Automatic sleep classification using adaptive segmentation reveals an increased number of rapid eye movement sleep transitions.

The reference standard for sleep classification uses manual scoring of polysomnography with fixed 30-s epochs. This limits the analysis of sleep pattern, structure and, consequently, detailed association with other physiologic processes. We aimed to improve the details of sleep evaluation by developing a data-driven method that objectively classifies sleep in smaller time intervals. Two adaptive segmentation methods using 3, 10 and 30-s windows were compared. One electroencephalographic (EEG) channel was used to segment into quasi-stationary segments and each segment was classified using a multinomial logistic regression model. Classification features described the power in the clinical frequency bands of three EEG channels and an electrooculographic (EOG) anticorrelation measure for each segment. The models were optimised using 19 healthy control subjects and validated on 18 healthy control subjects. The models obtained overall accuracies of 0.71 ± 0.09, 0.74 ± 0.09 and 0.76 ± 0.08 on the validation data. However, the models allowed a more dynamic sleep, which challenged a true validation against manually scored hypnograms with fixed epochs. The automated classifications indicated an increased number of stage transitions and shorter sleep bouts using models with smaller window size compared with the hypnograms. An increased number of transitions from rapid eye movement (REM) sleep was likewise expressed in the model using 30-s windows, indicating that REM sleep has more fluctuations than captured by today's standard. The models developed are generally applicable and may contribute to concise sleep structure evaluation, research in sleep control and improved understanding of sleep and sleep disorders. The models could also contribute to objective measuring of sleep stability.

Proteomic biomarkers of sleep apnea.

STUDY OBJECTIVES: Obstructive sleep apnea (OSA) is characterized by recurrent partial to complete upper airway obstructions during sleep, leading to repetitive arousals and oxygen desaturations. Although many OSA biomarkers have been reported individually, only a small subset have been validated through both cross-sectional and intervention studies. We sought to profile serum protein biomarkers in OSA in unbiased high throughput assay. METHODS: A highly multiplexed aptamer array (SomaScan) was used to profile 1300 proteins in serum samples from 713 individuals in the Stanford Sleep Cohort, a patient-based registry. Outcome measures derived from overnight polysomnography included Obstructive Apnea Hypopnea Index (OAHI), Central Apnea Index (CAI), 2% Oxygen Desaturation index, mean and minimum oxygen saturation indices during sleep. Additionally, a separate intervention-based cohort of 16 individuals was used to assess proteomic profiles pre- and post-intervention with positive airway pressure. RESULTS: OAHI was associated with 65 proteins, predominantly pathways of complement, coagulation, cytokine signaling, and hemostasis which were upregulated. CAI was associated with two proteins including Roundabout homolog 3 (ROBO3), a protein involved in bilateral synchronization of the pre-Bötzinger complex and cystatin F. Analysis of pre- and post intervention samples revealed IGFBP-3 protein to be increased while LEAP1 (Hepicidin) to be decreased with intervention. An OAHI machine learning classifier (OAHI >=15 vs OAHI<15) trained on SomaScan protein measures alone performed robustly, achieving 76% accuracy in a validation dataset. CONCLUSIONS: Multiplex protein assays offer diagnostic potential and provide new insights into the biological basis of sleep disordered breathing.

Munc13-4 is an effector of rab27a and controls secretion of lysosomes in hematopoietic cells.

Griscelli syndrome type 2 (GS2) is a genetic disorder in which patients exhibit life-threatening defects of cytotoxic T lymphocytes (CTLs) whose lytic granules fail to dock on the plasma membrane and therefore do not release their contents. The disease is caused by the absence of functional rab27a, but how rab27a controls secretion of lytic granule contents remains elusive. Mutations in Munc13-4 cause familial hemophagocytic lymphohistiocytosis subtype 3 (FHL3), a disease phenotypically related to GS2. We show that Munc13-4 is a direct partner of rab27a. The two proteins are highly expressed in CTLs and mast cells where they colocalize on secretory lysosomes. The region comprising the Munc13 homology domains is essential for the localization of Munc13-4 to secretory lysosomes. The GS2 mutant rab27aW73G strongly reduced binding to Munc13-4, whereas the FHL3 mutant Munc13-4Delta608-611 failed to bind rab27a. Overexpression of Munc13-4 enhanced degranulation of secretory lysosomes in mast cells, showing that it has a positive regulatory role in secretory lysosome fusion. We suggest that the secretion defects seen in GS2 and FHL3 have a common origin, and we propose that the rab27a/Munc13-4 complex is an essential regulator of secretory granule fusion with the plasma membrane in hematopoietic cells. Mutations in either of the two genes prevent formation of this complex and abolish secretion.

Breathing Disturbances Without Hypoxia Are Associated With Objective Sleepiness in Sleep Apnea.

Study Objectives: To determine whether defining two subtypes of sleep-disordered breathing (SDB) events-with or without hypoxia-results in measures that are more strongly associated with hypertension and sleepiness. Methods: A total of 1022 participants with 2112 nocturnal polysomnograms from the Wisconsin Sleep Cohort were analyzed with our automated algorithm, developed to detect breathing disturbances and desaturations. Breathing events were time-locked to desaturations, resulting in two indices-desaturating (hypoxia-breathing disturbance index [H-BDI]) and nondesaturating (nonhypoxia-breathing disturbance index [NH-BDI]) events-regardless of arousals. Measures of subjective (Epworth Sleepiness Scale) and objective (2981 multiple sleep latency tests from a subset of 865 participants) sleepiness were analyzed, in addition to clinically relevant clinicodemographic variables. Hypertension was defined as BP ≥ 140/90 or antihypertensive use. Results: H-BDI, but not NH-BDI, correlated strongly with SDB severity indices that included hypoxia (r ≥ 0.89, p ≤ .001 with 3% oxygen-desaturation index [ODI] and apnea hypopnea index with 4% desaturations). A doubling of desaturation-associated events was associated with hypertension prevalence, which was significant for ODI but not H-BDI (3% ODI OR = 1.06, 95% CI = 1.00-1.12, p < .05; H-BDI OR 1.04, 95% CI = 0.98-1.10) and daytime sleepiness (ß = 0.20 Epworth Sleepiness Scale [ESS] score, p < .0001; ß = -0.20 minutes in MSL on multiple sleep latency test [MSLT], p < .01). Independently, nondesaturating event doubling was associated with more objective sleepiness (ß = -0.52 minutes in MSL on MSLT, p < .001), but had less association with subjective sleepiness (ß = 0.12 ESS score, p = .10). In longitudinal analyses, baseline nondesaturating events were associated with worsening of H-BDI over a 4-year follow-up, suggesting evolution in severity. Conclusions: In SDB, nondesaturating events are independently associated with objective daytime sleepiness, beyond the effect of desaturating events.

A novel Munc13-4/S100A10/annexin A2 complex promotes Weibel-Palade body exocytosis in endothelial cells.

Endothelial cells respond to blood vessel injury by the acute release of the procoagulant von Willebrand factor, which is stored in unique secretory granules called Weibel-Palade bodies (WPBs). Stimulated WPB exocytosis critically depends on their proper recruitment to the plasma membrane, but factors involved in WPB-plasma membrane tethering are not known. Here we identify Munc13-4, a protein mutated in familial hemophagocytic lymphohistiocytosis 3, as a WPB-tethering factor. Munc13-4 promotes histamine-evoked WPB exocytosis and is present on WPBs, and secretagogue stimulation triggers an increased recruitment of Munc13-4 to WPBs and a clustering of Munc13-4 at sites of WPB-plasma membrane contact. We also identify the S100A10 subunit of the annexin A2 (AnxA2)-S100A10 protein complex as a novel Munc13-4 interactor and show that AnxA2-S100A10 participates in recruiting Munc13-4 to WPB fusion sites. These findings indicate that Munc13-4 supports acute WPB exocytosis by tethering WPBs to the plasma membrane via AnxA2-S100A10.

Sleep stability and transitions in patients with idiopathic REM sleep behavior disorder and patients with Parkinson's disease.

OBJECTIVE: Patients with idiopathic rapid eye movement (REM) sleep behavior disorder (iRBD) are at high risk of developing Parkinson's disease (PD). As wake/sleep-regulation is thought to involve neurons located in the brainstem and hypothalamic areas, we hypothesize that the neurodegeneration in iRBD/PD is likely to affect wake/sleep and REM/non-REM (NREM) sleep transitions. METHODS: We determined the frequency of wake/sleep and REM/NREM sleep transitions and the stability of wake (W), REM and NREM sleep as measured by polysomnography (PSG) in 27 patients with PD, 23 patients with iRBD, 25 patients with periodic leg movement disorder (PLMD) and 23 controls. Measures were computed based on manual scorings and data-driven labeled sleep staging. RESULTS: Patients with PD showed significantly lower REM stability than controls and patients with PLMD. Patients with iRBD had significantly lower REM stability compared with controls. Patients with PD and RBD showed significantly lower NREM stability and significantly more REM/NREM transitions than controls. CONCLUSIONS: We conclude that W, NREM and REM stability and transitions are progressively affected in iRBD and PD, probably reflecting the successive involvement of brain stem areas from early on in the disease. SIGNIFICANCE: Sleep stability and transitions determined by a data-driven approach could support the evaluation of iRBD and PD patients.

Sleep spindle alterations in patients with Parkinson's disease.

UNLABELLED: The aim of this study was to identify changes of sleep spindles (SS) in the EEG of patients with Parkinson's disease (PD). Five sleep experts manually identified SS at a central scalp location (C3-A2) in 15 PD and 15 age- and sex-matched control subjects. Each SS was given a confidence score, and by using a group consensus rule, 901 SS were identified and characterized by their (1) duration, (2) oscillation frequency, (3) maximum peak-to-peak amplitude, (4) percent-to-peak amplitude, and (5) density. Between-group comparisons were made for all SS characteristics computed, and significant changes for PD patients vs. control subjects were found for duration, oscillation frequency, maximum peak-to-peak amplitude and density. Specifically, SS density was lower, duration was longer, oscillation frequency slower and maximum peak-to-peak amplitude higher in patients vs. CONTROLS: We also computed inter-expert reliability in SS scoring and found a significantly lower reliability in scoring definite SS in patients when compared to controls. How neurodegeneration in PD could influence SS characteristics is discussed. We also note that the SS morphological changes observed here may affect automatic detection of SS in patients with PD or other neurodegenerative disorders (NDDs).

Data-driven modeling of sleep EEG and EOG reveals characteristics indicative of pre-Parkinson's and Parkinson's disease.

BACKGROUND: Manual scoring of sleep relies on identifying certain characteristics in polysomnograph (PSG) signals. However, these characteristics are disrupted in patients with neurodegenerative diseases. NEW METHOD: This study evaluates sleep using a topic modeling and unsupervised learning approach to identify sleep topics directly from electroencephalography (EEG) and electrooculography (EOG). PSG data from control subjects were used to develop an EOG and an EEG topic model. The models were applied to PSG data from 23 control subjects, 25 patients with periodic leg movements (PLMs), 31 patients with idiopathic REM sleep behavior disorder (iRBD) and 36 patients with Parkinson's disease (PD). The data were divided into training and validation datasets and features reflecting EEG and EOG characteristics based on topics were computed. The most discriminative feature subset for separating iRBD/PD and PLM/controls was estimated using a Lasso-regularized regression model. RESULTS: The features with highest discriminability were the number and stability of EEG topics linked to REM and N3, respectively. Validation of the model indicated a sensitivity of 91.4% and a specificity of 68.8% when classifying iRBD/PD patients. COMPARISON WITH EXISTING METHOD: The topics showed visual accordance with the manually scored sleep stages, and the features revealed sleep characteristics containing information indicative of neurodegeneration. CONCLUSIONS: This study suggests that the amount of N3 and the ability to maintain NREM and REM sleep have potential as early PD biomarkers. Data-driven analysis of sleep may contribute to the evaluation of neurodegenerative patients.

Automatic sleep classification using a data-driven topic model reveals latent sleep states.

BACKGROUND: The golden standard for sleep classification uses manual scoring of polysomnography despite points of criticism such as oversimplification, low inter-rater reliability and the standard being designed on young and healthy subjects. NEW METHOD: To meet the criticism and reveal the latent sleep states, this study developed a general and automatic sleep classifier using a data-driven approach. Spectral EEG and EOG measures and eye correlation in 1s windows were calculated and each sleep epoch was expressed as a mixture of probabilities of latent sleep states by using the topic model Latent Dirichlet Allocation. Model application was tested on control subjects and patients with periodic leg movements (PLM) representing a non-neurodegenerative group, and patients with idiopathic REM sleep behavior disorder (iRBD) and Parkinson's Disease (PD) representing a neurodegenerative group. The model was optimized using 50 subjects and validated on 76 subjects. RESULTS: The optimized sleep model used six topics, and the topic probabilities changed smoothly during transitions. According to the manual scorings, the model scored an overall subject-specific accuracy of 68.3 ± 7.44 (% µ ± σ) and group specific accuracies of 69.0 ± 4.62 (control), 70.1 ± 5.10 (PLM), 67.2 ± 8.30 (iRBD) and 67.7 ± 9.07 (PD). COMPARISON WITH EXISTING METHOD: Statistics of the latent sleep state content showed accordances to the sleep stages defined in the golden standard. However, this study indicates that sleep contains six diverse latent sleep states and that state transitions are continuous processes. CONCLUSIONS: The model is generally applicable and may contribute to the research in neurodegenerative diseases and sleep disorders.

Classification of iRBD and Parkinson's disease patients based on eye movements during sleep.

Patients suffering from the sleep disorder idiopathic rapid-eye-movement sleep behavior disorder (iRBD) have been observed to be in high risk of developing Parkinson's disease (PD). This makes it essential to analyze them in the search for PD biomarkers. This study aims at classifying patients suffering from iRBD or PD based on features reflecting eye movements (EMs) during sleep. A Latent Dirichlet Allocation (LDA) topic model was developed based on features extracted from two electrooculographic (EOG) signals measured as parts in full night polysomnographic (PSG) recordings from ten control subjects. The trained model was tested on ten other control subjects, ten iRBD patients and ten PD patients, obtaining a EM topic mixture diagram for each subject in the test dataset. Three features were extracted from the topic mixture diagrams, reflecting "certainty", "fragmentation" and "stability" in the timely distribution of the EM topics. Using a Naive Bayes (NB) classifier and the features "certainty" and "stability" yielded the best classification result and the subjects were classified with a sensitivity of 95 %, a specificity of 80% and an accuracy of 90 %. This study demonstrates in a data-driven approach, that iRBD and PD patients may exhibit abnorm form and/or timely distribution of EMs during sleep.

Classification of iRBD and Parkinson's patients using a general data-driven sleep staging model built on EEG.

Sleep analysis is an important diagnostic tool for sleep disorders. However, the current manual sleep scoring is time-consuming as it is a crude discretization in time and stages. This study changes Esbroeck and Westover's [1] latent sleep staging model into a global model. The proposed data-driven method trained a topic mixture model on 10 control subjects and was applied on 10 other control subjects, 10 iRBD patients and 10 Parkinson's patients. In that way 30 topic mixture diagrams were obtained from which features reflecting distinct sleep architectures between control subjects and patients were extracted. Two features calculated on basis of two latent sleep states classified subjects as "control" or "patient" by a simple clustering algorithm. The mean sleep staging accuracy compared to classical AASM scoring was 72.4% for control subjects and a clustering of the derived features resulted in a sensitivity of 95% and a specificity of 80 %. This study demonstrates that frequency analysis of sleep EEG can be used for data-driven global sleep classification and that topic features separates iRBD and Parkinson's patients from control subjects.