«Duermo todo el día para demostrarle al mundo que lucho por mis sueños» / “I sleep all day to show the world that I fight for my dreams”

Se presenta el caso clínico de un paciente que asocia tres trastornos de sueño diferentes: narcolepsia, apnea obstructiva del sueño (AOS) y trastorno de conducta del sueño REM. El objetivo es resaltar la importancia de la narcolepsia, una patología infradiagnosticada y que a veces puede quedar enmascarada por otros trastornos de sueño. En este caso, el paciente es diagnosticado inicialmente de AOS, pero dado que persiste con hipersomnolencia diurna debemos descartar otras causas. (AU)

We report a clinical case of a patient who presents three different sleep disorders, namely, narcolepsy, obstructive sleep apnea (OSA), and REM sleep behavior disorder. The objective of this study is to highlight the importance of narcolepsy, which is an underdiagnosed pathology that can sometimes be masked by other sleep disorders. In this case, the patient is initially diagnosed with OSA, but, due to the persistence of excessive daytime sleepiness, we have to rule out other causes. (AU)

A Holistic Strategy for Classification of Sleep Stages with EEG.

Manual sleep stage scoring is usually implemented with the help of sleep specialists by means of visual inspection of the neurophysiological signals of the patient. As it is a very hectic task to perform, automated sleep stage classification systems were developed in the past, and advancements are being made consistently by researchers. The various stages of sleep are identified by these automated sleep stage classification systems, and it is quite an important step to assist doctors for the diagnosis of sleep-related disorders. In this work, a holistic strategy named as clustering and dimensionality reduction with feature extraction cum selection for classification along with deep learning (CDFCD) is proposed for the classification of sleep stages with EEG signals. Though the methodology follows a similar structural flow as proposed in the past works, many advanced and novel techniques are proposed under each category in this work flow. Initially, clustering is applied with the help of hierarchical clustering, spectral clustering, and the proposed principal component analysis (PCA)-based subspace clustering. Then the dimensionality of it is reduced with the help of the proposed singular value decomposition (SVD)-based spectral algorithm and the standard variational Bayesian matrix factorization (VBMF) technique. Then the features are extracted and selected with the two novel proposed techniques, such as the sparse group lasso technique with dual-level implementation (SGL-DLI) and the ridge regression technique with limiting weight scheme (RR-LWS). Finally, the classification happens with the less explored multiclass Gaussian process classification (MGC), the proposed random arbitrary collective classification (RACC), and the deep learning technique using long short-term memory (LSTM) along with other conventional machine learning techniques. This methodology is validated on the sleep EDF database, and the results obtained with this methodology have surpassed the results of the previous studies in terms of the obtained classification accuracy reporting a high accuracy of 93.51% even for the six-classes classification problem.

Regulation of microRNA Expression in Sleep Disorders in Patients with Epilepsy.

The effects of epilepsy on sleep and the activating effects of sleep on seizures are well documented in the literature. To date, many sleep-related and awake-associated epilepsy syndromes have been described. The relationship between sleep and epilepsy has led to the recognition of polysomnographic testing as an important diagnostic tool in the diagnosis of epilepsy. The authors analyzed the available medical database in search of other markers that assess correlations between epilepsy and sleep. Studies pointing to microRNAs, whose abnormal expression may be common to epilepsy and sleep disorders, are promising. In recent years, the role of microRNAs in the pathogenesis of epilepsy and sleep disorders has been increasingly emphasized. MicroRNAs are a family of single-stranded, non-coding, endogenous regulatory molecules formed from double-stranded precursors. They are typically composed of 21-23 nucleotides, and their main role involves post-transcriptional downregulation of expression of numerous genes. Learning more about the role of microRNAs in the pathogenesis of sleep disorder epilepsy may result in its use as a biomarker in these disorders and application in therapy.

Epilepsy and lockdown: A survey of patients normally attending a Spanish centre.

BACKGROUND: Lockdown due to the SARS-CoV-2 pandemic became a challenge to maintain care for patients with epilepsy; we aimed to find out how the pandemic affected them. METHODS: We sent an online 22-item questionnaire to patients from our outpatient clinic, a reference centre in Spain for drug-resistant epilepsy, inquiring about the effects of lockdown, from March to May 2020. RESULTS: We sent the survey to 627 patients; 312 (58% women) sent a complete response and were included. Of all respondents, 57% took >2 antiseizure medications. One-third of respondents (29%) declared an associated cognitive or motor disability. A minority had confirmed infection with SARS-CoV-2 (1.92%). Seizure frequency remained like usual in 56% of patients, while 31.2% reported an increase. Less than 10% needed emergent assistance. Almost half reported anxiety or depression, and 25% increased behavioural disorders. Mood (F: 5.40; p: 0.002) and sleep disorders (F = 2.67; p: 0.05) were associated with increase in seizure frequency. Patients were able to contact their physicians when needed and were open to a future telematic approach to follow-up visits. CONCLUSIONS: Seizure frequency and severity remained unchanged in most patients during the lockdown. Mood and sleep disorders were common and associated with seizure worsening. Patients were open to telematic care in the future.

Diagnóstico diferencial del insomnio con otros trastornos primarios del sueño comórbidos / Differential diagnosis of insomnia from other comorbid primary sleep disorders

Actualmente la «queja de insomnio» tiene una prevalencia muy elevada en la consulta médica, lo que obliga que cualquier facultativo deba tener unas nociones básicas de cómo enfocarla. Pero actualmente existen ciertos aspectos que dificultan este enfoque, sobre todo cuando queremos descartar el «trastorno de insomnio» del resto de los trastornos primarios del sueño. Con este artículo se pretende romper con la confusión terminológica que abarca el concepto de «insomnio», aportando las definiciones de los diferentes términos más correctos a utilizar. Esto nos proporcionará unas buenas herramientas para la correcta recopilación de la información, que nos serán de gran ayuda para la aplicación de un algoritmo diagnóstico clínico del «trastorno de insomnio crónico del adulto», para diferenciarlo principalmente del resto de trastornos primarios del sueño, y así, aplicar un tratamiento específico, que resolverá desde la base la «queja de insomnio» del paciente

The "insomnia complaint" currently has a very high prevalence in the medical consulting room. This assumes that any doctor must have some basic notions of how to approach it. But currently, there are certain aspects that hinder this approach, especially when the "insomnia disorder" needs to be ruled out from the rest of the primary sleep disorders. This article aims to break with the terminological confusion that encompasses the concept of "insomnia", providing the definitions of the most correct terms to use. This will provide us with good tools for the correct collection of information, which will be of great help for the application of a clinical diagnostic algorithm of "Chronic adult Insomnia disorder". This will be able to differentiate it mainly from the rest of primary sleep disorders, and thus, apply a specific treatment, which will resolved on the basis of the "insomnia complaint" by the patient

Diagnosis of central disorders of hypersomnolence: A reappraisal by European experts.

The aim of this European initiative is to facilitate a structured discussion to improve the next edition of the International Classification of Sleep Disorders (ICSD), particularly the chapter on central disorders of hypersomnolence. The ultimate goal for a sleep disorders classification is to be based on the underlying neurobiological causes of the disorders with clear implication for treatment or, ideally, prevention and or healing. The current ICSD classification, published in 2014, inevitably has important shortcomings, largely reflecting the lack of knowledge about the precise neurobiological mechanisms underlying the majority of sleep disorders we currently delineate. Despite a clear rationale for the present structure, there remain important limitations that make it difficult to apply in routine clinical practice. Moreover, there are indications that the current structure may even prevent us from gaining relevant new knowledge to better understand certain sleep disorders and their neurobiological causes. We suggest the creation of a new consistent, complaint driven, hierarchical classification for central disorders of hypersomnolence; containing levels of certainty, and giving diagnostic tests, particularly the MSLT, a weighting based on its specificity and sensitivity in the diagnostic context. We propose and define three diagnostic categories (with levels of certainty): 1/"Narcolepsy" 2/"Idiopathic hypersomnia", 3/"Idiopathic excessive sleepiness" (with subtypes).

Subacute sleep disturbance in moderate to severe traumatic brain injury: a systematic review.

Objective: This systematic review evaluated subacute sleep disturbance following moderate to severe traumatic brain injury (TBI) and the impact of secondary factors such as mood or pain.Methods: A comprehensive search strategy was applied to nine databases. Inclusion criteria included: adults ≥18 years, moderate and severe TBI and within 3 months of injury. Eligible studies were critically appraised using the McMaster Quantitative Critical Review Form. Study characteristics, outcomes, and methodological quality were synthesized. This systematic review was registered with PROSPERO (Registration number: CRD42018087799).Results: Ten studies were included. Research identified early-onset sleep disturbances; characterized as fragmented sleep periods and difficulty initiating sleep. Alterations to sleep architecture (e.g. rapid eye movement sleep) were reported. Sleep disturbance appears to associate with alterations of consciousness. Sleep disturbance tended to be particularly increased during the phase of post-traumatic amnesia (PTA) (78.7%).Conclusions: There is a limited amount of research available, which has inherent measurement and sample size limitations. The gold standard for measuring sleep (polysomnography) was rarely utilized, which may affect the detection of sleep disturbance and sleep architecture. Secondary factors potentially influencing sleep were generally not reported. Further evaluation on associations between sleep and PTA is needed.

Overview of Sleep and Circadian Rhythm Disorders in Parkinson Disease.

Sleep disorders are common among PD patients and affect quality of life. They are often under-recognized and under-treated. Mechanisms of sleep disorders in PD remain relatively poorly understood. Improved awareness of common sleep problems in PD. Tailored treatment and evidence for efficacy are lacking. The purpose of this review is to provide an overview and update on the most common sleep disorders in PD. We review specific features of the most common sleep disorders in PD, including insomnia, excessive daytime sleepiness, sleep-disordered breathing, restless legs syndrome, circadian rhythm disorders and REM sleep behavior disorders.

Using Lempel-Ziv complexity as effective classification tool of the sleep-related breathing disorders.

BACKGROUND AND OBJECTIVE: People suffer from sleep disorders caused by work-related stress, irregular lifestyle or mental health problems. Therefore, development of effective tools to diagnose sleep disorders is important. Recently, to analyze biomedical signals Information Theory is exploited. We propose efficient classification method of sleep anomalies by applying entropy estimating algorithms to encoded ECGs signals coming from patients suffering from Sleep-Related Breathing Disorders (SRBD). METHODS: First, ECGs were discretized using the encoding method which captures the biosignals variability. It takes into account oscillations of ECG measurements around signals averages. Next, to estimate entropy of encoded signals Lempel-Ziv complexity algorithm (LZ) which measures patterns generation rate was applied. Then, optimal encoding parameters, which allow distinguishing normal versus abnormal events during sleep with high sensitivity and specificity were determined numerically. Simultaneously, subjects' states were identified using acoustic signal of breathing recorded in the same period during sleep. RESULTS: Random sequences show normalized LZ close to 1 while for more regular sequences it is closer to 0. Our calculations show that SRBDs have normalized LZ around 0.32 (on average), while control group has complexity around 0.85. The results obtained to public database are similar, i.e. LZ for SRBDs around 0.48 and for control group 0.7. These show that signals within the control group are more random whereas for the SRBD group ECGs are more deterministic. This finding remained valid for both signals acquired during the whole duration of experiment, and when shorter time intervals were considered. Proposed classifier provided sleep disorders diagnostics with a sensitivity of 93.75 and specificity of 73.00%. To validate our method we have considered also different variants as a training and as testing sets. In all cases, the optimal encoding parameter, sensitivity and specificity values were similar to our results above. CONCLUSIONS: Our pilot study suggests that LZ based algorithm could be used as a clinical tool to classify sleep disorders since the LZ complexities for SRBD positives versus healthy individuals show a significant difference. Moreover, normalized LZ complexity changes are related to the snoring level. This study also indicates that LZ technique is able to detect sleep abnormalities in early disorders stage.

Gender differences in latent classes of sleep quality in community-dwelling adults based on the Pittsburgh sleep quality index.

This study investigated the latent classes of sleep quality based on the Pittsburgh Sleep Quality index (PSQI) in Chinese adults, and analyzed whether these latent classes differed between men and women. A total of 982 adults (age: 18-59 years) participated in the questionnaire survey, and the PSQI was used to assess sleep quality. Two latent classes of sleep quality were found in men, and the cut-off value of PSQI for predicting the poor sleep quality group was 6. Four latent classes were found in women, which were named 'good sleep', 'daytime dysfunction', 'inadequate sleep', and 'poor sleep', respectively. In women, the optimal cut-off values of the PSQI for predicting the poor and good sleep quality groups were 9 and 6, respectively. Women who were not included in the 'poor sleep' group but had a score not less than 2 in the 'sleep duration' or 'daytime dysfunction' dimensions of PSQI were classified into the 'inadequate sleep' group or the 'daytime dysfunction' group. The findings may benefit the identification of sleep problems and assist in more effective alleviation of these issues.

The Epworth Sleepiness Scale in Service Members with Sleep Disorders.

INTRODUCTION: Excessive daytime sleepiness affects an estimated 20% of the general population. While the prevalence of sleepiness in the military is largely unknown, it is well established that short sleep duration is endemic. The reasons for this include: the demanding nature of their duties, shift work and 24-hour duty periods, deployments and exigencies of military service as well as sleep disorders. The Epworth Sleepiness Scale (ESS) is the most widely used sleep questionnaire and provides a self-assessment of daytime sleepiness. To date the clinical utility of this questionnaire in differentiating sleep disorders in military patients with sleep disorders has never been evaluated. MATERIALS AND METHODS: The primary aim of this manuscript was to assess if Epworth Sleepiness Scale (ESS) scores differed between military personnel with insomnia, obstructive sleep apnea (OSA), comorbid insomnia/obstructive sleep apnea (COMISA), and a group with neither insomnia nor obstructive sleep apnea (NISA). This study assessed the clinical utility of the ESS in differentiating sleep disorders amongst a sample (N = 488) of U.S. military personnel with insomnia (n = 92), OSA (n = 142), COMISA (n = 221), and a NISA group (n = 33) which served as the control population. RESULTS: In the present sample, 68.4% of service members reported excessive daytime sleepiness (EDS) with an ESS > 10. ESS scores differed between military personnel with COMISA (13.5 ± 4.83) and those with OSA only (11.5 ± 4.08; p < 0.001) and the NISA group (9.46 ± 4.84; p < 0.001). Also, ESS scores differed between patients with insomnia only (13.0 ± 4.84) and the NISA group (p < 0.01). CONCLUSIONS: Overall, the ESS had poor ability to differentiate sleep disorders. In military personnel, the ESS appears elevated in the most common sleep disorders, likely due to their insufficient sleep, and does not help to differentiate OSA from insomnia. Further studies are required to validate this questionnaire and determine an appropriate threshold value for abnormal sleepiness in the military population.

Value of combined video EEG and polysomnography in clinical management of children with epilepsy and daytime or nocturnal spells.

PURPOSE: Sleep disorders are common in epilepsy. Additionally, events of staring, jerking, or nocturnal behaviors are common presentations in neurology or sleep practice. Moreover, sleepiness and nocturnal awakenings are common symptoms in children with epilepsy and differentiation form ongoing seizures and sleep disorders is needed. However, limited data exist for the best evaluation methods. This study evaluated the usefulness of combined video electroencephalography (EEG) and polysomnography (PSG) studies (vEEG/PSG). METHODS: Polysomnography custom database was searched for combined vEEG/PSG studies, performed from July 2010 to April 2014, which identified 240 studies. From chart review, data were collected for presenting symptoms, sleep disorder and epilepsy/neurology diagnoses, and EEG and PSG results. RESULTS: Most common indications for performing combined vEEG/PSG were correlating sleep events with seizure occurrence, evaluating sleepiness, nocturnal awakenings and nocturnal events. Sleep physician evaluation and/or PSG were abnormal in 94% of the studies. The EEG was abnormal in 53% and events or seizures were recorded in 40% of the studies. Hence, vEEG/PSG addressed the diagnostic questions. Additionally, as compared to children with epilepsy, a significantly larger number of children with spells/parasomnia had a normal sleep evaluation including a normal PSG (9 Vs 37%, p = 0.00003). CONCLUSIONS: This study demonstrates that combined video EEG and polysomnography is useful in addressing the common management questions in children with epilepsy and suspicious nocturnal events. Additionally, sleep disorders are more common in children with epilepsy than parasomnia. Hence sleep evaluation is important in children with epilepsy. Further prospective studies are needed.

Electrodermal activity patterns in sleep stages and their utility for sleep versus wake classification.

As the prevalence of sleep disorders is increasing, new methods for ambulatory sleep measurement are required. This paper presents electrodermal activity in different sleep stages and a sleep detection algorithm based on electrodermal activity. We analysed electrodermal activity and polysomnographic data of 43 healthy subjects and 48 patients with sleep disorders. Electrodermal activity was measured using an ambulatory device worn at the wrist. Two parameters to describe electrodermal activity were defined based on previous literature: EDASEF (electrodermal activity-smoothed feature) as parameter for skin conductance level; and EDAcounts (number of electrodermal activity-peaks) as skin conductance responses. Analysis of variance indicated significant EDASEF differences between the sleep stages wake versus N1, wake versus N2, wake versus slow-wave sleep, and wake versus rapid eye movement. The analysis of EDAcounts also showed significant differences, especially in the stages slow-wave sleep versus rapid eye movement. Between healthy subjects and patients, a significant disparity of EDAcounts was revealed in stage N1. Furthermore, the variances of EDASEF and EDAcounts in N1, N2 slow-wave sleep and rapid eye movement were higher in the patient group (p [F test] < .05). Next, an electrodermal activity-based sleep/wake discriminating algorithm was constructed. The optimized algorithm achieved an average sensitivity and specificity for sleep detection of 97% and 75%. The epoch agreement rate (average accuracy) was 86%. These outcomes are comparative to sleep detection algorithms based on actigraphy or heart rate variability. The results of this study indicate that electrodermal activity is not only a robust parameter for describing sleep, but also a potential suitable method for ambulatory sleep monitoring.

Co-morbid sleep disorders and epilepsy: A narrative review and case examples.

Co-morbid sleep disorders, including sleep apnea, insomnia, restless legs syndrome, and the parasomnias, occur frequently in people with epilepsy. This article reviews the cardinal presenting symptoms and diagnostic features of each of these disorders to enable epileptologists to readily screen and identify sleep co-morbidities in their patients. It summarizes current evidence concerning the reciprocal relationship between sleep disturbances and epilepsy and available treatment options for common sleep disorders in people with epilepsy. Several illustrative cases demonstrate the practical consequences of co-morbid sleep disorders in epilepsy patients and suggest diagnostic and treatment approaches that may improve daytime functioning, alertness, quality of life, and seizure burden.

Sleep disorders in adolescents and young adults: Insights into types, relationship to obesity and high altitude and possible lines of management.

A sleep disorder is a medical disorder of the sleep pattern of a person that may be serious enough to interfere with normal physical, mental and emotional functioning. Disruptions in sleep can be caused by a variety of causes, from teeth grinding to night terrors. Sleep disorders are usually prevalent among adolescents and young adults, possibly due to factors related to life style, dietary habits, hormonal and emotional disturbances. Other factors that may precipitate sleep disorders include environmental, psychological and genetic factors. Sleep disorders may lead to serious psychological and mood disorders and may even affect the immune system. Management of sleep disorders depends on amelioration of the precipitating factors and the use of certain drugs that may help to restore the normal sleep-wake cycle. This review sheds light on sleep disorders in adolescents and young adults regarding their types, etiology, dangers and possible lines of management.

Sleep Disorders in the Elderly.

Although some physiologic changes in sleep are a normal part of the aging process, other sleep complaints made by elderly patients can indicate a primary or secondary sleep disorder. It is important to recognize the difference between normal age-related changes and what may require further testing to make an accurate diagnosis. Proper diagnosis and treatment of sleep disorders can improve the quality of life and safety for the elderly and their families.

The impact of sleep disorders on driving safety-findings from the Second Strategic Highway Research Program naturalistic driving study.

Study Objectives: This study investigated the association between driving safety and seven sleep disorders amongst 3541 participants of the Second Strategic Highway Research Program (SHRP 2) naturalistic driving study. Methods: SHRP 2 collected naturalistic driving data from participants between 16 and 98 years old by instrumenting participants' vehicles. The analyses used logistic regression to determine the likelihood of crash or near-crash involvement, Poisson log-linear regression to assess crash or near-crash rate, and ordinal logistic regression to assess driver maneuver appropriateness and crash or near-crash severity. These analyses did not account for any medical treatments for the sleep disorders. Results: Females with restless legs syndrome/Willis-Ekbom disease (RLS/WED), drivers with insomnia or narcolepsy, are associated with significantly higher risk of crash or near-crash. Drivers with shift work sleep disorder (SWSD) are associated with significantly increased crash or near-crash rate. Females with RLS/WED or sleep apnea and drivers with SWSD are associated with less safe driver maneuver and drivers with periodic limb movement disorder are associated with more severe events. The four analyses provide no evidence of safety decrements associated with migraine. Conclusions: This study is the first examination on the association between seven sleep disorders and different measures of driving risk using large-scale naturalistic driving study data. The results corroborate much of the existing simulator and epidemiological research related to sleep-disorder patients and their driving safety, but add ecological validity to those findings. These results contribute to the empirical basis for medical professionals, policy makers, and employers in making decisions to aid individuals with sleep disorders in balancing safety and personal mobility.

[Guidelines in Practice: The New S3 Guideline "Sleeping Disorders - Sleep-Related Abnormal Breathing"]. / Leitlinien in der Praxis: Die neue S3-Leitlinie "Nicht erholsamer Schlaf/Schlafstörungen" ­ Kapitel "Schlafbezogene Atmungsstörungen".

Sleep related breathing disorders include central sleep apnea (CSA), obstructive sleep apnea (OSA), sleep-related hypoventilation, and sleep-related hypoxia. These disorders are frequent and growing in clinical relevance. The related chapter of the S3 guideline "Non-restorative sleep/Sleep disorders", published by the German Sleep Society (DGSM), has recently been updated in November 2016. Epidemiology, diagnostics, therapeutic procedures, and classification of sleep related disorders have been revised. Concerning epidemiology, a considerably higher mortality rate among pregnant women with OSA has been emphasized. With regards to diagnostics, the authors point out that respiratory polygraphy may be sufficient in diagnosing OSA, if a typical clinical condition is given. For CSA, recommendations were changed to diagnose CSA with low apnea rates present. Significant changes for treating CSA in patients with left ventricular dysfunction have been introduced. In addition, there is now to be differentiated between sleep-related hypoventilation and sleep-related hypoxaemia. Obesity hypoventilation syndrome is discussed in more detail. This article sums up and comments on the published changes.