Degree Of Sleep Fragmentation Research Articles

Circuit mechanism underlying fragmented sleep and memory deficits in 16p11.2 deletion mouse model of autism.

Sleep disturbances are prevalent in children with autism spectrum disorder (ASD) and have a major impact on the quality of life. Strikingly, sleep problems are positively correlated with the severity of ASD symptoms, such as memory impairment. However, the neural mechanisms underlying sleep disturbances and cognitive deficits in ASD are largely unexplored. Here, we show that non-rapid eye movement sleep (NREMs) is highly fragmented in the 16p11.2 deletion mouse model of ASD. The degree of sleep fragmentation is reflected in an increased number of calcium transients in the activity of locus coeruleus noradrenergic (LC-NE) neurons during NREMs. Exposure to a novel environment further exacerbates sleep disturbances in 16p11.2 deletion mice by fragmenting NREMs and decreasing rapid eye movement sleep (REMs). In contrast, optogenetic inhibition of LC-NE neurons and pharmacological blockade of noradrenergic transmission using clonidine reverse sleep fragmentation. Furthermore, inhibiting LC-NE neurons restores memory. Rabies-mediated unbiased screening of presynaptic neurons reveals altered connectivity of LC-NE neurons with sleep- and memory regulatory brain regions in 16p11.2 deletion mice. Our findings demonstrate that heightened activity of LC-NE neurons and altered brain-wide connectivity underlies sleep fragmentation in 16p11.2 deletion mice and identify a crucial role of the LC-NE system in regulating sleep stability and memory in ASD.

Functional connectivity in default mode network correlates with severity of hypoxemia in obstructive sleep apnea.

IntroductionObstructive sleep apnea (OSA)‐associated hypoxemia, sleep fragmentation, and cerebral vascular dysfunction are implicated in cognitive dysfunction. Functional connectivity within default mode network (DMN) is a possible mechanism underlying the cognitive impairment. The aim of this study was to investigate the impact of hypoxemia and sleep fragmentation on functional connectivity and on cognitive performance in patients with OSA.MethodsTwenty‐eight patients with OSA were included (mean age = 58.0 ± 8.5 years). We correlated the functional connectivity in DMN with cognitive performances and further analyzed the relationship of functional connectivity in DMN with hypoxemia severity, as revealed by apnea‐hypopnea index (AHI), oxygen desaturation index (ODI), and nadir SaO2 (%), and with degree of sleep fragmentation, as shown by sleep efficiency and wake after sleep onset.ResultsFunctional connectivity in DMN was associated with AHI, ODI, and nadir SaO2 (%) (p < .05) and was not associated with sleep fragmentation measures (p > .05). Functional connectivity that was associated with AHI, ODI, and nadir SaO2 (%) was in the areas of bilateral middle temporal gyri, bilateral frontal pole, and bilateral hippocampus and was positively correlated with Cognitive Abilities Screening Instrument (CASI) total score (ρ = 0.484; p = .012), CASI‐List‐generating, CASI‐Attention, and composite score of CASI‐List‐generating plus CASI‐Attention (p < .05).ConclusionFunctional connectivity in DMN is implicated in impairment of global cognitive function and of attention in OSA patients. The functional connectivity in the DMN is associated with hypoxemia rather than with sleep fragmentation.

Application of machine learning to predict obstructive sleep apnea syndrome severity

Obstructive sleep apnea syndrome has become an important public health concern. Polysomnography is traditionally considered an established and effective diagnostic tool providing information on the severity of obstructive sleep apnea syndrome and the degree of sleep fragmentation. However, the numerous steps in the polysomnography test to diagnose obstructive sleep apnea syndrome are costly and time consuming. This study aimed to test the efficacy and clinical applicability of different machine learning methods based on demographic information and questionnaire data to predict obstructive sleep apnea syndrome severity. We collected data about demographic characteristics, spirometry values, gas exchange (PaO2, PaCO2) and symptoms (Epworth Sleepiness Scale, snoring, etc.) of 313 patients with previous diagnosis of obstructive sleep apnea syndrome. After principal component analysis, we selected 19 variables which were used for further preprocessing and to eventually train seven types of classification models and five types of regression models to evaluate the prediction ability of obstructive sleep apnea syndrome severity, represented either by class or by apnea-hypopnea index. All models are trained with an increasing number of features and the results are validated through stratified 10-fold cross validation. Comparative results show the superiority of support vector machine and random forest models for classification, while support vector machine and linear regression are better suited to predict apnea-hypopnea index. Also, a limited number of features are enough to achieve the maximum predictive accuracy. The best average classification accuracy on test sets is 44.7 percent, with the same average sensitivity (recall). In only 5.7 percent of cases, a severe obstructive sleep apnea syndrome (class 4) is misclassified as mild (class 2). Regression results show a minimum achieved root mean squared error of 22.17. The problem of predicting apnea-hypopnea index or severity classes for obstructive sleep apnea syndrome is very difficult when using only data collected prior to polysomnography test. The results achieved with the available data suggest the use of machine learning methods as tools for providing patients with a priority level for polysomnography test, but they still cannot be used for automated diagnosis.

Prediagnosis of Obstructive Sleep Apnea via Multiclass MTS

Obstructive sleep apnea (OSA) has become an important public health concern. Polysomnography (PSG) is traditionally considered an established and effective diagnostic tool providing information on the severity of OSA and the degree of sleep fragmentation. However, the numerous steps in the PSG test to diagnose OSA are costly and time consuming. This study aimed to apply the multiclass Mahalanobis-Taguchi system (MMTS) based on anthropometric information and questionnaire data to predict OSA. Implementation results showed that MMTS had an accuracy of 84.38% on the OSA prediction and achieved better performance compared to other approaches such as logistic regression, neural networks, support vector machine, C4.5 decision tree, and rough set. Therefore, MMTS can assist doctors in prediagnosis of OSA before running the PSG test, thereby enabling the more effective use of medical resources.

Validity of aircraft noise induced awakening predictions

Aircraft noise disturbs sleep and impairs recuperation. Integrative noise measures like LDN or Lnight are commonly used to implicitly describe and limit nocturnal aircraft noise effects. However, noise exposures may differ in their sleep disturbing potential but nevertheless calculate to the same LDN or Lnight. Therefore, it has recently become more popular to use exposure-response relationships for single noise events to explicitly predict the degree of sleep fragmentation for a whole night with multiple noise events. These models have been criticized for not accounting for dependence between consecutive noise events, for disregarding the placement of noise events within the night, for disregarding other important acoustic parameters beside maximum sound pressure level (SPL) or single event level (SEL), and for not considering inter-individual differences in noise sensitivity. In this analysis, a Basic Model containing maximum SPL as the only explanatory variable and an Extended Model containing elapsed sleep time, time between maxima, and noise duration as additional variables are compared according to bias and precision in predicting the number of noise induced awakenings in single nights. Random subject effect logistic regression based on field study data, where the reaction to 16279 aircraft noise events was monitored in 64 subjects and 479 subject nights, was used for both models. The results indicate that a variable should only be included in the prediction model if (a) the variable has a relevant impact on sleep, (b) information on the distribution of the variable in the target population is available, and (c) the distribution of the variable in the target population differs relevantly from the distribution in the population that was used to generate the prediction model.

Sleep in the intensive care unit.

Abnormalities of sleep are extremely common in critically ill patients, but the mechanisms are poorly understood. About half of total sleep time occurs during the daytime, and circadian rhythm is markedly diminished or lost. Judgments based on inspection consistently overestimate sleep time and do not detect sleep disruption. Accordingly, reliable polygraphic recordings are needed to measure sleep quantity and quality in critically ill patients. Critically ill patients exhibit more frequent arousals and awakenings than is normal, and decreases in rapid eye movement and slow wave sleep. The degree of sleep fragmentation is at least equivalent to that seen in patients with obstructive sleep apnea. About 20% of arousals and awakenings are related to noise, 10% are related to patient care activities, and the cause for the remainder is not known; severity of underlying disease is likely an important factor. Mechanical ventilation can cause sleep disruption, but the precise mechanism has not been defined. Sleep disruption can induce sympathetic activation and elevation of blood pressure, which may contribute to patient morbidity. In healthy subjects, sleep deprivation can decrease immune function and promote negative nitrogen balance. Measures to improve the quantity and quality of sleep in critically ill patients include careful attention to mode of mechanical ventilation, decreasing noise, and sedative agents (although the latter are double-edged swords).

Neuropsychological Deficits and Sleep in Myotonic Dystrophy

Eight patients with myotonic dystrophy underwent comprehensive neuropsychological testing and overnight polysomnography to assess whether the waking cognitive impairment might be attributable to sleep structure abnormalities or to sleep-related respiratory problems. Patients showed substantial neuropsychological deficits, fragmented nocturnal sleep and, in half the patients, sleep apnea and/or hypopnea both mainly of central type. There was no statistically significant correlation between the degree of daytime cognitive deficit and the degree of sleep fragmentation or of respiratory problems at night. It was concluded that the neuropsychological deficit in mytonic dystrophy cannot be attributed to a secondary effect of nocturnal sleep apnea or sleep disruption but probably represents a direct effect of CNS lesions.

Postsurgical Evaluation of Uvulopalatopharyngoplasty: Two Case Reports

Uvulopalatopharyngoplasty (UPPP) has become a widely practiced procedure for the treatment of snoring and obstructive sleep apnea. Surgical outcome with UPPP has heretofore been assessed by investigators using the number of apneas per hour of sleep (apnea index) as the sole or primary criterion. We present two cases in which UPPP outcome was inadequately represented by the apnea index. It is suggested that the apnea/hypopnea index, extent of oxygen desaturations, degree of sleep fragmentation, level of daytime somnolence, interaction between respiratory function and sleep position and stages, and degree of respiratory strain be considered in the postsurgical evaluation.

Fragmentary pathological myoclonus in NREM sleep

A 42-year-old patient is reported who presented with marked daytime sleepiness and in whom the only major nocturnal polysomnographic abnormality was intense fragmentary (partial) myoclonus occurring with equal frequency in all stages of NREM sleep associated with some degree of sleep fragmentation. The myoclonus was very brief (less than 150 msec duration), aperiodic and recurred in asynchronous and asymmetrical fashion over the legs, arms and face. It appears unrelated to the clinically similar physiological myoclonus of REM sleep. Other main sleep disorders such as periodic movements, restless leg syndrome, sleep apnea and narcolepsy-cataplexy were excluded by history and polysomnography.