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Background and purpose: Sleep disorders are increasingly implicated as risk factors for stroke, as well as a determinant of stroke outcome. They can also occur secondary to the stroke itself. In this review, we describe the variety of different sleep disorders associated with stroke and analyze their effect on stroke risk and outcome. Methods: A search term-based literature review ("sleep," "insomnia," "narcolepsy," "restless legs syndrome," "periodic limb movements during sleep," "excessive daytime sleepiness" AND "stroke" OR "cerebrovascular" in PubMed; "stroke" and "sleep" in ClinicalTrials.gov) was performed. English articles from 1990 to March 2023 were considered. Results: Increasing evidence suggests that sleep disorders are risk factors for stroke. In addition, sleep disturbance has been reported in half of all stroke sufferers; specifically, an increase is not only sleep-related breathing disorders but also periodic limb movements during sleep, narcolepsy, rapid eye movement (REM) sleep behavior disorder, insomnia, sleep duration, and circadian rhythm sleep–wake disorders. Poststroke sleep disturbance has been associated with worse outcome. Conclusion: Sleep disorders are risk factors for stroke and associated with worse stroke outcome. They are also a common consequence of stroke. Recent guidelines suggest screening for sleep disorders after stroke. It is possible that treatment of sleep disorders could both reduce stroke risk and improve stroke outcome, although further data from clinical trials are required. [ABSTRACT FROM AUTHOR]

Social media platforms have surpassed cultural and linguistic boundaries, thus enabling online communication worldwide. However, the expanded use of various languages has intensified the challenge of online detection of hate speech content. Despite the release of multiple Natural Language Processing (NLP) solutions implementing cutting-edge machine learning techniques, the scarcity of data, especially labeled data, remains a considerable obstacle, which further requires the use of semisupervised approaches along with Generative Artificial Intelligence (Generative AI) techniques. This paper introduces an innovative approach, a multilingual semisupervised model combining Generative Adversarial Networks (GANs) and Pretrained Language Models (PLMs), more precisely mBERT and XLM-RoBERTa. Our approach proves its effectiveness in the detection of hate speech and offensive language in Indo-European languages (in English, German, and Hindi) when employing only 20% annotated data from the HASOC2019 dataset, thereby presenting significantly high performances in each of multilingual, zero-shot crosslingual, and monolingual training scenarios. Our study provides a robust mBERT-based semisupervised GAN model (SS-GAN-mBERT) that outperformed the XLM-RoBERTa-based model (SS-GAN-XLM) and reached an average F1 score boost of 9.23% and an accuracy increase of 5.75% over the baseline semisupervised mBERT model. [ABSTRACT FROM AUTHOR]

Natural language processing (NLP) has significantly transformed in the last decade, especially in the field of language modeling. Large language models (LLMs) have achieved SOTA performances on natural language understanding (NLU) and natural language generation (NLG) tasks by learning language representation in self-supervised ways. This paper provides a comprehensive survey to capture the progression of advances in language models. In this paper, we examine the different aspects of language models, which started with a few million parameters but have reached the size of a trillion in a very short time. We also look at how these LLMs transitioned from task-specific to task-independent to task-and-language-independent architectures. This paper extensively discusses different pretraining objectives, benchmarks, and transfer learning methods used in LLMs. It also examines different finetuning and in-context learning techniques used in downstream tasks. Moreover, it explores how LLMs can perform well across many domains and datasets if sufficiently trained on a large and diverse dataset. Next, it discusses how, over time, the availability of cheap computational power and large datasets have improved LLM's capabilities and raised new challenges. As part of our study, we also inspect LLMs from the perspective of scalability to see how their performance is affected by the model's depth, width, and data size. Lastly, we provide an empirical comparison of existing trends and techniques and a comprehensive analysis of where the field of LLM currently stands. [ABSTRACT FROM AUTHOR]

BackgroundPeriodic limb movement disorder (PLMD) and obstructive sleep apnea (OSA) are overlapping clinical syndromes with common risk factors. However, current literature has failed to establish a clear pathophysiological link between them. Thus, little is known about periodic limb movements (PLM) in otherwise healthy patients with suspected OSA.MethodsWe performed a retrospective analysis of 112 patients (age: 44.5 ± 12.0 years, 14.3% female) with suspected OSA who underwent full night polysomnography for the first time. Patients with chronic diseases of any kind, recent infections, malignancies, or daily or regular use of any type of medication were excluded. Group comparisons were made based on the severity of OSA (using the apnea hypopnea index, AHI) or the periodic limb movement index (PLMI).ResultsBoth, PLMI and the total number of periodic limb movements during sleep (PLMS), showed a significant increase in patients with severe OSA. In addition, AHI and apnea index (AI) were significantly higher in patients with PLMI >15/h, with a similar trend for hypopnea index (HI) (p 0.05, respectively). PLMI was significantly positive correlated with AHI, AI, and HI (r = 0.392, p 15/h were significantly older (p 15/h.ConclusionWe found a significant association between the severity of OSA and PLM in our study population with suspected OSA but without other comorbidities. PLMI and PLMS were significantly increased in patients with severe OSA. Future prospective studies with larger collectives should verify the presented results and should include mechanistic aspects in their evaluation.

Social media platforms have surpassed cultural and linguistic boundaries, thus enabling online communication worldwide. However, the expanded use of various languages has intensified the challenge of online detection of hate speech content. Despite the release of multiple Natural Language Processing (NLP) solutions implementing cutting-edge machine learning techniques, the scarcity of data, especially labeled data, remains a considerable obstacle, which further requires the use of semisupervised approaches along with Generative Artificial Intelligence (Generative AI) techniques. This paper introduces an innovative approach, a multilingual semisupervised model combining Generative Adversarial Networks (GANs) and Pretrained Language Models (PLMs), more precisely mBERT and XLM-RoBERTa. Our approach proves its effectiveness in the detection of hate speech and offensive language in Indo-European languages (in English, German, and Hindi) when employing only 20% annotated data from the HASOC2019 dataset, thereby presenting significantly high performances in each of multilingual, zero-shot crosslingual, and monolingual training scenarios. Our study provides a robust mBERT-based semisupervised GAN model (SS-GAN-mBERT) that outperformed the XLM-RoBERTa-based model (SS-GAN-XLM) and reached an average F1 score boost of 9.23% and an accuracy increase of 5.75% over the baseline semisupervised mBERT model.

Natural language processing (NLP) has significantly transformed in the last decade, especially in the field of language modeling. Large language models (LLMs) have achieved SOTA performances on natural language understanding (NLU) and natural language generation (NLG) tasks by learning language representation in self-supervised ways. This paper provides a comprehensive survey to capture the progression of advances in language models. In this paper, we examine the different aspects of language models, which started with a few million parameters but have reached the size of a trillion in a very short time. We also look at how these LLMs transitioned from task-specific to task-independent to task-and-language-independent architectures. This paper extensively discusses different pretraining objectives, benchmarks, and transfer learning methods used in LLMs. It also examines different finetuning and in-context learning techniques used in downstream tasks. Moreover, it explores how LLMs can perform well across many domains and datasets if sufficiently trained on a large and diverse dataset. Next, it discusses how, over time, the availability of cheap computational power and large datasets have improved LLM’s capabilities and raised new challenges. As part of our study, we also inspect LLMs from the perspective of scalability to see how their performance is affected by the model’s depth, width, and data size. Lastly, we provide an empirical comparison of existing trends and techniques and a comprehensive analysis of where the field of LLM currently stands.

A USB-based learning management system, called PLMS, was proposed and developed to achieve innovative teaching and learning. The objective of this paper was to measure the acceptance of PLMS in terms of performance expectancy, effort expectancy, attitudes, facilitating conditions, self-efficacy, anxiety, and behavioral intention as perceived by the respondents. Respondents of the study are 104 students from three selected higher education institutions in the Philippines. A survey questionnaire was used using the Unified Theory of Acceptance and Use of Technology model. The overall mean of the degree of acceptance of PLMS is 2.62, described as "agree." The study concludes that the newly developed USB-based learning management system is acceptable among higher education students. There is evidence that the students embrace the newly developed portable learning management system. The study recommends extensive utilization of the newly developed USB-based learning management system in higher education institutions. [ABSTRACT FROM AUTHOR]

N-Linked glycosylation is crucial for various biological processes such as protein folding, immune response, and cellular transport. Traditional experimental methods for determining N-linked glycosylation sites entail substantial time and labor investment, which has led to the development of computational approaches as a more efficient alternative. However, due to the limited availability of 3D structural data, existing prediction methods often struggle to fully utilize structural information and fall short in integrating sequence and structural information effectively. Motivated by the progress of protein pretrained language models (pLMs) and the breakthrough in protein structure prediction, we introduced a high-accuracy model called CoNglyPred. Having compared various pLMs, we opt for the large-scale pLM ESM-2 to extract sequence embeddings, thus mitigating certain limitations associated with manual feature extraction. Meanwhile, our approach employs a graph transformer network to process the 3D protein structures predicted by AlphaFold2. The final graph output and ESM-2 embedding are intricately integrated through a co-attention mechanism. Among a series of comprehensive experiments on the independent test dataset, CoNglyPred outperforms state-of-the-art models and demonstrates exceptional performance in case study. In addition, we are the first to report the uncertainty of N-linked glycosylation predictors using expected calibration error and expected uncertainty calibration error., (© 2024 Wiley‐VCH GmbH.)

Depression and periodic limb movement disease are both common disorders which frequently co-exist. Furthermore, antidepressants are known to cause and worsen periodic limb movements in sleep, which can worsen the quality of sleep and subsequently daytime symptoms. However, the effect of antidepressants on periodic limb movements is not uniform and depends on their mechanism of action. In this review we summarise the knowledge on the mechanism of periodic limb movements in sleep, and how changes in the concentration of neurotransmitters can contribute to them. We comprehensively evaluate the literature on antidepressants induced periodic limb movement in sleep. Based on this, we suggest clinical implications and further focus on research., Competing Interests: Declaration of competing interest None., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Study objectivesTo examine the association between periodic limb movements in sleep (PLMS) and change in selected aspects of cognition in community-dwelling older men.MethodsWe studied 2,636 older men without dementia who underwent in-home polysomnography with measurement of the periodic limb movement index (PLMI) and periodic limb movement arousal index (PLMAI) using piezoelectric sensors. Random-effects models and logistic regression were used to examine the association between PLMI, PLMAI, and 3- to 4-y change in cognition.ResultsAfter multivariable adjustment, men with a high PLMI had greater decline on the Trail Making Test - Part B (P trend = 0.02); those with a PLMI ≥ 30 were 48% more likely (odds ratio = 1.48, 95% confidence interval = 1.05-2.07) to experience the development of significant cognitive impairment (≥ 1 SD above mean change). Further adjustment for sleep efficiency, nocturnal hypoxemia, or dopaminergic medication use and analysis among men without Parkinson disease (n = 2,607) showed similar findings. No significant association was found for PLMAI or for Modified Mini-Mental State Examination scores.ConclusionsAmong older men without dementia, higher PLMS frequency was associated with greater decline in cognition, particularly in executive function.

The Muskingum-based (MK-based) distributions and their probability weighted moments (PWMs) have been used for frequency calculation of hydrological data that contain zero values. However, the performance of different MK-based distributions have not been compared and evaluated. Moreover, the partial L-moments (PLMs), which are used for analyzing censored samples, have not been used for frequency calculation of such hydrological data. To obtain the most effective method, this study compares and evaluates the performance of four MK-based distributions by fitting 64 monthly precipitation series and using the ordinary least square (OLS) criterion, Akaike information criterion (AIC), residual square sum criterion (RSS), and the Quasi-optimal Deterministic coefficient (QD). The distributions include ​exponential distribution combines with Dirac delta function (M-like), two-parameter gamma distribution (GA2) combines with Dirac delta function (DGA2), two-parameter generalized Pareto distribution combines with Dirac delta function (DGP2), and two-parameter Weibull distribution (WB2) combines with Dirac delta function (DWB2). The applicability of PLMs were also tested and PLMs of four traditional distributions, including GA2, WB2, generalized extreme value distribution (GEV) and three-parameter generalized Pareto distribution (GP3) were used in application. Results showed that the PLMs are feasible for frequency calculation of hydrological data with zeros. The DGP2 and GP3 are superior to the other MK-based distributions and traditional distributions, respectively. The DGP2 distribution is the optimal choice in most cases and is more universal than the other distributions. [ABSTRACT FROM AUTHOR]

Purpose: This study evaluates the differences in periodic leg movement (PLM) rates for Restless Legs Syndrome (RLS) and healthy controls when using the updated PLM scoring criteria developed by IRLSSG in 2016 versus the prior PLM scoring criteria developed by IRLSSG in 2006. Four major problems with the prior standards had been objectively identified, i.e. minimum inter-movement interval should be 10 not 5 s, non-PLM leg movements should end any preceding PLM sequence, a leg movement (LM) can be any length > 0.5 s, and a PLM should be a persisting movement not a couple or a series of closely spaced, very brief events. Each of these led to including, erroneously, various random leg movements as PLM. Correcting these problems was expected to increase specificity, reducing the number of PLM detected, particularly in situations producing relatively more random leg movements, e.g. wake vs. sleep and controls without PLMD vs. RLS patients. Methods: This study evaluated the putative benefits of the updated, 2016-scoring criteria. The LMs from 42 RLS patients and 30 age- and gender-matched controls were scored for PLMS and PLMW from standard all-night PSG recordings using both 2006 and 2016 WASM criteria. Results/Conclusion: The results confirmed that that the 2016 compared to the 2006 criteria generally decreased the PLM rates with particularly large decreases for the conditions with more random non-PLM events, e.g. wake times and normal healthy controls. This supported the view that the new criteria succeeded in increasing the specificity of PLM detection. Moreover, the changes in PLM rates were generally small for the conditions with relatively few random LM, e.g. RLS and sleep. Thus the bulk of existing PLMS research does not require reconsideration of results, with possible exception of special situations with relatively more random leg movements than periodic leg movements, e.g. wake, healthy normals and children. [ABSTRACT FROM AUTHOR]

Purpose: Clinical and animal studies indicate frequent small micro-arousals (McA) fragment sleep leading to health complications. McA in humans is defined by changes in EEG and EMG during sleep. Complex EEG recordings during the night are usually required to detect McA-limiting large-scale, prospective studies on McA and their impact on health. Even with the use of EEG, reliably measuring McA can be difficult because of low inter-scorer reliability. Surrogate measures in place of EEG could provide easier and possibly more reliable measures of McA. These have usually involved measuring heart rate and arm movements. They have not provided a reliable measurement of McA in part because they cannot adequately detect short wake periods and periods of wake after sleep onset. Leg movements in sleep (LMS) offer an attractive alternative. LMS and cortical arousal, including McA, commonly occur together. Not all McA occur with LMS, but the most clinically significant ones may be those with LMS [1]. Conversely, most LMS do not occur with McA, but LMS vary considerably in their characteristics. Evaluating LMS characteristics may serve to identify the LMS associated with McA. The use of standard machine learning approaches seems appropriate for this particular task. This proof-of-concept pilot project aims to determine the feasibility of detecting McA from machine learning methods analyzing movement characteristics of the LMS. Methods: This study uses a small but diverse group of subjects to provide a large variety of LMS and McA adequate for supervised machine learning. LMS measurements were obtained from a new advanced technology in the RestEaZe™ leg band that integrates gyroscope, accelerometer, and capacitance measurements. Eleven RestEaZe™ LMS features were selected for logistic regression analyses. Results: With the optimum logit probability threshold selected, the system accurately detected 76% of the McA matching the accuracy of trained visual inter-scorer reliability (71–76%). The classifier provided a sensitivity of 76% and a specificity of 86% for the identification of the LMS with McA. The classifier identified regions in sleep with high versus low rates of LMS with McA, indicating possible areas of fragmented versus undisturbed restful sleep. Conclusion: These pilot data are encouraging as a preliminary proof-of-concept for using advanced machine learning analyses of LMS to identify sleep fragmented by McA. [ABSTRACT FROM AUTHOR]

Background: Periodic limb movement disorder (PLMD) and obstructive sleep apnea (OSA) are overlapping clinical syndromes with common risk factors. However, current literature has failed to establish a clear pathophysiological link between them. Thus, little is known about periodic limb movements (PLM) in otherwise healthy patients with suspected OSA., Methods: We performed a retrospective analysis of 112 patients (age: 44.5 ± 12.0 years, 14.3% female) with suspected OSA who underwent full night polysomnography for the first time. Patients with chronic diseases of any kind, recent infections, malignancies, or daily or regular use of any type of medication were excluded. Group comparisons were made based on the severity of OSA (using the apnea hypopnea index, AHI) or the periodic limb movement index (PLMI)., Results: Both, PLMI and the total number of periodic limb movements during sleep (PLMS), showed a significant increase in patients with severe OSA. In addition, AHI and apnea index (AI) were significantly higher in patients with PLMI >15/h, with a similar trend for hypopnea index (HI) ( p  < 0.001, p  < 0.001, and p  > 0.05, respectively). PLMI was significantly positive correlated with AHI, AI, and HI ( r  = 0.392, p  < 0.001; r  = 0.361, p  < 0.001; and r  = 0.212, p  < 0.05, respectively). Patients with PLMI >15/h were significantly older ( p  < 0.001). There was no significant association between body mass index (BMI) and PLMI >15/h., Conclusion: We found a significant association between the severity of OSA and PLM in our study population with suspected OSA but without other comorbidities. PLMI and PLMS were significantly increased in patients with severe OSA. Future prospective studies with larger collectives should verify the presented results and should include mechanistic aspects in their evaluation., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Seifen, Herrmann, Pordzik, Matthias and Gouveris.)

Background: Sleep disorders are highly prevalent in the general population and may be linked in a bidirectional fashion to stroke, which is one of the leading causes of morbidity and mortality. Aim: Four major scientific societies established a task force of experts in neurology, stroke, respiratory medicine, sleep medicine and methodology to critically evaluate the evidence regarding potential links and the impact of therapy. Materials and methods: Thirteen research questions were evaluated in a systematic literature search using a stepwise hierarchical approach: first, systematic reviews and meta‐analyses; second, primary studies post‐dating the systematic reviews/meta‐analyses. A total of 445 studies were evaluated and 88 were included. Statements were generated regarding current evidence and clinical practice. Results: Severe obstructive sleep apnoea (OSA) doubles the risk for incident stroke, especially in young to middle‐aged patients. Continuous positive airway pressure (CPAP) may reduce stroke risk, especially in treatment‐compliant patients. The prevalence of OSA is high in stroke patients and can be assessed by polygraphy. Severe OSA is a risk factor for recurrence of stroke and may be associated with stroke mortality, whilst CPAP may improve stroke outcome. It is not clear if insomnia increases stroke risk, whilst the pharmacotherapy of insomnia may increase it. Periodic limb movements in sleep (PLMS), but not restless limb syndrome (RLS), may be associated with an increased risk of stroke. Preliminary data suggest a high frequency of post‐stroke insomnia and RLS and their association with a less favourable stroke outcome, whilst treatment data are scarce. Discussion/Conclusion: Overall, the evidence base is best for OSA relationship with stroke and supports active diagnosis and therapy. Research gaps remain especially regarding insomnia and RLS/PLMS relationships with stroke. [ABSTRACT FROM AUTHOR]

Highlights • Periodic limb movement index (PLMI) ≥15 highly prevalent in adult population. • The likelihood of having a PLMI ≥15 increased significantly with age. • PLMI ≥15 not associated with sleepiness unless stratified by restless legs syndrome. Abstract Objectives Periodic limb movements in sleep (PLMS) are thought to be prevalent in elderly populations, but their impact on quality of life remains unclear. We examined the prevalence of PLMS, impact of age on prevalence, and association between PLMS and sleepiness. Methods We identified limb movements in 2335 Wisconsin Sleep Cohort polysomnograms collected over 12 years. Prevalence of periodic limb movement index (PLMI) ≥15 was calculated at baseline (n = 1084). McNemar's test assessed changes in prevalence over time. Association of sleepiness and PLMS evaluated using linear mixed modeling and generalized estimating equations. Models adjusted for confounders. Results Prevalence of PLMI ≥15 at baseline was 25.3%. Longitudinal prevalence increased significantly with age (p = 2.97 × 10−14). Sleepiness did not differ significantly between PLMI groups unless stratified by restless legs syndrome (RLS) symptoms. The RLS+/PLM+ group was sleepier than the RLS+/PLM− group. Multiple Sleep Latency Test trended towards increased alertness in the RLS−/PLM+ group compared to RLS−/PLM−. Conclusions A significant number of adults have PLMS and prevalence increased with age. No noteworthy association between PLMI category and sleepiness unless stratified by RLS symptoms. Significance Our results indicate that RLS and PLMS may have distinct clinical consequences and interactions that can help guide treatment approach. [ABSTRACT FROM AUTHOR]

Voice activity detection (VAD) identifies the presence/absence of human speech in a frame of a given speech signal. Presence/Absence of human speech can easily be identified in clean speech signal but its accuracy decreases with decreasing Signal-to-Noise ratio (SNR) value. Robust VAD helps to enhance the efficiency of speech signal based automated applications like speech enhancement, speaker identification, hearing aid devices etc. In this paper, a new feature of speech signal- “Peak of Log Magnitude Spectrum (PLMS)” is introduced and used for VAD. This newly defined feature PLMS along with three existing acoustic features(MFCC;RASTA-PLP and Formant Frequency) are used to train SVM classifier for VAD. Experimentally, it is found that coefficients of PLMS play most prominent role. Experimentally, it is also observed that the accuracy of the trained SVM classifier for VAD is the highest when compared with other state of the art methods (Sohn VAD and VAD G.729). [ABSTRACT FROM AUTHOR]

Objectives: Recent animal models of restless legs syndrome (RLS) suggest that brain iron deficiency is associated with a hypoadenosinergic state, with downregulation of adenosine A1 receptors (A1R) in the striatum and cortex. We hypothesized that an increase in extracellular adenosine induced by inhibitors of adenosine transporters, such as the non-selective ENT1/ENT2 inhibitor dipyridamole, would result in an improvement in RLS symptoms.Methods: In a prospective two-month open-label, non-placebo controlled clinical trial, 15 untreated idiopathic RLS patients began treatment with 100 mg dipyridamole (with uptitration to 400 mg if necessary). Multiple Suggested Immobilization Tests and polysomnography were performed at baseline and at eight weeks. Severity was assessed at four and eight weeks using the IRLS, and the CGI scales. The primary endpoint was therapeutic response (50% improvement in IRLS total score).Results: Thirteen patients completed the study. IRLS score improved from a mean (±S.D.) of 23.4 ± 4.6 at baseline to 10.7 ± 4.5 at eight weeks. Six out of 13 patients were full responders and four were partial responders. The mean (±S.D.) effective dose of dipyridamole at eight weeks was 281.8 ± 57.5 mg/day. Sleep variables also improved, and the mean (±S.D.) periodic leg movement index decreased from 26.7 ± 7.2 to 4.3 ± 1.9. Dipyridamole was generally well tolerated. Main side effects were abdominal cramps, diarrhea, dizziness, and flushing.Conclusions: These preliminary results suggest that dipyridamole has significant therapeutic effects on both sensory and motor symptoms, as well as sleep. In addition, it provides evidence that hypoadenosinergic mechanisms play a central role in RLS.Classification Of Evidence: The study provides class III evidence supporting the therapeutic effects of dipyridamole in RLS. [ABSTRACT FROM AUTHOR]

Objective: The aim of this study was to analyze statistically the number of single leg movements (LMs) forming bilateral LMs during sleep, along with their combined duration, to eventually provide evidence-based data for the adjustment of the current scoring rules defining bilateral LMs.Methods: Polysomnographic recordings of 111 untreated patients with RLS with a median age of 56.0 years, along with 42 normal controls with a mean age of 60.0 years, were included. In each recording, we identified all LMs that were considered as bilateral when two or more LMs were overlapping or the onset of the following movement was <0.5 second after the offset of the preceding LM. The remaining LMs were classified as monolateral. A series of parameters were computed for both bilateral and monolateral LMs.Results: The duration of monolateral LMs in RLS patients was significantly longer than that of normal controls. For bilateral LMs, the maximum number of single LMs forming a bilateral movement and the maximum duration were slightly higher in RLS patients; however, the distribution of the number of individual LMs forming a single bilateral LM was similar. Only 0.12% and 0.27% of bilateral LMs consisted of >4 individual movements, and only 0.16% and 1.90% of bilateral LMs were >15 seconds in RLS patients and healthy controls, respectively.Conclusion: Our results strongly suggest that bilateral LMs during sleep should be constituted by no more than four individual LMs and should have a maximum duration of 15 seconds. [ABSTRACT FROM AUTHOR]

Objective: Current standard guidelines for scoring periodic leg movements (PLM) define the start and end of a movement but fail to explicitly specify the movement morphology necessary to classify an EMG event as a PLM, rather than some other muscle event. This is currently left to the expert visual scorer to determine. This study aimed to define this morphology to provide a consistent standard for visual scoring and to improve automatic periodic leg movements in sleep scoring. Methods: A review of expert PLM scoring produced a hypothesized morphology criterion: a window of high EMG activity within the movement lasting at least 0.5 s. Two diverse expert visual scorers were independently presented with images of EMG tracings from candidate leg movements (CLM) that either passed or failed this requirement (aka 'full' or 'empty' movements, respectively), and indicated whether each should be scored as CLM. The 0.5-s window was compared with alternatives of 0.25 and 0.75 windows. Results: Expert scorers on average identified 94 % of 'full' movements as CLM in contrast to only 8.5 % of 'empty' movements. The proposed minimum window of 0.5 s also resulted in the highest agreement between visual scorers and between scorers and an automatic program. Conclusion: An added criterion requiring 0.5 s of high EMG activity within a valid CLM improves the accuracy of automatic scoring algorithms in relation to the gold standard of expert visual scorers. Our results suggest that this rule is an accurate representation of the morphology feature used by experts. This new rule has the potential to improve consistency and accuracy of visual and automatic scoring of PLM. [ABSTRACT FROM AUTHOR]

Objectives: This study aimed to examine relationship between periodic limb movements during sleep (PLMS) and incident atrial fibrillation/flutter (AF).Methods: Prospective multicenter cohort (n = 2273: adjudicated AF group; n = 843: self-reported AF group) of community-dwelling men without prevalent AF were followed for an average of 8.3 years (adjudicated) and 6.5 years (self-reported). PLMS index (PLMI, <5 (ref), ≥5 to <30, ≥30) and PLM arousal index (PLMAI, <1 (ref), ≥1 to <5, ≥5) were measured by polysomnography. Incident adjudicated and self-reported AF were analyzed by Cox proportional hazards and logistic regression, respectively, and adjusted for age, clinic, race, body mass index (BMI), alcohol use, cholesterol level, cardiac medications, pacemaker, apnea-hypopnea index, renal function, and cardiac risk. The interaction of age and PLMS was examined.Results: In this primarily Caucasian (89.8%) cohort of older men (mean age 76.1 ± 5.5 years) with BMI of 27.2 ± 3.7, there were 261 cases (11.5%) of adjudicated and 85 cases (10.1%) of self-reported incident AF. In the overall cohort, PLMI and PLMAI were not associated with adjudicated or self-reported AF. There was some evidence of an interaction of age and PLMI (p = 0.08, adjudicated AF) and PLMAI (p ≤ 0.06, both outcomes). Among men aged ≥76 years, the highest PLMI tertile was at increased risk of adjudicated AF (≥30 vs. <5; hazard ratio (HR) = 1.63, 1.01-2.63) and the middle PLMAI tertile predicted increased risk of both outcomes (1 to <5 vs. <1; adjudicated, HR = 1.65, 1.05-2.58; self-reported HR = 5.76, 1.76-18.84). No such associations were found in men aged <76 years.Conclusions: Although PLMS do not predict AF incidence in the overall cohort, the findings suggest PLMS increases incident AF risk in the older subgroup. [ABSTRACT FROM AUTHOR]

Objective: To evaluate an alternative index for periodic leg movements during sleep (PLMS) and wakefulness (PLMW) expected to be similar to the standard index when leg movement activity is genuinely periodic, but significantly lower when periodicity is low.Subjects and Methods: One-hundred-and-seven subjects with restless legs syndrome (RLS) were retrospectively identified and included (47 males, 60 females, mean age 56.9 years), along with 63 controls (33 males, 30 females, 42.2 years). Night-to-night variability was analysed in a subgroup of 17 subjects with RLS. PLMW were evaluated in a subgroup of 66 RLS subjects. Two 'alternative' PLMS/PLMW indices were calculated: one increased the lower limit of the inter-movement intervals from 5 to 10 s ('Alt1') and another additionally considered only series not interrupted by LMs with a short inter-movement interval ('Alt2').Results: Despite a high correlation between methods, only the Alt2 algorithm provided significantly different results, with PLMS/PLMW indices being consistently lower than those provided by the other two methods. The difference was more evident in the controls and during wakefulness, when periodicity was lower. The difference between the Alt2 and the standard PLMS index showed a significantly negative correlation with the Periodicity Index. Night-to-night variability was similar for all PLMS indices and significantly higher than the variability seen in the Periodicity Index.Conclusion: This methodological study introduces an alternative to the standard PLMS/PLMW indices, initiating the validation process for a new way of computing the PLMS/PLMW index, more adherent to the parameters that allows a reliable evaluation of their periodicity. [ABSTRACT FROM AUTHOR]

Levodopa and dopamine agonists have been the main treatment for restless legs syndrome during the past decades. Although their efficacy has been well documented over the short term, long-term dopaminergic treatment is often complicated by augmentation, loss of efficacy, and other side effects. Recent large randomized controlled trials provide new evidence for the efficacy of high-potency opioids and α2δ ligands, and several post hoc analyses, meta-analyses, algorithms, and guidelines have been published, often with a specific focus, for example, on augmentation, or on management of restless legs syndrome during pregnancy. Several new contributions to understanding the pathophysiology of restless legs syndrome have been published, but at this time, whether they will have an impact on treatment possibilities in the future cannot be estimated. [ABSTRACT FROM AUTHOR]

Periodic limb movements during sleep (PLMS) are associated with immediate increases in blood pressure. Both PLMS and hypertension have different distributions across racial/ethnic groups. We sought to determine whether PLMS is associated with hypertension among various racial/ethnic groups. A total of 1740 men and women underwent measurement of blood pressure and polysomnography with quantification of PLMS. Hypertension was defined as systolic blood pressure (SBP) ≥140, diastolic BP ≥90, or taking antihypertensive medication. For those taking antihypertensives, an estimated pretreatment SBP value was derived based on observed SBP and medication type/dose. Measures of PLMS, PLMS index, and PLMS arousal index were the main explanatory variables. Hypertension and SBP were modeled with logistic and multivariable regression adjusted for age, sex, body mass index, cardiovascular risk factors, lifestyle/habitual factors, apnea-hypopnea index, and race/ethnicity. In the overall cohort, prevalent hypertension was modestly associated with PLMS index (10 U; odds ratio, 1.05; 95% confidence interval, 1.00-1.10) and PLMS arousal index (1 U; 1.05; 1.01-1.09) after adjusting for confounders. Association in the overall cohort was influenced by large effect sizes in blacks, in whom the odds of prevalent hypertension increased by 21% (1%-45%) for 10 U PLMS index increase and 20% (2%-42%) for 1-U PLMS arousal index increase. In blacks, every 1-U PLMS arousal index increase was associated with SBP 1.01 mm Hg higher (1.01; 0.04-1.98). Associations between PLMS and blood pressure outcomes were also suggested among Chinese-Americans but not in whites or Hispanics. In a multiethnic cohort of community-dwelling men and women, prevalent hypertension and SBP are associated with PLMS frequency in blacks. [ABSTRACT FROM AUTHOR]

In recent years, a growing body of evidence suggests that periodic limb movements during sleep (PLMS) are associated with hypertension, cardiovascular and cerebrovascular risk. However, several non-mutually exclusive mechanisms may determine a higher cardiovascular risk in patients with PLMS and the link between the two remains controversial. Prospective data are scant and the temporal relationship between PLMS and acute vascular events is difficult to ascertain because although PLMS may lead to acute vascular events such as stroke, stroke may also give rise to PLMS. This article describes the clinical and polygraphic features of PLMS and examines the literature evidence linking PLMS with an increased risk for the development and progression of cardiovascular diseases, discussing the possible pathways of this association.

Objective: Sleep bruxism (SB) and periodic limb movements during sleep (PLMS) may have a common underlying neurophysiologic mechanism, especially in relation to the occurrence of sleep-related electroencephalographic (EEG) arousals. To test this hypothesis, three research questions were assessed. First, it was assessed whether PLMS events occur more frequently in SB patients than in individuals without SB. Second, the question was put forward whether the combined presence of SB and PLMS events is more common than that of isolated SB or PLMS events in a group of SB patients. Third, as to further unravel the possible role of EEG arousals in the underlying neurophysiologic mechanism of SB and PLMS, it was assessed in a group of SB patients whether combined SB/PLMS events with associated EEG arousals are more common than those without associated EEG arousals. Positive answers to these questions could suggest a common neurophysiological basis for both movement disorders. Materials and methods: Seventeen SB patients and 11 healthy controls were polysomnographically studied. SB, PLMS, and EEG arousals were scored. An association was noted when the occurrence was within a 3-s association zone. Results: The PLMS index was higher in SB patients than in healthy controls ( P < 0.001). Within the group of SB patients, the combined SB/PLMS index was higher than the isolated SB index ( P < 0.001) and the isolated PLMS index ( P = 0.018). Similarly, the combined SB/PLMS index with EEG arousal was higher than the combined SB/PLMS index without EEG arousal in SB patients ( P < 0.001). Conclusion: The results of this study indicate that SB, PLMS, and EEG arousals commonly concur during sleep in a time-linked manner. Clinical relevance: SB and PLMS probably have a common underlying neurophysiological mechanism. [ABSTRACT FROM AUTHOR]

Highlights: [•] Periodic limb movements during sleep (PLMS) are increased with sertraline treatment. [•] Only a few of PLMS are significant. [•] This effect of sertraline on PLMS might be dependent on dosage. [•] Our PLMS group had a higher arousal index than the non-PLMS group. [•] Clinicians should pay more attention to PLMS during selective serotonin reuptake inhibitor treatment. [ABSTRACT FROM AUTHOR]

Myoclonus and other jerky movements form a large heterogeneous group of disorders. Clinical neurophysiology studies can have an important contribution to support diagnosis but also to gain insight in the pathophysiology of different kind of jerks. This review focuses on myoclonus, tics, startle disorders, restless legs syndrome, and periodic leg movements during sleep. Myoclonus is defined as brief, shock-like movements, and subtypes can be classified based the anatomical origin. Both the clinical phenotype and the neurophysiological tests support this classification: cortical, cortical-subcortical, subcortical/non-segmental, segmental, peripheral, and functional jerks. The most important techniques used are polymyography and the combination of electromyography-electroencephalography focused on jerk-locked back-averaging, cortico-muscular coherence, and the Bereitschaftspotential. Clinically, the differential diagnosis of myoclonus includes tics, and this diagnosis is mainly based on the history with premonitory urges and the ability to suppress the tic. Electrophysiological tests are mainly applied in a research setting and include the Bereitschaftspotential, local field potentials, transcranial magnetic stimulation, and pre-pulse inhibition. Jerks due to a startling stimulus form the group of startle syndromes. This group includes disorders with an exaggerated startle reflex, such as hyperekplexia and stiff person syndrome, but also neuropsychiatric and stimulus-induced disorders. For these disorders polymyography combined with a startling stimulus can be useful to determine the pattern of muscle activation and thus the diagnosis. Assessment of symptoms in restless legs syndrome and periodic leg movements during sleep can be performed with different validated scoring criteria with the help of electromyography., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 International Federation of Clinical Neurophysiology. Published by Elsevier B.V.)

Abstract: Objective: To evaluate whether eliciting repetitive cortical and autonomic arousals during sleep is able to induce the occurrence of periodic leg movements during sleep (PLMS). Methods: Fifteen normal subjects underwent one night of uninterrupted and two sequential nights of experimental sleep fragmentation achieved by auditory and mechanical stimuli eliciting frequent EEG arousals. Sleep was polygraphically recorded and subsequently used to determine the frequency of arousals and occurrence of leg movement (LM) activity during the first (baseline) and the second fragmentation night. Also, heart rate variability parameters were obtained to assess the autonomic changes induced by the stimulation. Results: Sleep fragmentation was associated with an increase in the arousal index, percentage of sleep stage 1, and frequency of stage shifts. In addition, there was a decrease in sleep latency and in percentage of slow-wave sleep. Moreover, a significant increase in heart rate variability and especially of its sympathetic component, was also found. In contrast, parameters of the leg movement activity showed no significant change following experimental sleep fragmentation. The lack of an increase in leg movement activity was also observed in one subject who demonstrated PLMS at baseline. Conclusions: Experimental sleep fragmentation is not associated with an increase in PLMS in normal young adults. [Copyright &y& Elsevier]

Abstract: Objective: To describe the alterations in the macrostructure of sleep in a large cohort of sleep-disturbed patients with Parkinson’s disease (PD) and investigate influencing factors. Methods: A cohort of sleep-disturbed but otherwise unselected PD patients (n =351) was investigated with video-supported polysomnography. We analyzed the influence of age, disease duration, disease severity, and dopaminergic medication on subjective sleep perception, sleep efficiency, the amount of slow wave sleep, awakenings, periodic leg movements in sleep (PLMS), and REM sleep behavior disorder (RBD). Results: Sleep efficiency and slow wave sleep decreased with age (p =0.003 and p =0.041, respectively). The number of awakenings and the frequency of RBD increased with age (p =0.028 and p =0.006, respectively). Higher Hoehn & Yahr stages were associated with more PLMS (p =0.017). A higher daily dose of levodopa corresponded to more RBD (p <0.001). Neither disease duration nor levodopa dosage had any influence on sleep efficiency, slow wave sleep, awakenings, or PLMS. Dopamine agonists increased awakenings (p <0.001) and lowered PLMS (p <0.001). Subjective sleep perception was not influenced by any of the factors analyzed. The only path model that could be replicated identified disease severity and dopamine agonists as interdependent factors influencing awakenings and PLMS. Conclusion: Age leads to less sleep and a higher risk for RBD, and disease severity increases motor phenomena such as PLMS; dopamine agonists reduce PLMS but increase awakenings. No single factor analyzed influenced subjective sleep perception in this cohort of sleep disturbed PD patients. [Copyright &y& Elsevier]

Background: Sleep disturbances and excessive daytime somnolence are common and disabling features in adult-onset myotonic dystrophy type 1 (DM1). Methods: Our study used questionnaires, ambulatory polysomnography and the multiple sleep latency test to evaluate sleep-wake cycle and daytime sleepiness in unselected adult-onset DM1 patients. We recruited 18 patients affected by adult-onset DM1 and 18 matched controls. Results: Sleep efficiency was <90% in 16/18 patients, and it was significantly reduced when compared with controls. Reduced sleep efficiency was associated with abnormal respiratory events (5/18 patients) and/or periodic limb movements (11/18 patients). The Periodic Limb Movement Index was significantly increased in DM1 versus controls. A significantly lower mean MSLT sleep latency was detected in DM1 versus controls, but it did not reach pathological levels. Conclusions: Our controlled study demonstrated sleep alterations in unselected consecutive DM1 patients. Periodic limb movements in sleep are commonly associated with sleep disturbance in adult-onset DM1, and it may represent a marker of CNS neurodegenerative processes in DM1. [ABSTRACT FROM AUTHOR]

Abstract: Objective: Periodic limb movements in sleep (PLMS) show a time-of-night pattern, with most movements at the beginning of the night. Our study aimed to determine whether this pattern is due to an endogenous circadian rhythm, like that in the related movement disorder Restless Legs Syndrome (RLS). Methods: Four healthy older adults with a screening PLMI>20 were studied in an inpatient forced desynchrony protocol with an imposed sleep–wake cycle of 20h for 12 “nights,” allowing separation of circadian and sleep homeostatic influences on leg movements. We recorded sleep polysomnographically throughout each scheduled episode, including left and right anterior tibialis EMG. Results: PLMS in Stage 2 showed both a significant time-within-sleep pattern and a significant circadian rhythm. The circadian rhythm in PLMS peaked at the circadian phases when usual sleep onset occurs, preceding the evening rise in melatonin secretion. Conclusions: In our subjects, the circadian pattern of PLMs expression was very similar to that previously reported in patients with RLS. This evidence for a circadian rhythm in PLMS has implications for treatment and provides direction for future studies of the pathophysiology of this movement disorder. [Copyright &y& Elsevier]

The section presents abstracts on medical topics which include the societal burden of untreated sleep apnea, polysomnographic patterns related to the main risk factors of childhood obstructive sleep apnea syndrome, and the epidemic of obesity in children.

Abstract: Objective: To determine the distribution of age-at-onset in a large cohort of patients with restless legs syndrome (RLS) and to compare clinical and polysomnographic characteristics of patients with early and late age-at-onset of RLS. Methods: Two hundred and fifty patients with RLS were studied. Information on age-at-onset, etiology, familial history and symptoms severity of RLS was obtained. Age-at-onset density functions were determined from bootstrap methods and kernel density estimators. Results: Age-at-onset showed a significant bimodal distribution with a large peak occurring at 20years of age and a smaller peak in the mid-40s. Early- and late-onset RLS could be separated with a cut-off at 36years of age. Distributions of age-at-onset differed as a function of presence/absence of a familial history and etiology of RLS. Age-at-onset clearly differentiated patients with a primary RLS (early onset) from those with secondary RLS. Finally, early-onset RLS was associated with increased RLS severity with higher indices of periodic leg movements in sleep (PLMS) associated with microarousals and periodic leg movements during wakefulness (PLMW). Conclusions: Early- and late-onset RLS could be distinguished depending on familial history and etiology of RLS. Our data suggest that different pathological processes are involved in these two groups, the early-onset group being highly genetically determined. [Copyright &y& Elsevier]

Question of the study The term 'sleep attack' is used in patients with Parkinson's disease (PD) to designate a sudden onset of sleep (SOS) without any prior sleepiness. Clinically, sleep attacks can be distinguished from unintended sleep episodes, i.e. SOS with prior sleepiness. This study aimed to assess the prevalence of SOS and to identify its predictors in patients with sleep-disordered breathing (SDB). Patients and methods A total of 114 consecutive patients with the clinical suspicion of obstructive sleep apnoea syndrome (OSAS) and a respiratory disturbance index (RDI) of >5/h were examined using a questionnaire and polysomnography (PSG). Results Altogether 57.7% reported SOS, with 5.2% of the total population describing exclusively sleep attacks. SOS was observed in both patients with obstructive sleep apnoea-hypopnoea (OSAH) or increased upper airway resistance (UAR). Increased scores on the Epworth Sleepiness Scale (ESS) were the best predictor of SOS. Logistic regression showed a possible association of SOS with a low PLMS index, while no definite effect was observed for other PSG parameters. Conclusions Our data show that SDB is frequently associated with SOS. Future studies should address in more detail whether SDB patients with sleep attacks pose an increased accident risk. [ABSTRACT FROM AUTHOR]

Question of the study Besides their cognitive disorders, patients with dementia commonly present with a sleep-wake rhythm disturbance, sleep fragmentation, and sleep efficiency reduction. To date, very few studies have comparatively evaluated sleep disorders in different classes of dementia. The objective of our examination was to detect specific and unspecific polysomnographic changes in different dementia types. Patients and methods To date, 21 patients with a low to moderate grade of dementia have been examined. All patients received a thorough examination including routine neurologic and psychiatric examination, blood chemistry, CT/MRI, neuropsychologic evaluation, EEG and, when necessary, lumbar puncture and Doppler/duplex. According to international standard criteria, 14 patients were classified with Alzheimer's disease (DAT), five with frontotemporal dementia (FTD), and two with dementia with Lewy bodies (DLB). Patients with vascular dementia were not included in our analysis due the heterogeneity of this particular type of dementia. After an adaptation night, a polysomnography was performed on all patients, who were free of psychotropic medication before the beginning of therapy with antidementive medication. Results In all patients, a marked sleep continuity disruption was observed, which in most cases was not perceived by the patients. Moreover, in patients with DAT, a more accentuated REM sleep reduction in comparison to patients with FTD was found. In addition, we detected a high amount of sleep-related breathing disorders (SRBD) and periodic leg movements with arousals (PLMS), predominantly in patients with DAT. Conclusion The prevalence of sleep disorders in patients with primary dementias is clearly higher than previously suspected. Sleep disturbances due to alteration of sleep architecture and REM sleep and due to additional factors such as SRBD and PLMS may be related to the deterioration of cognitive performance and thus can exert an important role in the expression and course of dementia. [ABSTRACT FROM AUTHOR]