Auto-annotating sleep stages based on polysomnographic data.
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ABSTRACT:
SUBMITTER: Zhang H
PROVIDER: S-EPMC8767308 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Obstructive sleep apnea (OSA) is the most common sleep disorder. Sleep bruxism (SB) is a masticatory muscle activity during sleep that commonly co-occurs with OSA. The presented study aimed to assess this relationship and to identify factors affecting this co-occurrence. Adult patients (n = 110) were evaluated for OSA and SB in a sleep laboratory using polysomnography. The episodes of bruxism and respiratory events were scored according to the standards of the American Academy of Sleep Medicine. The prevalence of OSA and SB was found to be 86.37% and 50%, respectively. The bruxism episode index (BEI) was increased in the group with mild and moderate OSA (apnea-hypopnea index (AHI) <30) compared to that in the group with severe OSA (AHI ? 30) (5.50 ± 4.58 vs. 1.62 ± 1.28, p < 0.05). A positive correlation between AHI and BEI was observed in the group with AHI < 30. Regression analysis indicated that higher AHI, male gender, and diabetes were independent predictors for the increased BEI in group with AHI < 30. The relationship between OSA and SB depends on the degree of severity of OSA. OSA is correlated with SB in mild and moderate cases of OSA in the group of patients with increased risk of OSA.
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Project description:Rapid eye movement (REM) sleep behavior disorder is a condition characterized by dream enactment. This condition may accompany neurodegenerative disorders. However, only a few reports from India are available, that too, without any polysomnographic evidence. We are reporting a case of REM sleep behavior disorder with polysomnographic evidence.
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Project description:BACKGROUND:Sleep-wakefulness cycles are an essential diagnostic criterion for disorders of consciousness (DOC), differentiating prolonged DOC from coma. Specific sleep features, like the presence of sleep spindles, are an important marker for the prognosis of recovery from DOC. Based on increasing evidence for a link between sleep and neuronal plasticity, understanding sleep in DOC might facilitate the development of novel methods for rehabilitation. Yet, well-controlled studies of sleep in DOC are lacking. Here, we aimed to quantify, on a reliable evaluation basis, the distribution of behavioral and neurophysiological sleep patterns in DOC over a 24-h period while controlling for environmental factors (by recruiting a group of conscious tetraplegic patients who resided in the same hospital). METHODS:We evaluated the distribution of sleep and wakefulness by means of polysomnography (EEG, EOG, EMG) and video recordings in 32 DOC patients (16 unresponsive wakefulness syndrome [UWS], 16 minimally conscious state [MCS]), and 10 clinical control patients with severe tetraplegia. Three independent raters scored the patients' polysomnographic recordings. RESULTS:All but one patient (UWS) showed behavioral and electrophysiological signs of sleep. Control and MCS patients spent significantly more time in sleep during the night than during daytime, a pattern that was not evident in UWS. DOC patients (particularly UWS) exhibited less REM sleep than control patients. Forty-four percent of UWS patients and 12% of MCS patients did not have any REM sleep, while all control patients (100%) showed signs of all sleep stages and sleep spindles. Furthermore, no sleep spindles were found in 62% of UWS patients and 21% of MCS patients. In the remaining DOC patients who had spindles, their number and amplitude were significantly lower than in controls. CONCLUSIONS:The distribution of sleep signs in DOC over 24?h differs significantly from the normal sleep-wakefulness pattern. These abnormalities of sleep in DOC are independent of external factors such as severe immobility and hospital environment.
| S-EPMC7678091 | biostudies-literature
Project description:Study objectivesMany sleep disorders are characterized by increased motor activity during sleep. In contrast, studies on motor activity during physiological sleep are largely lacking. We quantitatively investigated a large range of motor phenomena during polysomnography in physiological sleep.DesignProspective polysomnographic investigation.SettingAcademic referral sleep laboratory.ParticipantsOne hundred healthy sleepers age 19-77 y were strictly selected from a representative population sample by a two-step screening procedure.InterventionsN/A.Measurements and resultsPolysomnography according to American Academy of Sleep Medicine (AASM) standards was performed, and quantitative normative values were established for periodic limb movements in sleep (PLMS), high frequency leg movements (HFLM), fragmentary myoclonus (FM), neck myoclonus (NM), and rapid eye movement (REM)-related electromyographic (EMG) activity. Thirty-six subjects had a PLMS index > 5/h, 18 had a PLMS index > 15/h (90th percentile: 24.8/h). Thirty-three subjects had HFLM (90th percentile: four sequences/night). All subjects had FM (90th percentile 143.7/h sleep). Nine subjects fulfilled AASM criteria for excessive FM. Thirty-five subjects had NM (90th percentile: 8.8/h REM sleep). For REM sleep, different EMG activity measures for the mentalis and flexor digitorum superficialis muscles were calculated: the 90th percentile for phasic mentalis EMG activity for 30-sec epochs according to AASM recommendation was 15.6%, and for tonic mentalis EMG activity 2.6%. Twenty-five subjects exceeded the recently proposed phasic mentalis cutoff of 11%. None of the subjects exceeded the tonic mentalis cutoff of 9.6%.ConclusionQuantification of motor phenomena is a basic prerequisite to develop normative values, and is a first step toward a more precise description of the various motor phenomena present during sleep. Because rates of motor events were unexpectedly high even in physiological sleep, the future use of normative values for both research and clinical routine is essential.
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Project description:As the number of freight trains on railway networks increases, so does the potential for vibration exposure in dwellings nearby to freight railway lines. Nocturnal trains in particular are of particular importance since night-time exposure may interfere with sleep. The present work investigates the impact of vibration and noise from night-time freight trains on human sleep. In an experimental polysomnographic laboratory study, 24 young healthy volunteers with normal hearing were exposed to simulated freight pass-bys with vibration amplitudes of 0.7 and 1.4?mm/s either 20 or 36 times during the night. Stronger vibrations were associated with higher probabilities of event-related arousals and awakenings (p?<?0.001), and sleep stage changes (p?<?0.05). Sleep macrostructure was most affected in high vibration nights with 36 events, with increased wakefulness (p?<?0.05), reduced continual slow wave sleep (p?<?0.05), earlier awakenings (p?<?0.05) and an overall increase in sleep stage changes (p?<?0.05). Subjects reported sleep disturbance due to vibration (F(4,92)?=?25.9, p?<?0.001) and noise (F(4,92)?=?25.9, p?<?0.001), with the number of trains having an effect only for the 0.7?mm/s condition (p?<?0.05). The findings show that combined vibration and noise from railway freight affects the natural rhythm of sleep, but extrapolation of significance for health outcomes should be approached with caution.
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Project description:Study objectivesTo investigate whether a foundational transformer model using 8-hour, multichannel data from polysomnograms can outperform existing artificial intelligence (AI) methods for sleep stage classification.MethodsWe utilized the Sleep Heart Health Study (SHHS) visits 1 and 2 for training and validation and the Multi-Ethnic Study of Atherosclerosis (MESA) for testing of our model. We trained a self-supervised foundational transformer (called PFTSleep) that encodes 8-hour long sleep studies at 125 Hz with 7 signals including brain, movement, cardiac, oxygen, and respiratory channels. These encodings are used as input for training of an additional model to classify sleep stages, without adjusting the weights of the foundational transformer. We compared our results to existing AI methods that did not utilize 8-hour data or the full set of signals but did report evaluation metrics for the SHHS dataset.ResultsWe trained and validated a model with 8,444 sleep studies with 7 signals including brain, movement, cardiac, oxygen, and respiratory channels and tested on an additional 2,055 studies. In total, we trained and tested 587,944 hours of sleep study signal data. Area under the precision recall curve (AUPRC) scores were 0.82, 0.40, 0.53, 0.75, and 0.82 and area under the receiving operating characteristics curve (AUROC) scores were 0.99, 0.95, 0.96, 0.98, and 0.99 for wake, N1, N2, N3, and REM, respectively, on the SHHS validation set. For MESA, the AUPRC scores were 0.56, 0.16, 0.40, 0.45, and 0.65 and AUROC scores were 0.94, 0.77, 0.87, 0.91, and 0.96, respectively. Our model was compared to the longest context window state-of-the-art model and showed increases in macro evaluation scores, notably sensitivity (3.7% increase) and multi-class REM (3.39% increase) and wake (0.97% increase) F1 scores.ConclusionsUtilizing full night, multi-channel PSG data encodings derived from a foundational transformer improve sleep stage classification over existing methods.
| S-EPMC11326349 | biostudies-literature
Project description:Investigating sleep in mental disorders has the potential to reveal both disorder-specific and transdiagnostic psychophysiological mechanisms. This meta-analysis aimed at determining the polysomnographic (PSG) characteristics of several mental disorders. Relevant studies were searched through standard strategies. Controlled PSG studies evaluating sleep in affective, anxiety, eating, pervasive developmental, borderline and antisocial personality disorders, attention-deficit-hyperactivity disorder (ADHD), and schizophrenia were included. PSG variables of sleep continuity, depth, and architecture, as well as rapid-eye movement (REM) sleep were considered. Calculations were performed with the "Comprehensive Meta-Analysis" and "R" software. Using random effects modeling, for each disorder and each variable, a separate meta-analysis was conducted if at least 3 studies were available for calculation of effect sizes as standardized means (Hedges' g). Sources of variability, that is, sex, age, and mental disorders comorbidity, were evaluated in subgroup analyses. Sleep alterations were evidenced in all disorders, with the exception of ADHD and seasonal affective disorders. Sleep continuity problems were observed in most mental disorders. Sleep depth and REM pressure alterations were associated with affective, anxiety, autism and schizophrenia disorders. Comorbidity was associated with enhanced REM sleep pressure and more inhibition of sleep depth. No sleep parameter was exclusively altered in 1 condition; however, no 2 conditions shared the same PSG profile. Sleep continuity disturbances imply a transdiagnostic imbalance in the arousal system likely representing a basic dimension of mental health. Sleep depth and REM variables might play a key role in psychiatric comorbidity processes. Constellations of sleep alterations may define distinct disorders better than alterations in 1 single variable. (PsycINFO Database Record
| S-EPMC5110386 | biostudies-literature
Project description:BackgroundObstructive sleep apnoea (OSA) is a heterogeneous disorder, and improved understanding of physiologic phenotypes and their clinical implications is needed. We aimed to determine whether routine polysomnographic data can be used to identify OSA phenotypes (clusters) and to assess the associations between the phenotypes and cardiovascular outcomes.MethodsCross-sectional and longitudinal analyses of a multisite, observational US Veteran (n=1247) cohort were performed. Principal components-based clustering was used to identify polysomnographic features in OSA's four pathophysiological domains (sleep architecture disturbance, autonomic dysregulation, breathing disturbance and hypoxia). Using these features, OSA phenotypes were identified by cluster analysis (K-means). Cox survival analysis was used to evaluate longitudinal relationships between clusters and the combined outcome of incident transient ischaemic attack, stroke, acute coronary syndrome or death.ResultsSeven patient clusters were identified based on distinguishing polysomnographic features: 'mild', 'periodic limb movements of sleep (PLMS)', 'NREM and arousal', 'REM and hypoxia', 'hypopnoea and hypoxia', 'arousal and poor sleep' and 'combined severe'. In adjusted analyses, the risk (compared with 'mild') of the combined outcome (HR (95% CI)) was significantly increased for 'PLMS', (2.02 (1.32 to 3.08)), 'hypopnoea and hypoxia' (1.74 (1.02 to 2.99)) and 'combined severe' (1.69 (1.09 to 2.62)). Conventional apnoea-hypopnoea index (AHI) severity categories of moderate (15≤AHI<30) and severe (AHI ≥30), compared with mild/none category (AHI <15), were not associated with increased risk.ConclusionsAmong patients referred for OSA evaluation, routine polysomnographic data can identify physiological phenotypes that capture risk of adverse cardiovascular outcomes otherwise missed by conventional OSA severity classification.
| S-EPMC6693344 | biostudies-literature
Project description:Background and objectives: Sleep bruxism is a common phenomenon that can affect approximately 13% of adult population. It is estimated that bruxism can be caused by three types of factors: biological, psychological, and exogenous. There are many scientific reports about the coexistence of bruxism, stress, and psychoemotional disorders. The aim of this study is to evaluate the possible correlation between occurrence of sleep bruxism and perceived stress and depressive symptoms. Material and methods: The material of this study consisted of 77 patients of Clinic of Prosthetic Dentistry operating at the Department of Prosthetic Dentistry, Wroclaw Medical University, Poland in which after using guidelines of the American Academy of Sleep Medicine probable sleep bruxism was fund. Patients then underwent video-polysomnography. Exposure to perceived stress was evaluated with Perceived Stress Scale-10 (PSS-10). Occurrence of depressive symptoms was evaluated with Beck's Depression Inventory (BDI). Results: The analysis showed lack of statistically significant correlation between Bruxism Episodes Index (BEI) and Perceived Stress Scale-10 and Beck's Depression Inventory scores (p = 0.64, p = 0.65; respectively), also when comparing study group (bruxers) and control group (non-bruxers) (p = 0.88, p = 0.77; respectively). Conclusion: Intensity of sleep bruxism was not statistically significantly correlated with self-reported perceived stress and depression. This issue requires further research.
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Project description:Huge amounts of small compound bioactivity data have been entering the public domain as a consequence of open innovation initiatives. It is now the time to carefully analyse existing bioassay data and give it a systematic structure. Our study aims to annotate prominent in vitro assays used for the determination of bioactivities of human P-glycoprotein inhibitors and substrates as they are represented in the ChEMBL and TP-search open source databases. Furthermore, the ability of data, determined in different assays, to be combined with each other is explored. As a result of this study, it is suggested that for inhibitors of human P-glycoprotein it is possible to combine data coming from the same assay type, if the cell lines used are also identical and the fluorescent or radiolabeled substrate have overlapping binding sites. In addition, it demonstrates that there is a need for larger chemical diverse datasets that have been measured in a panel of different assays. This would certainly alleviate the search for other inter-correlations between bioactivity data yielded by different assay setups.
| S-EPMC3531886 | biostudies-literature