Reduced subjective sleep quality in people rating themselves as electro-hypersensitive: An observational study.

BACKGROUND: Disturbed sleep is among the most frequent health complaints of people exposed to radio frequency electromagnetic fields (RF-EMF) used in mobile telecommunication, particularly in individuals who consider themselves as EMF hypersensitive (EHS). We aimed at investigating whether the EHS status per se is associated with sleep complaints. Because allelic variants of the gene encoding the L-type, voltage-gated calcium channel Cav1.2 (CACNA1C) were previously associated with sleep complaints reminiscent of those reported by EHS individuals, we also explored whether self-rated EHS status and sleep quality associate with these gene variants. METHODS: A total of 2'040 participants (1'381 females) aged 18-30 years completed online, validated questionnaires on EMF sensitivity, subjective sleep quality, daytime sleepiness, mentation during sleep, and diurnal preference. They also provided a saliva sample for genotyping three functional variants of CACNA1C (rs7304986, rs16929277 and rs2302729). Eligible participants endorsing the question "Are you electro-hypersensitive?" were considered as "EHS" (n = 105), those denying this question yet believing to develop detrimental health symptoms due to prevailing electromagnetic pollution as "attributers" (n = 254), and the remaining participants as "non-EHS" (n = 1'406). We combined the EHS and attributers into one group for binary analyses. In exploratory analyses, we then tested possible associations between EMF sensitivity, subjective sleep variables and CACNA1C variants using linear and logistic regression. We used age, sex, level of education, presence of sleep disorders and habitual mobile phone use as covariates and corrected with Benjamini-Hochberg False Discovery Rate for multiple comparisons. RESULTS: The EHS/attributers consistently reported prolonged sleep latency, reduced sleep quality, higher sleepiness and more nocturnal mentation when compared to non-EHS. Habitual mobile phone use was not associated with self-rated sleep latency and sleep quality scores. While the T-allele of variant rs2302729 of CACNA1C was associated with both, self-reported EMF sensitivity and reduced subjective sleep quality, we found no evidence for the hypothesis that EHS mediates impaired sleep quality via this allelic variant. CONCLUSIONS: Irrespective of reported RF-EMF exposure, self-rated EHS/attributers rated subjective sleep quality worse than non-EHS individuals. TRIAL REGISTRATION: Swiss National Clinical Trials Portal (SNCTP000002285) and ClinicalTrials.gov (NCT03074617).

Processing of semantic incongruency at the onset of sleep: An auditory N400 evoked potential study.

The ability to organize self-generated thought into coherent, meaningful semantic representations is a central aspect of human cognition and undergoes regular alterations throughout the day. To investigate whether changes in semantic processing might explain the loss of coherence, logic, and voluntary control over thinking typically accompanying the transition to sleep, we recorded N400 evoked potentials from 44 healthy subjects. Auditory word pairs with varying semantic distance were presented while they were allowed to fall asleep. Using semantic distance and wakefulness level as regressors, we found that semantic distance reliably elicited an N400, and lower wakefulness levels were associated with increased frontal negativity within a similar time range. Additionally, and contrary to our initial hypothesis, the results showed an interaction of semantic distance and wakefulness that is best interpreted as an increased N400 effect with decreasing wakefulness. While these results do not rule out a possible role of semantic processes in the generation of diminished logic and thought control during the transition to sleep, we discuss the possibility of additional brain mechanisms that usually constrain the inner stream of consciousness during wakefulness.

EEG Microstate Dynamics Associated with Dream-Like Experiences During the Transition to Sleep.

Consciousness always requires some representational content; that is, one can only be conscious about something. However, the presence of conscious experience (awareness) alone does not determine whether its content is in line with the external and physical world. Dreams, apart from certain forms of hallucinations, typically consist of non-veridical percepts, which are not recognized as false, but rather considered real. This type of experiences have been described as a state of dissociation between phenomenal and reflective awareness. Interestingly, during the transition to sleep, reflective awareness seems to break down before phenomenal awareness as conscious experience does not immediately fade with reduced wakefulness but is rather characterized by the occurrence of uncontrolled thinking and perceptual images, together with a reduced ability to recognize the internal origin of the experience. Relative deactivation of the frontoparietal and preserved activity in parieto-occipital networks has been suggested to account for dream-like experiences during the transition to sleep. We tested this hypothesis by investigating subjective reports of conscious experience and large-scale brain networks using EEG microstates in 45 healthy young subjects during the transition to sleep. We observed an inverse relationship between cognitive effects and physiological activation; dream-like experiences were associated with an increased presence of a microstate with sources in the superior and middle frontal gyrus and precuneus. Additionally, the presence of a microstate associated with higher-order visual areas was decreased. The observed inverse relationship might therefore indicate a disengagement of cognitive control systems that is mediated by specific, inhibitory EEG microstates.

BizarreVR: Dream-like bizarreness in immersive virtual reality induced changes in conscious experience of reality while leaving spatial presence intact.

Differences in conscious experience of reality occur between waking, dreaming, and psychotic states. Between these states, there are systematic differences in the judgment about the reality of the experience when being confronted with bizarre breaks. However, the mechanisms underlying experience of reality in these different states are still unknown. To investigate the effect of bizarre breaks on experience of reality during the wake state, we propose a new paradigm using dream-like bizarreness and immersive virtual reality. Results showed that the realistic non-bizarre virtual environment induced high levels of reality judgment and spatial presence, whereas the confrontation with bizarre breaks induced high levels of experienced bizarreness. Moreover, experienced bizarreness significantly reduced reality judgment in both the bizarre and the realistic condition. Further, there was no effect of bizarre breaks on spatial presence. These results provide proof of concept for the new method to elicit natural bizarre experience within a realistic scenario.

Validation of Fitbit Charge 2 Sleep and Heart Rate Estimates Against Polysomnographic Measures in Shift Workers: Naturalistic Study.

BACKGROUND: Multisensor fitness trackers offer the ability to longitudinally estimate sleep quality in a home environment with the potential to outperform traditional actigraphy. To benefit from these new tools for objectively assessing sleep for clinical and research purposes, multisensor wearable devices require careful validation against the gold standard of sleep polysomnography (PSG). Naturalistic studies favor validation. OBJECTIVE: This study aims to validate the Fitbit Charge 2 against portable home PSG in a shift-work population composed of 59 first responder police officers and paramedics undergoing shift work. METHODS: A reliable comparison between the two measurements was ensured through the data-driven alignment of a PSG and Fitbit time series that was recorded at night. Epoch-by-epoch analyses and Bland-Altman plots were used to assess sensitivity, specificity, accuracy, the Matthews correlation coefficient, bias, and limits of agreement. RESULTS: Sleep onset and offset, total sleep time, and the durations of rapid eye movement (REM) sleep and non-rapid-eye movement sleep stages N1+N2 and N3 displayed unbiased estimates with nonnegligible limits of agreement. In contrast, the proprietary Fitbit algorithm overestimated REM sleep latency by 29.4 minutes and wakefulness after sleep onset (WASO) by 37.1 minutes. Epoch-by-epoch analyses indicated better specificity than sensitivity, with higher accuracies for WASO (0.82) and REM sleep (0.86) than those for N1+N2 (0.55) and N3 (0.78) sleep. Fitbit heart rate (HR) displayed a small underestimation of 0.9 beats per minute (bpm) and a limited capability to capture sudden HR changes because of the lower time resolution compared to that of PSG. The underestimation was smaller in N2, N3, and REM sleep (0.6-0.7 bpm) than in N1 sleep (1.2 bpm) and wakefulness (1.9 bpm), indicating a state-specific bias. Finally, Fitbit suggested a distribution of all sleep episode durations that was different from that derived from PSG and showed nonbiological discontinuities, indicating the potential limitations of the staging algorithm. CONCLUSIONS: We conclude that by following careful data processing processes, the Fitbit Charge 2 can provide reasonably accurate mean values of sleep and HR estimates in shift workers under naturalistic conditions. Nevertheless, the generally wide limits of agreement hamper the precision of quantifying individual sleep episodes. The value of this consumer-grade multisensor wearable in terms of tackling clinical and research questions could be enhanced with open-source algorithms, raw data access, and the ability to blind participants to their own sleep data.

Homeostatic response to sleep restriction in adolescents.

The high prevalence of chronic sleep restriction in adolescents underscores the importance of understanding how adolescent sleep is regulated under such conditions. One component of sleep regulation is a homeostatic process: if sleep is restricted, then sleep intensity increases. Our knowledge of this process is primarily informed by total sleep deprivation studies and has been incorporated in mathematical models of human sleep regulation. Several animal studies, however, suggest that adaptation occurs in chronic sleep restriction conditions, showing an attenuated or even decreased homeostatic response. We investigated the homeostatic response of adolescents to different sleep opportunities. Thirty-four participants were allocated to one of three groups with 5, 7.5, or 10 h of sleep opportunity per night for five nights. Each group underwent a protocol of nine nights designed to mimic a school week between two weekends: two baseline nights (10 h sleep opportunity), five condition nights (5, 7.5, or 10 h), and two recovery nights (10 h). Measures of sleep homeostasis (slow-wave activity and slow-wave energy) were calculated from frontal and central EEG derivations and compared to predictions derived from simulations of the homeostatic process of the two-process model of sleep regulation. Only minor differences were found between empirical data and model predictions, indicating that sleep homeostasis is preserved under chronic sleep restriction in adolescents. These findings improve our understanding of effects of repetitive short sleep in adolescents.

Automatic Detection of Microsleep Episodes With Deep Learning.

Brief fragments of sleep shorter than 15 s are defined as microsleep episodes (MSEs), often subjectively perceived as sleepiness. Their main characteristic is a slowing in frequency in the electroencephalogram (EEG), similar to stage N1 sleep according to standard criteria. The maintenance of wakefulness test (MWT) is often used in a clinical setting to assess vigilance. Scoring of the MWT in most sleep-wake centers is limited to classical definition of sleep (30 s epochs), and MSEs are mostly not considered in the absence of established scoring criteria defining MSEs but also because of the laborious work. We aimed for automatic detection of MSEs with machine learning, i.e., with deep learning based on raw EEG and EOG data as input. We analyzed MWT data of 76 patients. Experts visually scored wakefulness, and according to recently developed scoring criteria MSEs, microsleep episode candidates (MSEc), and episodes of drowsiness (ED). We implemented segmentation algorithms based on convolutional neural networks (CNNs) and a combination of a CNN with a long-short term memory (LSTM) network. A LSTM network is a type of a recurrent neural network which has a memory for past events and takes them into account. Data of 53 patients were used for training of the classifiers, 12 for validation and 11 for testing. Our algorithms showed a good performance close to human experts. The detection was very good for wakefulness and MSEs and poor for MSEc and ED, similar to the low inter-expert reliability for these borderline segments. We performed a visualization of the internal representation of the data by the artificial neuronal network performing best using t-distributed stochastic neighbor embedding (t-SNE). Visualization revealed that MSEs and wakefulness were mostly separable, though not entirely, and MSEc and ED largely intersected with the two main classes. We provide a proof of principle that it is feasible to reliably detect MSEs with deep neuronal networks based on raw EEG and EOG data with a performance close to that of human experts. The code of the algorithms (https://github.com/alexander-malafeev/microsleep-detection) and data (https://zenodo.org/record/3251716) are available.

Naps not as effective as a night of sleep at dissipating sleep pressure.

The two-process model of sleep posits that two processes interact to regulate sleep and wake: a homeostatic (Process S) and a circadian process (Process C). Process S compensates for sleep loss by increasing sleep duration and intensity. Process C gates the timing of sleep/wake favouring sleep during the circadian night in humans. In this study, we examined whether taking six naps throughout a 24-hr period would result in the same amount of dissipation of homeostatic pressure at the end of the day as a night of sleep, when time in bed is equivalent. Data from 46 participants (10-23 years; mean = 14.5 [±â€…2.9]; 25 females) were analysed. Slow-wave energy, normalized to account for individual differences in slow-wave activity, was used as a measure of sleep homeostasis. In the nap condition, slow-wave energy of six naps distributed equally during a 24-hr period was calculated. In the baseline condition, slow-wave energy was measured after 9-hr time in bed. A paired t-test was used to compare nap and baseline conditions. A linear regression was used to examine whether slow-wave energy varied as a function of age. Slow-wave energy was greater during baseline than the nap condition (p < .001). No association between age and slow-wave energy was found for baseline or nap conditions. Our findings indicate that multiple naps throughout the day are not as effective at dissipating sleep pressure as a night of sleep. This is likely due to the influence of the circadian system, which staves off sleep during certain times of the day.

Fundamentally altered global- and microstate EEG characteristics in Huntington's disease.

OBJECTIVE: Huntington's disease (HD) is characterized by psychiatric, cognitive, and motor disturbances. The study aimed to determine electroencephalography (EEG) global state and microstate changes in HD and their relationship with cognitive and behavioral impairments. METHODS: EEGs from 20 unmedicated HD patients and 20 controls were compared using global state properties (connectivity and dimensionality) and microstate properties (EEG microstate analysis). For four microstate classes (A, B, C, D), three parameters were computed: duration, occurrence, coverage. Global- and microstate properties were compared between groups and correlated with cognitive test scores for patients. RESULTS: Global state analysis showed reduced connectivity in HD and an increasing dimensionality with increasing HD severity. Microstate analysis revealed parameter increases for classes A and B (coverage), decreases for C (occurrence) and D (coverage and occurrence). Disease severity and poorer test performances correlated with parameter increases for class A (coverage and occurrence), decreases for C (coverage and duration) and a dimensionality increase. CONCLUSIONS: Global state changes may reflect higher functional dissociation between brain areas and the complex microstate changes possibly the widespread neuronal death and corresponding functional deficits in brain regions associated with HD symptomatology. SIGNIFICANCE: Combining global- and microstate analyses can be useful for a better understanding of progressive brain deterioration in HD.

Global sleep homeostasis reflects temporally and spatially integrated local cortical neuronal activity.

Sleep homeostasis manifests as a relative constancy of its daily amount and intensity. Theoretical descriptions define 'Process S', a variable with dynamics dependent on global sleep-wake history, and reflected in electroencephalogram (EEG) slow wave activity (SWA, 0.5-4 Hz) during sleep. The notion of sleep as a local, activity-dependent process suggests that activity history must be integrated to determine the dynamics of global Process S. Here, we developed novel mathematical models of Process S based on cortical activity recorded in freely behaving mice, describing local Process S as a function of the deviation of neuronal firing rates from a locally defined set-point, independent of global sleep-wake state. Averaging locally derived Processes S and their rate parameters yielded values resembling those obtained from EEG SWA and global vigilance states. We conclude that local Process S dynamics reflects neuronal activity integrated over time, and global Process S reflects local processes integrated over space.

From thoughtless awareness to effortful cognition: alpha - theta cross-frequency dynamics in experienced meditators during meditation, rest and arithmetic.

Neural activity is known to oscillate within discrete frequency bands and the synchronization between these rhythms is hypothesized to underlie information integration in the brain. Since strict synchronization is only possible for harmonic frequencies, a recent theory proposes that the interaction between different brain rhythms is facilitated by transient harmonic frequency arrangements. In this line, it has been recently shown that the transient occurrence of 2:1 harmonic cross-frequency relationships between alpha and theta rhythms (i.e. falpha ≈ 12 Hz; ftheta ≈ 6 Hz) is enhanced during effortful cognition. In this study, we tested whether achieving a state of 'mental emptiness' during meditation is accompanied by a relative decrease in the occurrence of 2:1 harmonic cross-frequency relationships between alpha and theta rhythms. Continuous EEG recordings (19 electrodes) were obtained from 43 highly experienced meditators during meditation practice, rest and an arithmetic task. We show that the occurrence of transient alpha:theta 2:1 harmonic relationships increased linearly from a meditative to an active cognitive processing state (i.e. meditation < rest < arithmetic task). It is argued that transient EEG cross-frequency arrangements that prevent alpha:theta cross-frequency coupling could facilitate the experience of 'mental emptiness' by avoiding the interaction between the memory and executive components of cognition.

Automatically Detected Microsleep Episodes in the Fitness-to-Drive Assessment.

Study Objectives: Microsleep episodes (MSEs) are short fragments of sleep (1-15 s) that can cause dangerous situations with potentially fatal outcomes. In the diagnostic sleep-wake and fitness-to-drive assessment, accurate and early identification of sleepiness is essential. However, in the absence of a standardised definition and a time-efficient scoring method of MSEs, these short fragments are not assessed in clinical routine. Based on data of moderately sleepy patients, we recently developed the Bern continuous and high-resolution wake-sleep (BERN) criteria for visual scoring of MSEs and corresponding machine learning algorithms for automatic MSE detection, both mainly based on the electroencephalogram (EEG). The present study aimed to investigate the relationship between automatically detected MSEs and driving performance in a driving simulator, recorded in parallel with EEG, and to assess algorithm performance for MSE detection in severely sleepy participants. Methods: Maintenance of wakefulness test (MWT) and driving simulator recordings of 18 healthy participants, before and after a full night of sleep deprivation, were retrospectively analysed. Performance of automatic detection was compared with visual MSE scoring, following the BERN criteria, in MWT recordings of 10 participants. Driving performance was measured by the standard deviation of lateral position and the occurrence of off-road events. Results: In comparison to visual scoring, automatic detection of MSEs in participants with severe sleepiness showed good performance (Cohen's kappa = 0.66). The MSE rate in the MWT correlated with the latency to the first MSE in the driving simulator (r s = -0.54, p < 0.05) and with the cumulative MSE duration in the driving simulator (r s = 0.62, p < 0.01). No correlations between MSE measures in the MWT and driving performance measures were found. In the driving simulator, multiple correlations between MSEs and driving performance variables were observed. Conclusion: Automatic MSE detection worked well, independent of the degree of sleepiness. The rate and the cumulative duration of MSEs could be promising sleepiness measures in both the MWT and the driving simulator. The correlations between MSEs in the driving simulator and driving performance might reflect a close and time-critical relationship between sleepiness and performance, potentially valuable for the fitness-to-drive assessment.

Gentle rocking movements during sleep in the elderly.

Vestibular stimulation in the form of rocking movements could be a promising non-pharmacological intervention for populations with reduced sleep quality, such as the elderly. We hypothesized that rocking movements influence sleep by promoting comfort. We assessed whether gentle rocking movements can facilitate the transition from wake to sleep, increase sleep spindle density and promote deep sleep in elderly people. We assessed self-reported comfort using a pilot protocol including translational movements and movements along a pendulum trajectory with peak linear accelerations between 0.10 and 0.20 m/s2 . We provided whole-night stimulation using the settings rated most comfortable during the pilot study (movements along a pendulum trajectory with peak linear acceleration of 0.15 m/s2 ). Sleep measures (polysomnography) of two baseline and two movement nights were compared. In our sample (n = 19; eight female; mean age: 66.7 years, standard deviation: 3 years), vestibular stimulation using preferred stimulation settings did not improve sleep. A reduction of delta power was observed, suggesting reduced sleep depth during rocking movements. Sleep fragmentation was similar in both conditions. We did not observe a sleep-promoting effect using settings optimized to be comfortable. This finding could imply that comfort is not the underlying mechanism. At frequencies below 0.3 Hz, the otoliths cannot distinguish tilt from translation. Translational movement trajectories, such as used in previous studies reporting positive effects of rocking, could have caused sensory confusion due to a mismatch between vestibular and other sensory information. We propose that this sensory confusion might be essential to the sleep-promoting effect of rocking movements described in other studies.

Automatic detection of microsleep episodes with feature-based machine learning.

STUDY OBJECTIVES: Microsleep episodes (MSEs) are brief episodes of sleep, mostly defined to be shorter than 15 s. In the electroencephalogram (EEG), MSEs are mainly characterized by a slowing in frequency. The identification of early signs of sleepiness and sleep (e.g. MSEs) is of considerable clinical and practical relevance. Under laboratory conditions, the maintenance of wakefulness test (MWT) is often used for assessing vigilance. METHODS: We analyzed MWT recordings of 76 patients referred to the Sleep-Wake-Epilepsy-Center. MSEs were scored by experts defined by the occurrence of theta dominance on ≥1 occipital derivation lasting 1-15 s, whereas the eyes were at least 80% closed. We calculated spectrograms using an autoregressive model of order 16 of 1 s epochs moved in 200 ms steps in order to visualize oscillatory activity and derived seven features per derivation: power in delta, theta, alpha and beta bands, ratio theta/(alpha + beta), quantified eye movements, and median frequency. Three algorithms were used for MSE classification: support vector machine (SVM), random forest (RF), and an artificial neural network (long short-term memory [LSTM] network). Data of 53 patients were used for the training of the classifiers, and 23 for testing. RESULTS: MSEs were identified with a high performance (sensitivity, specificity, precision, accuracy, and Cohen's kappa coefficient). Training revealed that delta power and the ratio theta/(alpha + beta) were most relevant features for the RF classifier and eye movements for the LSTM network. CONCLUSIONS: The automatic detection of MSEs was successful for our EEG-based definition of MSEs, with good performance of all algorithms applied.

Microsleep episodes in the borderland between wakefulness and sleep.

STUDY OBJECTIVES: The wake-sleep transition zone represents a poorly defined borderland, containing, for example, microsleep episodes (MSEs), which are of potential relevance for diagnosis and may have consequences while driving. Yet, the scoring guidelines of the American Academy of Sleep Medicine (AASM) completely neglect it. We aimed to explore the borderland between wakefulness and sleep by developing the Bern continuous and high-resolution wake-sleep (BERN) criteria for visual scoring, focusing on MSEs visible in the electroencephalography (EEG), as opposed to purely behavior- or performance-defined MSEs. METHODS: Maintenance of Wakefulness Test (MWT) trials of 76 randomly selected patients were retrospectively scored according to both the AASM and the newly developed BERN scoring criteria. The visual scoring was compared with spectral analysis of the EEG. The quantitative EEG analysis enabled a reliable objectification of the visually scored MSEs. For less distinct episodes within the borderland, either ambiguous or no quantitative patterns were found. RESULTS: As expected, the latency to the first MSE was significantly shorter in comparison to the sleep latency, defined according to the AASM criteria. In certain cases, a large difference between the two latencies was observed and a substantial number of MSEs occurred between the first MSE and sleep. Series of MSEs were more frequent in patients with shorter sleep latencies, while isolated MSEs were more frequent in patients who did not reach sleep. CONCLUSION: The BERN criteria extend the AASM criteria and represent a valuable tool for in-depth analysis of the wake-sleep transition zone, particularly important in the MWT.

Reduced Brain Electric Activity and Functional Connectivity in Bipolar Euthymia: An sLORETA Source Localization Study.

Bipolar disorder (BD) is a chronic illness with a relapsing and remitting time course. Relapses are manic or depressive in nature and intermitted by euthymic states. During euthymic states, patients lack the criteria for a manic or depressive diagnosis, but still suffer from impaired cognitive functioning as indicated by difficulties in executive and language-related processing. The present study investigated whether these deficits are reflected by altered intracortical activity in or functional connectivity between brain regions involved in these processes such as the prefrontal and the temporal cortices. Vigilance-controlled resting state EEG of 13 euthymic BD patients and 13 healthy age- and sex-matched controls was analyzed. Head-surface EEG was recomputed into intracortical current density values in 8 frequency bands using standardized low-resolution electromagnetic tomography. Intracortical current densities were averaged in 19 evenly distributed regions of interest (ROIs). Lagged coherences were computed between each pair of ROIs. Source activity and coherence measures between patients and controls were compared (paired t tests). Reductions in temporal cortex activity and in large-scale functional connectivity in patients compared to controls were observed. Activity reductions affected all 8 EEG frequency bands. Functional connectivity reductions affected the delta, theta, alpha-2, beta-2, and gamma band and involved but were not limited to prefrontal and temporal ROIs. The findings show reduced activation of the temporal cortex and reduced coordination between many brain regions in BD euthymia. These activation and connectivity changes may disturb the continuous frontotemporal information flow required for executive and language-related processing, which is impaired in euthymic BD patients.

A Novel Approach to Assess Sleep-Related Rhythmic Movement Disorder in Children Using Automatic 3D Analysis.

Background: Unlike other episodic sleep disorders in childhood, there are no agreed severity indices for rhythmic movement disorder. While movements can be characterized in detail by polysomnography, in our experience most children inhibit rhythmic movement during polysomnography. Actigraphy and home video allow assessment in the child's own environment, but both have limitations. Standard actigraphy analysis algorithms fail to differentiate rhythmic movements from other movements. Manual annotation of 2D video is time consuming. We aimed to develop a sensitive, reliable method to detect and quantify rhythmic movements using marker free and automatic 3D video analysis. Method: Patients with rhythmic movement disorder (n = 6, 4 male) between age 5 and 14 years (M: 9.0 years, SD: 4.2 years) spent three nights in the sleep laboratory as part of a feasibility study (https://clinicaltrials.gov/ct2/show/NCT03528096). 2D and 3D video data recorded during the adaptation and baseline nights were analyzed. One ceiling-mounted camera captured 3D depth images, while another recorded 2D video. We developed algorithms to analyze the characteristics of rhythmic movements and built a classifier to distinguish between rhythmic and non-rhythmic movements based on 3D video data alone. Data from 3D automated analysis were compared to manual 2D video annotations to assess algorithm performance. Novel indices were developed, specifically the rhythmic movement index, frequency index, and duration index, to better characterize severity of rhythmic movement disorder in children. Result: Automatic 3D video analysis demonstrated high levels of agreement with the manual approach indicated by a Cohen's kappa >0.9 and F1-score >0.9. We also demonstrated how rhythmic movement assessment can be improved using newly introduced indices illustrated with plots for ease of visualization. Conclusion: 3D video technology is widely available and can be readily integrated into sleep laboratory settings. Our automatic 3D video analysis algorithm yields reliable quantitative information about rhythmic movements, reducing the burden of manual scoring. Furthermore, we propose novel rhythmic movement disorder severity indices that offer a means to standardize measurement of this disorder in both clinical and research practice. The significance of the results is limited due to the nature of a feasibility study and its small number of samples. A larger follow up study is needed to confirm presented results.

Brain dynamics during the sleep onset transition: An EEG source localization study.

EEG source localization is an essential tool to reveal the cortical sources underlying brain oscillatory activity. We applied LORETA, a technique of EEG source localization, to identify the principal brain areas involved in the process of falling asleep (sleep onset, SO). We localized the contributing brain areas of activity in the classical frequency bands and tracked their temporal evolution (in 2-min intervals from 2 min prior to SO up to 10 min after SO) during a baseline night and subsequent recovery sleep after total sleep deprivation of 40 h. Delta activity (0.5-5 Hz) gradually increased both in baseline and recovery sleep, starting in frontal areas and finally involving the entire cortex. This increase was steeper in the recovery condition. The evolution of sigma activity (12-16 Hz) resembled an inverted U-shape in both conditions and the activity was most salient in the parietal cortex. In recovery, sigma activity reached its maximum faster than in baseline, but attained lower levels. Theta activity (5-8 Hz) increased with time in large parts of the occipital lobe (baseline and recovery) and in recovery involved additionally frontal areas. Changes in alpha activity (8-12 Hz) at sleep onset involved large areas of the cortex, whereas activity in the beta range (16-24 Hz) was restricted to small cortical areas. The dynamics in recovery could be considered as a "fast-forward version" of the one in baseline. Our results confirm that the process of falling asleep is neither spatially nor temporally a uniform process and that different brain areas might be falling asleep at a different speed potentially reflecting use dependent aspects of sleep regulation.

Oscillatory patterns in the electroencephalogram at sleep onset.

Falling asleep is a gradually unfolding process. We investigated the role of various oscillatory activities including sleep spindles and alpha and delta oscillations at sleep onset (SO) by automatically detecting oscillatory events. We used two datasets of healthy young males, eight with four baseline recordings, and eight with a baseline and recovery sleep after 40 h of sustained wakefulness. We analyzed the 2-min interval before SO (stage 2) and the five consecutive 2-min intervals after SO. The incidence of delta/theta events reached its maximum in the first 2-min episode after SO, while the frequency of them was continuously decreasing from stage 1 onwards, continuing over SO and further into deeper sleep. Interestingly, this decrease of the frequencies of the oscillations were not affected by increased sleep pressure, in contrast to the incidence which increased. We observed an increasing number of alpha events after SO, predominantly frontally, with their prevalence varying strongly across individuals. Sleep spindles started to occur after SO, with first an increasing then a decreasing incidence and a continuous decrease in their frequency. Again, the frequency of the spindles was not altered after sleep deprivation. Oscillatory events revealed derivation dependent aspects. However, these regional aspects were not specific of the process of SO but rather reflect a general sleep related phenomenon. No individual traits of SO features (incidence and frequency of oscillations) and their dynamics were observed. Delta/theta events are important features for the analysis of SO in addition to slow waves.

Automatic artefact detection in single-channel sleep EEG recordings.

Quantitative electroencephalogram analysis (e.g. spectral analysis) has become an important tool in sleep research and sleep medicine. However, reliable results are only obtained if artefacts are removed or excluded. Artefact detection is often performed manually during sleep stage scoring, which is time consuming and prevents application to large datasets. We aimed to test the performance of mostly simple algorithms of artefact detection in polysomnographic recordings, derive optimal parameters and test their generalization capacity. We implemented 14 different artefact detection methods, optimized parameters for derivation C3A2 using receiver operator characteristic curves of 32 recordings, and validated them on 21 recordings of healthy participants and 10 recordings of patients (different laboratory) and considered the methods as generalizable. We also compared average power density spectra with artefacts excluded based on algorithms and expert scoring. Analyses were performed retrospectively. We could reliably identify artefact contaminated epochs in sleep electroencephalogram recordings of two laboratories (healthy participants and patients) reaching good sensitivity (specificity 0.9) with most algorithms. The best performance was obtained using fixed thresholds of the electroencephalogram slope, high-frequency power (25-90 Hz or 45-90 Hz) and residuals of adaptive autoregressive models. Artefacts in electroencephalogram data can be reliably excluded by simple algorithms with good performance, and average electroencephalogram power density spectra with artefact exclusion based on algorithms and manual scoring are very similar in the frequency range relevant for most applications in sleep research and sleep medicine, allowing application to large datasets as needed to address questions related to genetics, epidemiology or precision medicine.