Sleep Stages Research Articles

Daytime dexmedetomidine sedation with closed-loop acoustic stimulation alters slow wave sleep homeostasis in healthy adults

BackgroundThe alpha-2 adrenergic agonist dexmedetomidine induces EEG patterns resembling those of non-rapid eye movement (NREM) sleep. Fulfilment of slow wave sleep (SWS) homeostatic needs would address the assumption that dexmedetomidine induces functional biomimetic sleep states. MethodsIn-home sleep EEG recordings were obtained from 13 healthy participants before and after dexmedetomidine sedation. Dexmedetomidine target-controlled infusions and closed-loop acoustic stimulation were implemented to induce and enhance EEG slow waves, respectively. EEG recordings during sedation and sleep were staged using modified American Academy of Sleep Medicine criteria. Slow wave activity (EEG power from 0.5 to 4 Hz) was computed for NREM stage 2 (N2) and NREM stage 3 (N3/SWS) epochs, with the aggregate partitioned into quintiles by time. The first slow wave activity quintile served as a surrogate for slow wave pressure, and the difference between the first and fifth quintiles as a measure of slow wave pressure dissipation. ResultsCompared with pre-sedation sleep, post-sedation sleep showed reduced N3 duration (mean difference of −17.1 min, 95% confidence interval −30.0 to −8.2, P=0.015). Dissipation of slow wave pressure was reduced (P=0.02). Changes in combined durations of N2 and N3 between pre- and post-sedation sleep correlated with total dexmedetomidine dose, (r=−0.61, P=0.03). ConclusionsDaytime dexmedetomidine sedation and closed-loop acoustic stimulation targeting EEG slow waves reduced N3/SWS duration and measures of slow wave pressure dissipation on the post-sedation night in healthy young adults. Thus, the paired intervention induces sleep-like states that fulfil certain homeostatic NREM sleep needs in healthy young adults. Clinical trial registrationClinicalTrials.gov NCT04206059.

The Effectiveness of Hammock Positioning in Regulating Behavioral Patterns in Preterm: A Systematic Review

Background- Preterm newborns benefit from supportive developmental techniques such as hammock placement, which give them a sense of being in the womb that promotes calm, relaxation, and deep restful sleep. Objective- To identify and synthesize the available evidence on the effect of hammock positioning in regulating behavioral patterns and sleep in preterm infants admitted to the NICU Data Sources: We searched the Google Scholar, Pubmed, and Scopus databases for reports of primary research studies using a three-step strategy. We also searched for and reviewed the reference lists of retrieved articles. Methods: We included data from randomized controlled trials, Quasi-experimental and Comparative study measuring the effects of hammock positioning on physiological stress outcomes, defined as oxygen saturation, body temperature, heart rate and respiratory rate, Sleep-wakefulness, and behavioral pattern. The PRISMA model was used to conduct data extraction. We performed a narrative synthesis of all studies when data were available from multiple studies. Results: Five studies were eligible for inclusion. Infants who received hammock positioning has shown to have a higher mean heart rate, oxygen saturation and temperature, Sleep-wakefulness and improved behavioral patterns. Conclusions: Current evidence suggests that hammock positioning in the neonatal intensive care unit setting is a safe method that may significantly affect some of the physiological parameters of stress, improved behavioral patterns and Sleep-wakefulness in preterm infants. However, due to clinical heterogeneity, further studies are needed to assess the effects of physiological stress in the neonatal intensive care unit on the development of preterm infants.

The impact of cannabis use proximal to sleep and cannabinoid metabolites on sleep architecture.

Cannabis is a common sleep aid, however the effects of its use prior to sleep are poorly understood. This study aims to determine the impact of non-medical whole plant cannabis use three hours prior to sleep and measured cannabis metabolites on polysomnogram measures. This is a cross-sectional study of 177 healthy adults who provided detailed cannabis use history, underwent a one-night home sleep test (HST) and had measurement of eleven plasma and urinary cannabinoids, quantified using mass spectroscopy, the morning after the HST. Multivariable models were used to assess the relationship between cannabis use proximal to sleep, which was defined as use three hours before sleep, and individual HST measurements. Correlation between metabolite concentrations and polysomnogram measures were assessed. In adjusted models, cannabis use proximal to sleep was associated with increased wake after sleep onset (median 60.5 versus 45.8 minutes), rate ratio 1.59 (1.22, 2.05), and increased proportion of stage one sleep (median 15.2% versus 12.3%), effect estimate 0.16 (0.06, 0.25). Compared to non-users, frequent cannabis users (>20 days per month) also had increased wake after sleep onset and stage one sleep, in addition to increased REM latency and decreased percent sleep efficiency. Delta-9 tetrahydrocannabinol metabolites correlated with these HST measures. Cannabis use proximal to sleep was associated with minimal changes in sleep architecture. Its use wasn't associated with measures of improved sleep including increased sleep time or efficiency and may be associated with poor quality sleep through increase wake onset and stage 1 sleep.

REM sleep preserves affective response to social stress - experimental study.

Sleep's contribution to affective regulation is insufficiently understood. Previous human research has focused on memorizing or rating affective pictures and less on physiological affective responsivity. This may result in overlapping definitions of affective and declarative memories, and inconsistent deductions for how rapid eye movement sleep (REMS) and slow-wave sleep (SWS) are involved. Literature associates REMS theta (4-8Hz) activity with emotional memory processing, but its contribution to social stress habituation is unknown.Applying selective sleep stage suppression and oscillatory analyses, we investigated how sleep modulated affective adaptation towards social stress and retention of neutral declarative memories. Native Finnish participants (N=29, age M=25.8y) were allocated to REMS or SWS suppression conditions. We measured physiological (skin conductance response, SCR) and subjective stress response and declarative memory retrieval three times: before laboratory night, the next morning, and after three days. Linear mixed models were applied to test the effects of condition and sleep parameters on emotional responsivity and memory retrieval.Greater overnight increase in SCR towards the social stressor emerged after suppressed SWS (intact REMS) relative to suppressed REMS (20.1% vs. 6.1%, p=.016). The overnight SCR increase was positively associated with accumulated REMS theta energy irrespective of the condition (r=.601, p=.002). Subjectively rated affective response and declarative memory recall were comparable between the conditions.The contributions of REMS and SWS to habituation of social stress are distinct. REMS theta activity proposedly facilitates the consolidation of autonomic affective responses. Declarative memory consolidation may not have greater dependence on intact SWS relative to intact REMS.Significance statement Disrupted sleep is a common problem with negative effects on affective regulation. While research indicates that rapid eye movement sleep has a central role in off-line affective processing, the mechanisms are not well defined. We used selective sleep stage suppression to investigate how disrupted sleep and stage-specific neural activity modulated the affective responsivity towards a self-conscious stressor inducing shame. We show that theta band oscillatory activity during rapid eye movement sleep is especially important for preserving the physiological stress response overnight. Understanding sleep-driven affective regulation facilitates development of applications aiming at improving mental wellbeing.

Effects of the low Fowler's sleep position and methazolamide treatment on sleep bruxism: A randomized controlled trial.

Intracranial pressure is one of the determinants of sympathetic activities, and sleep bruxism is associated with increased sympathetic activities. This study aimed to investigate effects of the low Fowler's sleep position and methazolamide treatment on the occurrence of rhythmic masticatory muscle activities/sleep bruxism episodes in patients with sleep bruxism in a randomized controlled trial. Polysomnographic recordings were performed on the patients with sleep bruxism sleeping in the low Fowler's (15°-30°) or supine position (n = 11), and with methazolamide or placebo treatment (100 mg, 3-4 hr before bedtime, P.O., n = 9), and changes in sleep variables and heart rate variance during sleep in the low Fowler's position or with methazolamide treatment were determined. Sleep bruxism index, number of masseter muscle electromyographic bursts per hour of sleep, ratio of rhythmic masticatory muscle activities/sleep bruxism duration to the total sleep duration, index of total limb movements, index of limb movements with rhythmic masticatory muscle activities, and number of sleep bruxism clusters per hour of sleep in the low Fowler's position and after methazolamide intake were significantly smaller (p < 0.05-0.001) than those in the supine position and after placebo intake, respectively. The low-frequency heart rate variance powers during non-rapid eye movement sleep stage 2 (N2) in the low Fowler's position and with methazolamide treatment were significantly lower (p < 0.05) than those during sleep in the supine position and with placebo treatment, respectively. In conclusion, sleep in the low Fowler's position and methazolamide treatment were associated with significant decreases in the occurrence of rhythmic masticatory muscle activities/sleep bruxism episodes, which might be due to a reduction in intracranial pressure and sympathetic activities mainly during non-rapid eye movement sleep stage 2.

Lateral parabrachial FoxP2 neurons regulate respiratory responses to hypercapnia

About half of the neurons in the parabrachial nucleus (PB) that are activated by CO2 are located in the external lateral (el) subnucleus, express calcitonin gene-related peptide (CGRP), and cause forebrain arousal. We report here, in male mice, that most of the remaining CO2-responsive neurons in the adjacent central lateral (PBcl) and Kölliker-Fuse (KF) PB subnuclei express the transcription factor FoxP2 and many of these neurons project to respiratory sites in the medulla. PBclFoxP2 neurons show increased intracellular calcium during wakefulness and REM sleep and in response to elevated CO2 during NREM sleep. Photo-activation of the PBclFoxP2 neurons increases respiration, whereas either photo-inhibition of PBclFoxP2 or genetic deletion of PB/KFFoxP2 neurons reduces the respiratory response to CO2 stimulation without preventing awakening. Thus, augmenting the PBcl/KFFoxP2 response to CO2 in patients with sleep apnea in combination with inhibition of the PBelCGRP neurons may avoid hypoventilation and minimize EEG arousals.

Employing a Convolutional Neural Network to Classify Sleep Stages from EEG Signals Using Feature Reduction Techniques

One of the most essential components of human life is sleep. One of the first steps in spotting abnormalities connected to sleep is classifying sleep stages. Based on the kind and frequency of signals obtained during a polysomnography test, sleep phases can be separated into groups. Accurate classification of sleep stages from electroencephalogram (EEG) signals plays a crucial role in sleep disorder diagnosis and treatment. This study proposes a novel approach that combines feature selection techniques with convolutional neural networks (CNNs) to enhance the classification performance of sleep stages using EEG signals. Firstly, a comprehensive feature selection process was employed to extract discriminative features from raw EEG data, aiming to reduce dimensionality and enhance the efficiency of subsequent classification using mutual information (MI) and analysis of variance (ANOVA) after splitting the dataset into two sets—the training set (70%) and testing set (30%)—then processing it using the standard scalar method. Subsequently, a 1D-CNN architecture was designed to automatically learn hierarchical representations of the selected features, capturing complex patterns indicative of different sleep stages. The proposed method was evaluated on a publicly available EDF-Sleep dataset, demonstrating superior performance compared to traditional approaches. The results highlight the effectiveness of integrating feature selection with CNNs in improving the accuracy and reliability of sleep stage classification from EEG signals, which reached 99.84% with MI-50. This approach not only contributes to advancing the field of sleep disorder diagnosis, but also holds promise for developing more efficient and robust clinical decision support systems.

P.050 Spike source localizations between the three non-REM sleep stages: resemblances to wakefulness and distinctions from REM sleep

Background: Sleep-wake states (SWS) affect the expression of interictal epileptiform discharges (“spikes”), which affects resultant source localization calculations used in epilepsy evaluation. We hypothesize that spike localizations from non-REM sleep 1-3 are most concordant with one another. Methods: We used Standardized low-resolution brain electromagnetic tomography (sLORETA) in Curry 8 software to calculate source localization voxels of spikes in N1-3, REM, or wakefulness (W). We assessed voxel concordance between N1-N2-N3/N1-N2-W/N1-N3-W/N2-N3-W/REM-N1-N2/REM-N1-N3/REM-N2-N3/REM-N1-W/REM-N2-W/REM-N3-W. We classified concordances into those containing and not containing a SWS (e.g. N1 vs. not-N1 = N1-N2-N3/N1-N2-W/N1-N3-W/REM-N1-N2/REM-N1-N3/REM-N1-W vs. REM-N2-W/REM-N3-W/REM-N2-N3/N2-N3-W) for comparison. Results: Concordances did not differ for N1-3 or W. However, concordances with REM were lower than those without REM as a fraction of source localization space (median 32.1% vs. 56.1%, p&lt;0.001) and cortical grey matter (median 20.4% vs. 27.3%, p=0.003). Conclusions: As expected, source localizations from spikes in N1, N2, and N3 did not significantly differ from one another because these three states are constituent members of non-REM sleep. Surprisingly, however, source localizations derived from awake spikes – not a constituent of non-REM sleep – also did not differ. In contrast, REM was most different by reproducibly exhibiting the least three-way concordance. These findings reinforce the unique localizing ability of REM sleep.

P.036 Vagal nerve stimulation in three cases of continuous spike and wave in slow-wave sleep

Background: Continuous spike and waves during slow-wave sleep (CSWS) is a childhood-onset epileptic encephalopathy that is characterized by clinical seizures, electrical status epilepticus during sleep (ESES), and neurocognitive regression. Early intervention can preserve neurocognitive development, and vagus nerve stimulator (VNS) therapy had positive outcomes in the few previously reported case reports. We present three patients with intractable CSWS unresponsive to medications, who had a positive response to VNS therapy. Methods: Review of clinical records of three pediatric patients diagnosed with CSWS were compared for selected clinical outcomes and electrographic data both prior to and in the years following the initiation of VNS therapy. Results: Three patients now aged 13, 16 and 20 years, were treated with VNS following intolerance and a lack of response to multiple medications (5-9) for CSWS. The ketogenic diet was not an option. The CSWS resolved in all three patients, resulting in improved cognitive function. Patient 3 had resurgence of CSWS on EEG when the VNS settings inadvertently reset to the factory settings and improved with adjustment in the cycling. Conclusions: In patients who are unresponsive to medication, VNS provides an alternative option for resolving CSWS to preserve and, in some cases, potentially restore neurocognitive function.

Effect of positive airway pressure treatment on excessive fragmentary myoclonus in 100 sleep-related breathing disorder patients

Study objectivesExcessive fragmentary myoclonus (EFM) is a frequent finding in patients undergoing video-polysomnography (VPSG). We aimed to evaluate the potential effect of sleep-related breathing disorder's treatment with positive airway pressure (PAP) therapy on EFM. MethodsOne hundred consecutive patients with EFM and sleep-related breathing disorder subsequently treated with PAP at the sleep lab of the Medical University of Innsbruck, Department of Neurology, Austria, were included. Each patient underwent two nights of VPSG: the first night without and the second night with PAP therapy. Fragmentary myoclonus was automatically scored with validated software, and fragmentary myoclonus index (FMI) and minutes of non-rapid eye movement (NREM) sleep with EFM (minNREM+EFM) were calculated. ResultsUnder PAP therapy there was a significant decrease in the minNREM+EFM — 60.5 (9.5–161.8) at baseline vs. 37.5 (6.3–168.8) minutes under PAP, p = 0.025. No significant differences were observed for FMI between the two nights. Sleep variables, sleep diagnoses, comorbidities, and medication did not influence FMI or the minNREM+EFM. ConclusionsThe initiation of PAP treatment led to a significant reduction of minNREM+EFM, but not of FMI. The results suggest that PAP therapy might influence the distribution of FM potentials.

Does Targeted Memory Reactivation during SWS and REM sleep have differential effects on mnemonic discrimination and generalization?

Targeted memory reactivation (TMR), or the presentation of learning-related cues during sleep, has been shown to benefit memory consolidation for specific memory traces when applied during non-rapid eye movement (NREM) sleep. Prior studies suggest that TMR during REM sleep may play a role in memory generalization processes, but evidence remains scarce. We tested the hypothesis that TMR exerts a differential effect on distinct mnemonic processes as a function of the sleep state (REM vs. NREM) in which TMR is delivered. Mnemonic discrimination and generalization of semantic categories were investigated using an adapted version of the Mnemonic Similarity Task, before and after sleep. Forty-eight participants encoded pictures from eight semantic categories, each associated with a sound. In the pre-sleep immediate test, they had to discriminate "old" (targets) from "similar" (lures) or "new" (foils) pictures. During sleep, half of the sounds were replayed in slow wave sleep (SWS) or REM sleep. Recognition, discrimination, and generalization memory indices were tested in the morning. These indices did not differ between SWS and REM TMR groups or reactivated and non-reactivated item categories. Additional results suggest a positive effect of TMR on performance for highly similar items mostly relying on mnemonic discrimination processes. During sleep, EEG activity after cue presentation increased in the delta-theta and sigma band in the SWS group, and in the beta band in the REM TMR group. These results do not support the hypothesis of a differential processing of novel memory traces when TMR is administered in distinctive physiological sleep states.

Targeted memory reactivation during non-rapid eye movement sleep enhances neutral, but not negative, components of memory.

Emotionally salient components of memory are preferentially remembered at the expense of accompanying neutral information. This emotional memory trade-off is enhanced over time, and possibly sleep, through a process of memory consolidation. Sleep is believed to benefit memory through a process of reactivation during non-rapid eye movement sleep (NREM). Here, targeted memory reactivation (TMR) was used to manipulate the reactivation of negative and neutral memories during NREM sleep. Thirty-one male and female participants encoded composite scenes containing either a negative or neutral object superimposed on an always neutral background. During NREM sleep, sounds associated with the scene object were replayed, and memory for object and background components was tested the following morning. We found that TMR during NREM sleep improved memory for neutral, but not negative scene objects. This effect was associated with sleep spindle activity, with a larger spindle response following TMR cues predicting TMR effectiveness for neutral items only. These findings therefore do not suggest a role of NREM memory reactivation in enhancing the emotional memory trade-off across a 12-hour period but do align with growing evidence of spindle-mediated memory reactivation in service of neutral declarative memory.Significance statement Memory reactivation during sleep is believed to be a key mechanism facilitating consolidation, the strengthening and stabilisation of memories over time. Emotional memories appear to be preferentially consolidated compared to neutral information, but the role sleep-related memory reactivation in this process is still unclear. Here, we found that experimentally reactivating memories during sleep in humans did not preferentially enhance emotional memory components but did improve neutral memory when tested after one night of sleep. These findings speak against a role of memory reactivation in the early stages of emotional memory consolidation.

What Is a Correct Output by Generative AI From the Viewpoint of Well-Being? – Perspective From Sleep Stage Estimation –

This paper explores an answer to the question of “what is a correct output by generative AI from the viewpoint of well-being?” and discusses an effectiveness of taking account of a biological rhythm for this issue. Concretely, this paper focuses on an estimation of the REM sleep stage as one of sleep stages, and compared its estimations based on random forest as one of the machine learning methods and the ultradian rhythm as one of the biological rhythms. From the human subject experiment, the following implications have been revealed: (1) the REM sleep stage is wrongly estimated in many areas by random forest; and (2) the integration of the REM sleep stage estimation based on the biological rhythm with that based on random forest improves the F-score of the estimated REM sleep stage.

Thalamocortical dynamics during Rapid Eye Movement Sleep in the Mouse Somatosensory Pathway.

Rapid eye movement sleep (REM), also referred to as paradoxical sleep for the striking resemblance of its electroencephalogram (EEG) to the one observed in wakefulness, is characterized by the occurrence of transient events such as limb twitches or facial and rapid eye movements. Here, we investigated the local activity of the primary somatosensory or barrel cortex (S1) in naturally sleeping head-fixed male mice during REM. Through local field potential (LFP) recordings, we uncovered local appearances of spindle waves in the barrel cortex during REM concomitant with strong delta power, challenging the view of a wakefulness-like activity in REM. We further performed extra- and intracellular recordings of thalamic cells in head-fixed mice. Our data show high frequency thalamic bursts of spikes and subthreshold spindle oscillations in about half of the neurons of the ventral posterior medial nucleus which further confirmed the thalamic origin of local cortical spindles in S1 in REM. Cortical spindle oscillations were suppressed, while thalamus spike firing increased, associated with rapid mouse whisker movements and S1 cortical activity transitioned to an activated state. During REM, sensory thalamus and barrel cortex therefore alternate between high (wake-like) and low (non-REM sleep-like) activation states, potentially providing a neuronal substrate for mnemonic processes occurring during this paradoxical sleep stage.Significance Statement Mammalian sleep includes slow wave sleep, or non-rapid eye movement sleep (NREM), and the subsequent REM sleep (REM). At the EEG global level, cortical activity during REM sleep has long been believed to share some similarities with awake cortical activity. The well-defined mouse whisker system offers the unique opportunity to precisely investigate cortical and thalamic dynamics during wakefulness and REM. Combining local field potential (LFP) recordings in primary somatosensory cortex, with single-unit neuronal recordings in its associated thalamic nucleus, we found that during REM, sensory thalamus and barrel cortex alternate between high (wake-like) and low (non-REM sleep-like) activation states, potentially providing a neuronal substrate for mnemonic processes occurring during this paradoxical sleep stage.

Self-supervised learning of accelerometer data provides new insights for sleep and its association with mortality

Sleep is essential to life. Accurate measurement and classification of sleep/wake and sleep stages is important in clinical studies for sleep disorder diagnoses and in the interpretation of data from consumer devices for monitoring physical and mental well-being. Existing non-polysomnography sleep classification techniques mainly rely on heuristic methods developed in relatively small cohorts. Thus, we aimed to establish the accuracy of wrist-worn accelerometers for sleep stage classification and subsequently describe the association between sleep duration and efficiency (proportion of total time asleep when in bed) with mortality outcomes. We developed a self-supervised deep neural network for sleep stage classification using concurrent laboratory-based polysomnography and accelerometry. After exclusion, 1448 participant nights of data were used for training. The difference between polysomnography and the model classifications on the external validation was 34.7 min (95% limits of agreement (LoA): −37.8–107.2 min) for total sleep duration, 2.6 min for REM duration (95% LoA: −68.4–73.4 min) and 32.1 min (95% LoA: −54.4–118.5 min) for NREM duration. The sleep classifier was deployed in the UK Biobank with 100,000 participants to study the association of sleep duration and sleep efficiency with all-cause mortality. Among 66,214 UK Biobank participants, 1642 mortality events were observed. Short sleepers (<6 h) had a higher risk of mortality compared to participants with normal sleep duration of 6–7.9 h, regardless of whether they had low sleep efficiency (Hazard ratios (HRs): 1.58; 95% confidence intervals (CIs): 1.19–2.11) or high sleep efficiency (HRs: 1.45; 95% CIs: 1.16–1.81). Deep-learning-based sleep classification using accelerometers has a fair to moderate agreement with polysomnography. Our findings suggest that having short overnight sleep confers mortality risk irrespective of sleep continuity.

Sleep Stage Estimation by Introduction of Sleep Domain Knowledge to AI: Towards Personalized Sleep Counseling System with GenAI

As a first step towards realizing an AI sleep counselor capable of generating personalized advice, this paper proposes a method for monitoring daily sleep conditions with a mattress sensor. To improve the accuracy of sleep stage estimation and to get accurate sleep structure, this paper introduced sleep domain knowledge to machine learning for improving the accuracy of sleep stage estimation. Concretely, the proposed method estimates ultradian rhythm based on the body movement density, updates prediction probabilities of each sleep stage by ML model and applies WAKE/NR3 detection based on the large/small body movement. Through the human subject experiment, the following implications have been revealed: (1) the proposed method improved the percentage of Accuracy by 65.0% from 61.5% and the QWK score by 0.196 from 0.297 by the conventional machine learning method; (2) the proposed method prevents over-NR12 estimating and is useful for understanding sleep structure by estimating REM sleep and NR3 sleep correctly. (3) the correct estimation of ultradian rhythms significantly improved the sleep stage estimation, with an Accuracy of 77.6% and a QWK score of 0.52 when all subjects' ultradian rhythms were estimated correctly.

NREM3 Sleep Stage Estimation Based on Accelerometer by Body Movement Count and Biological Rhythms

This paper proposes the method by physiological knowledge to improve the estimation performance of the NREM3 sleep based on the waist-attached accelerometer. Specifically, this paper proposes the hybrid method that combines the method based on body movement counts and the method based on biological rhythms of sleep. Through the human subject experiment, the following implications were revealed: (1) the proposed method can outperform famous machine learning models (Random Forest and LSTM) trained with automatically generated features that do not sufficiently incorporate domain knowledge; (2) when the input features are based on domain knowledge, the estimator explicitly designed by humans can outperform the machine learning method; and (3) combining the body movement counting method and the biological rhythm-based method can suppress the error of the body movement counting method and reduce false positives.

Large Muscle Group Movements during Sleep in Restless Leg Syndrome: Neurophysiological and Clinical Implications.

Recently, criteria have been drawn up for Large Muscle group Movements during sleep (LMM), defined as movements lasting for 3-45 seconds in adults, which are often accompanied by changes in sleep stage, arousals, and increases in heart rate. The aim of this study was to characterize LMM in RLS, in order to better evaluate their impact on the neurophysiology of the disorder and, therefore, the possible clinical implications. Consecutive, drug-free patients diagnosed with RLS and controls, aged 18 years or more, were retrospectively enrolled. Leg movement activity - short-interval (SILMS), periodic (PLMS) and isolated (ISOLMS) leg movements during sleep - and LMM were detected and scored. One-hundred patients and 67 controls were recruited. All movement measures were significantly higher in RLS. A significant positive correlation was found between LMM and ISOLMS index but not PLMS index in both groups. LMM index showed a significant negative correlation with total sleep time, sleep efficiency, and percentage of sleep stages N3 and R, as well as a significant positive correlation with number of awakenings, and percentage of sleep stages N1 and N2 only in patients with RLS. No significant correlation was found for either LMM or PLMS index and RLS severity. Different types of movements, including SILMS, ISOLMS, and LMM, play somewhat distinct roles in sleep neurophysiology in RLS. Notably, LMM, a newly recognized category of movements, demonstrate associations with sleep architecture instability and fragmentation, arousals, and awakenings, suggesting potential clinical implications.

Overcoming traps and pitfalls leading to misinterpretation of normal EEG variants and variation of the background activity.

Normal EEG variants, especially the epileptiform variants, can be challenging to interpret because they often have sharp contours and may be confused with "epileptic" interictal activities. However, they can be recognized by the fact that "most spikes or sharp wave discharges of clinical import are followed by a slow wave or a series of slow deflections" (Maulsby, 1971).If there is no wave after the spike, electroencephalographers should be suspicious of artifacts and normal EEG variants. Most normal EEG variants display a single rhythm with the same frequency within the pattern and the morphology remains stable throughout the entire EEG recording with repetition of the same pattern. In case of doubt or difficulties with a standard EEG, it is recommended to undergo an EEG that includes sleep stages with or without sleep deprivation. Finally, epileptiform is an ambiguous term corresponding to an electroencephalographic trait. Epileptiform does not imply a pathological condition, including epilepsy. The clinical context remains the most paramount in the diagnosis of epilepsy. In this article, we propose a set of rules and guidelines to identify normal EEG variants in EEG tracings and normal variation of the background activity. It is not easy to accurately assign a specific/precise name to all EEG activity, but with an orderly approach to EEG that involves using a set of criteria, nonepileptic activity can be identified.

The clinical characteristic of catathrenia: a new look at an old issue-a systematic review of existing literature.

The International Classification of Sleep Disorders categorized catathrenia as a respiratory disorder, but there are doubts whether episodes appear during rapid eye movement (REM) sleep or the non-rapid eye movement (NREM), their duration, and symptoms. The main objectives were to identify the most common features and relations of catathrenia. PubMed, Embase, and Web of Science were searched according to the PRISMA 2020 guidelines. The Joanna Briggs Institute and the ROBINS-I tools were chosen to assess the risk of bias. A total of 288 records were identified, 31 articles were included. The majority of the studies had a moderate risk of bias. 49.57% of episodes occurred during the NREM sleep, while 46% took place during REM. In 60.34% females, catathrenia was more common in the NREM, while in 59.26% of males was in REM sleep (p < 0.05). Females and obese individuals were found to have shorter episodes (p < 0.05). Age was inversely correlated with minimal episodes duration (r = - 0.34). The continuous positive airway pressure (CPAP) therapy was inversely correlated with the maximal episode duration (r = - 0.48). Catathrenia occurs with similar frequency in both genders. The most frequent symptoms embraced groaning, awareness of disturbing bedpartners, and daytime somnolence-not confirmed by the Epworth Sleepiness Scale. The episodes occur more frequently in NREM than in REM sleep. Catathrenia may be considered as a sex-specific condition. The effects of CPAP treatment leading to shortening episodes duration, which may indicate the respiratory origin of catathrenia.