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Sleep is an integral and vital component of human life, contributing significantly to overall health and well-being, but a considerable number of people worldwide experience sleep disorders. Sleep disorder diagnosis heavily depends on accurately classifying sleep stages. Traditionally, this classification has been performed manually by trained sleep technologists that visually inspect polysomnography records. However, in order to mitigate the labor-intensive nature of this process, automated approaches have been developed. These automated methods aim to streamline and facilitate sleep stage classification. This study aims to classify sleep stages in a dataset comprising subjects with insomnia, PLM, and sleep apnea. The dataset consists of PSG recordings from the multi-ethnic study of atherosclerosis (MESA) cohort of the national sleep research resource (NSRR), including 2056 subjects. Among these subjects, 130 have insomnia, 39 suffer from PLM, 156 have sleep apnea, and the remaining 1731 are classified as good sleepers. This study proposes an automated computerized technique to classify sleep stages, developing a machine-learning model with explainable artificial intelligence (XAI) capabilities using wavelet-based Hjorth parameters. An optimal biorthogonal wavelet filter bank (BOWFB) has been employed to extract subbands (SBs) from 30 seconds of electroencephalogram (EEG) epochs. Three EEG channels, namely: Fz_Cz, Cz_Oz, and C4_M1, are employed to yield an optimum outcome. The Hjorth parameters extracted from SBs were then fed to different machine learning algorithms. To gain an understanding of the model, in this study, we used SHAP (Shapley Additive explanations) method. For subjects suffering from the aforementioned diseases, the model utilized features derived from all channels and employed an ensembled bagged trees (EnBT) classifier. The highest accuracy of 86.8%, 87.3%, 85.0%, 84.5%, and 83.8% is obtained for the insomniac, PLM, apniac, good sleepers and complete datasets, respectively. Using these techniques and datasets, the study aims to enhance sleep stage classification accuracy and improve understanding of sleep disorders such as insomnia, PLM, and sleep apnea.

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OBJECTIVE: Poor sleep is frequently reported in children with neuromuscular diseases (NMD) and cerebral palsy (CP) however breathing disorders during sleep are often the clinical focus. Periodic limb movements (PLMs) have an increased prevalence in adults with NMD and may contribute to sleep disturbance in this population. We assessed the prevalence of PLMs in children with NMD or CP.
METHODS: Retrospective review of polysomnography (PSG) with leg electromyography in children age 1-18 years with NMD (including Duchenne muscular dystrophy, myotonic dystrophy, spinal muscular atrophy) or CP performed at a paediatric sleep centre 2004-2022.
RESULTS: Leg electromyography was available in at least 1 PSG in 239 children (125 NMD, 114 CP), and in 2 PSGs in 105 children (73 NMD, 32 CP). At initial PSG, 72 (30 %) were female with a median age 9y and respiratory disturbance index 3.5/h (interquartile range 1.3-9.9/h). Elevated PLM index (PLMI; >5/h) occurred in 9.6 % of each of the CP and NMD groups, quantified by initial PSG. Overall, PLMI increased from baseline (median 0, maximum 33/h) to follow-up (median 0, maximum 55.8/h; p < 0.05). In those with an elevated PLMI, arousal percentage attributable to PLMs was up to 25 % (median 7.5 %).
CONCLUSIONS: Elevated PLMI occurred at a higher prevalence in children with NMD and CP than reported in other clinic-referred paediatric populations. It is important that PLMs are not overlooked as identification and treatment may help improve sleep outcomes. Further research is required to understand the pathophysiology and consequences of PLMs specifically in this population.

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OBJECTIVE: Excessive 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.
METHODS: One 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.
RESULTS: Under 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.
CONCLUSIONS: The 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.

In a recent survey of 16,694 people receiving treatment for Restless Legs Syndrome (RLS), approximately 25% were treated with benzodiazepines either singly or in combination with other RLS treatments. Because of the large number of people receiving benzodiazepines for treatment of RLS, we conducted a historical overview of the therapeutic role of benzodiazepines in RLS and its associated condition Periodic Limb Movements in Sleep (PLMS). We found 17 articles on the use of clonazepam in RLS, PLMS, or both, 3 on triazolam and PLMS, 1 on alprazolam and RLS, 1 on temazepam and PLMS, and 1 on nitrazepam and PLMS. The order of benefit of benzodiazepines from the summarized literature is Sleep>RLS>PLMS and arousals > PLMS. Most of the studies on clonazepam employed dosages of 0.5-2.0 mg. Dosages of 3 or 4 mg caused lethargy, somnolence and confusion. An epidemiological study on the therapy of RLS suggests that treatment of RLS with most types of RLS medications including benzodiazepines in combination with other RLS therapies lowers the future cardiovascular risk associated with RLS. The major effect of benzodiazepines is through potentiation of the effect of GABA on the GABA A receptor. Neuroimaging studies suggest that GABA is altered either positively or negatively in various brain regions in RLS and genetic studies suggest that there are alterations in the GABA receptor in RLS. These results suggest that medications with different GABAergic mechanisms such as tiagabine (Gabitril) or others should be investigated in RLS for their possible therapeutic benefit.
UNASSIGNED: Benzodiazepines are frequently used as therapy in Restless Legs Syndrome (RLS) and Periodic Limb Movements in Sleep. The order of benefit is Sleep>RLS>PLMS and arousals > PLMS. For clonazepam dosages of 0.5 mg-2.0 mg/day are most frequently employed. Benzodiazepines exert their therapeutic effect through GABA-ergic mechanisms.

OBJECTIVE: Although manual scoring has been classically considered the gold standard to identify periodic leg movements (PLM), it is a very time consuming and expensive process, also subject to variability in interpretation. In the last decades, different authors have observed reasonably good agreement between automated PSG scoring algorithms and manual scoring in adults, according to established criteria. We aim to compare the automatic software analysis of our polysomnogram with the manual staging in children with sleep-disordered breathing.
METHODS: We performed a semiautomatic method, in which an experienced technician watched the video recording and removed from the automatic analysis those movements that did not correspond to true candidate leg movement (LM).
RESULTS: A total of 131 PSGs were studied; applying the established criteria, 65 children were diagnosed of obstructive sleep apnea, and 66 presented snoring but with no sleep apnea. The mean age was 6.7 years (±1.7) and twenty-five children (19.08 %) had a PLMI >5/h. Statistical differences were found not only for PLMI (manual: 2.20 (0.7, 4.1) vs automatic (6.4 (3.85,9.5); p < 0.001), but for almost of all indexes assessed between the automatic and the manual scoring analysis. The level of concordance was only moderate for PLM index (0.63 [0.51-0.72]); showing that, unlike the articles published in the adult population, automatic analysis is not accurate in children and, manually or semi-automatically analysis as ours need to be done.
CONCLUSIONS: It seems that PLM detection algorithm might work accurately but, the real need would be a true LM detection algorithm.

This systematic review evaluates the scientific literature on pediatric periodic limb movement disorder (PLMD), adhering to PRISMA guidelines and utilizing PICOS criteria. The search across PubMed, EMBASE, and Scopus yielded 331 articles, with 17 meeting inclusion criteria. Diagnostic criteria evolved, with polysomnography and PLMS index ≥5 required since 2003. Also, PLMD diagnosis mandates clinical consequences like insomnia, hypersomnia, and fatigue, excluding comorbidities causing sleep disruption. Prevalence in children is low (0.3%), emphasizing the need for meticulous investigation. Comorbidities, particularly the bidirectional relationship with ADHD, were explored. Challenges in diagnosis and understanding arise from overlapping conditions such as sleep disordered breathing, psychotropic medication, and criteria non-adherence. Despite generally good study quality, weaknesses include sample size justification and biases. The periodic leg movement index shows high sensitivity but low specificity, underscoring strict diagnostic criteria adherence. Diverse metrics for symptoms necessitate standardized approaches. Family history of RLS in children with PLMD suggests unexplored aspects. Treatment, mainly iron supplementation, lacks standardized assessment metrics. The review emphasizes diagnostic and treatment challenges, recommending unbiased studies with precise techniques. Comprehensive research, quantifying PLMS and objectively assessing sleep parameters, is crucial for advancing understanding in pediatric PLMD. PROSPERO REGISTRATION NUMBER: CRD42021251406.