multichannel polysomnographic recording signal. The network has been designed and optimized to efficiently predict nonapnea sleep arousals on held-out test data at a 5 ms resolution level, while not compromising the prediction accuracy. When compared to DeepSleep, the obtained experimental results in terms of gross area under the precision–recall curve (AUPRC) and gross area under the receiver operating characteristic curve (AUROC) suggest a lightweight architecture, which can achieve similar prediction performance at a lower computational cost, is realizable.

Pre-Sleep Cognitive Arousal Is Negatively Associated with Sleep Misperception in Healthy Sleepers during Habitual Environmental Noise Exposure: An Actigraphy Study

Specific noises (e.g., traffic or wind turbines) can disrupt sleep and potentially cause a mismatch between subjective sleep and objective sleep (i.e., “sleep misperception”). Some individuals are likely to be more vulnerable than others to noise-related sleep disturbances, potentially as a result of increased pre-sleep cognitive arousal. The aim of the present study was to examine the relationships between pre-sleep cognitive arousal and sleep misperception. Sixteen healthy sleepers participated in this naturalistic, observational study. Three nights of sleep were measured using actigraphy, and each 15-s epoch was classified as sleep or wake. Bedside noise was recorded, and each 15-s segment was classified as containing noise or no noise and matched to actigraphy. Participants completed measures of habitual pre-sleep cognitive and somatic arousal and noise sensitivity. Pre-sleep cognitive and somatic arousal levels were negatively associated with subjective−objective total sleep time discrepancy (p < 0.01). There was an association between sleep/wake and noise presence/absence in the first and last 90 min of sleep (p < 0.001). These results indicate that higher levels of habitual pre-sleep arousal are associated with a greater degree of sleep misperception, and even in healthy sleepers, objective sleep is vulnerable to habitual bedside noise.