Analysis of sleep for the diagnosis of sleep disorders such as Type-1 Narcolepsy (T1N) currently requires visual inspection of polysomnography records by trained scoring technicians. Here, we used ...neural networks in approximately 3,000 normal and abnormal sleep recordings to automate sleep stage scoring, producing a hypnodensity graph-a probability distribution conveying more information than classical hypnograms. Accuracy of sleep stage scoring was validated in 70 subjects assessed by six scorers. The best model performed better than any individual scorer (87% versus consensus). It also reliably scores sleep down to 5 s instead of 30 s scoring epochs. A T1N marker based on unusual sleep stage overlaps achieved a specificity of 96% and a sensitivity of 91%, validated in independent datasets. Addition of HLA-DQB1*06:02 typing increased specificity to 99%. Our method can reduce time spent in sleep clinics and automates T1N diagnosis. It also opens the possibility of diagnosing T1N using home sleep studies.
•Periodic limb movement index (PLMI) ≥15 highly prevalent in adult population.•The likelihood of having a PLMI ≥15 increased significantly with age.•PLMI ≥15 not associated with sleepiness unless ...stratified by restless legs syndrome.
Periodic limb movements in sleep (PLMS) are thought to be prevalent in elderly populations, but their impact on quality of life remains unclear. We examined the prevalence of PLMS, impact of age on prevalence, and association between PLMS and sleepiness.
We identified limb movements in 2335 Wisconsin Sleep Cohort polysomnograms collected over 12 years. Prevalence of periodic limb movement index (PLMI) ≥15 was calculated at baseline (n = 1084). McNemar's test assessed changes in prevalence over time. Association of sleepiness and PLMS evaluated using linear mixed modeling and generalized estimating equations. Models adjusted for confounders.
Prevalence of PLMI ≥15 at baseline was 25.3%. Longitudinal prevalence increased significantly with age (p = 2.97 × 10−14). Sleepiness did not differ significantly between PLMI groups unless stratified by restless legs syndrome (RLS) symptoms. The RLS+/PLM+ group was sleepier than the RLS+/PLM− group. Multiple Sleep Latency Test trended towards increased alertness in the RLS−/PLM+ group compared to RLS−/PLM−.
A significant number of adults have PLMS and prevalence increased with age. No noteworthy association between PLMI category and sleepiness unless stratified by RLS symptoms.
Our results indicate that RLS and PLMS may have distinct clinical consequences and interactions that can help guide treatment approach.
The National Cancer Institute's (NCI) wear time classification algorithm uses a rule based on the occurrence of physical activity data counts-a cumulative measure of movement, influenced by both ...magnitude and duration of acceleration-to differentiate between when a physical activity monitoring (PAM) device (ActiGraph accelerometer) is being worn by a participant (wear) from when it is not (nonwear). It was applied to PAM data generated from the 2003-2004 National Health and Nutrition Examination Survey (NHANES 2003-2004). We discuss two corner case conditions that can produce unexpected, and perhaps unintended results when the algorithm is applied. We show, using simulated data of two special cases, how this algorithm classifies a 24-hour period with only 72 total counts as 100% wear in one case, and classifies a 24-hour period with 96,000 counts as 0.1% wear in another. The prevalence of like scenarios in the NHANES 2003-2004 PAM dataset is presented with corresponding summary statistics for varying degrees of the algorithm's nonwear classification threshold (T). The number of participants with valid days, defined as 10 or more hours classified as wear time in a 24-hour day, increased while the mean counts-per-minute (CPM) decreased as the threshold for excluding non-wear was reduced from the allowed 4,000 counts in an hour. The number of participants with four or more valid days increased 2.29% (n = 113) and mean CPM dropped 2.45% (9.5 CPM) when adjusting the nonwear classification threshold to 50 counts an hour. Applying the most liberal criteria, only excluding hours as nonwear which contained 1 count or less, resulted in a 397 more participants (7.83% increase) and 26.5 fewer CPM (6.98% decrease) in NHANES 2003-2004 participants with four or more valid days. The algorithm should be used with caution due to the potential influence of these corner cases.
Abstract Determining diagnostic criteria for specific disorders is often a tedious task that involves determining optimal diagnostic thresholds for symptoms and biomarkers using receiver-operating ...characteristic (ROC) statistics. To help this endeavor, we developed softROC, a user-friendly graphic-based tool that lets users visually explore possible ROC tradeoffs. The software requires MATLAB installation and an Excel file containing threshold symptoms/biological measures, with corresponding gold standard diagnoses for a set of patients. The software scans the input file for diagnostic and symptom/biomarkers columns, and populates the graphical-user-interface (GUI). Users select symptoms/biomarkers of interest using Boolean algebra as potential inputs to create diagnostic criteria outputs. The software evaluates subtests across the user-established range of cut-points and compares them to a gold standard in order to generate ROC and quality ROC scatter plots. These plots can be examined interactively to find optimal cut-points of interest for a given application (e.g. sensitivity versus specificity needs). Split-set validation can also be used to set up criteria and validate these in independent samples. Bootstrapping is used to produce confidence intervals. Additional statistics and measures are provided, such as the area under the ROC curve (AUC). As a testing set, softROC is used to investigate nocturnal polysomnogram measures as diagnostic features for narcolepsy. All measures can be outputted to a text file for offline analysis. The softROC toolbox, with clinical training data and tutorial instruction manual, is provided as supplementary material and can be obtained online at http://www.stanford.edu/~hyatt4/software/softroc or from the open source repository at http://www.github.com/informaton/softroc.
A data visualization tool, the PhenoFinder, is developed to help researchers find clinically meaningful or heritable patterns in brainwave activity during sleep in a cohort of 1836 nocturnal ...polysomnography studies. The interactive software lets researchers quickly view and explore various electroencephalography power spectral density patient profiles and select desired phenotypes for genome-wide association or sequencing. The design study addressed here evolved over an iterative process of informal studies conducted with end-users from Stanford's Center for Sleep Sciences and Behavioral Medicine and focused on highlighting the similarities and differences in patient data. Several new hypotheses were formed and new phenotypes considered during the design process. The software is primarily for domain experts; however, a lay user may find exploration of demographic changes in sleep insightful. It is available online with a small companion data-set at
http://www.github.com/informaton/phenofinder
.
SEV is a graphical toolbox designed in MATLAB for displaying polysomnography (PSG) signals recorded during sleep studies, prototyping signal-processing algorithms and automating sleep feature ...extraction methods across large collections or cohorts of such studies. Format imported are European Data Formats and event/hypnogram files. Time-series analysis can be performed using a suite of classifiers, filters and signal decomposition tools (e.g. wavelets) developed internally or implemented from validated methods published by others. Power spectral analysis can be performed using either periodogram averaging or multiple spectrum independent component analysis. The tool is highly configurable and provides a simple framework for classifier optimization and extensibility. MATLAB's parallel processing toolbox is utilized during batch processing. Output formats include MySQL database entry, tab-delimited text and MATLAB archive (.MAT). The tool is well suited for genetic or epidemiological sleep research questions requiring rigorous, robust and reproducible evaluation of a PSG-based sleep study cohort. Current built-in applications include modules to detect and quantify rapid eye movements and spindle activity (using existing algorithms), inter-channel electroencephalography coherence and a detector developed in house to quantify periodic leg movements during sleep. SEV is open source and freely available under a common creative license.
Analysis of sleep for the diagnosis of sleep disorders such as Type-1 Narcolepsy (T1N) currently requires visual inspection of polysomnography records by trained scoring technicians. Here, we used ...neural networks in approximately 3,000 normal and abnormal sleep recordings to automate sleep stage scoring, producing a hypnodensity graph - a probability distribution conveying more information than classical hypnograms. Accuracy of sleep stage scoring was validated in 70 subjects assessed by six scorers. The best model performed better than any individual scorer (87% versus consensus). It also reliably scores sleep down to 5 instead of 30 second scoring epochs. A T1N marker based on unusual sleep-stage overlaps achieved a specificity of 96% and a sensitivity of 91%, validated in independent datasets. Addition of HLA-DQB1*06:02 typing increased specificity to 99%. Our method can reduce time spent in sleep clinics and automates T1N diagnosis. It also opens the possibility of diagnosing T1N using home sleep studies.
To examine association between periodic leg movements (PLM) and 13 single nucleotide polymorphisms (SNPs) in 6 loci known to increase risk of restless legs syndrome (RLS).
Stanford Center for Sleep ...Sciences and Medicine and Clinical Research Unit of University of Wisconsin Institute for Clinical and Translational Research.
Adult participants (n = 1,090, mean age = 59.7 years) from the Wisconsin Sleep Cohort (2,394 observations, 2000-2012).
A previously validated automatic detector was used to measure PLMI. Thirteen SNPs within BTBD9, TOX3/BC034767, MEIS1 (2 unlinked loci), MAP2K5/SKOR1, and PTPRD were tested. Analyses were performed using a linear model and by PLM category using a 15 PLM/h cutoff. Statistical significance for loci was Bonferroni corrected for 6 loci (P < 8.3 × 10(-3)). RLS symptoms were categorized into four groups: likely, possible, no symptoms, and unknown based on a mailed survey response.
Prevalence of PLMI ≥ 15 was 33%. Subjects with PLMs were older, more likely to be male, and had more frequent RLS symptoms, a shorter total sleep time, and higher wake after sleep onset. Strong associations were found at all loci except one. Highest associations for PLMI > 15/h were obtained using a multivariate model including age, sex, sleep disturbances, and the best SNPs for each loci, yielding the following odds ratios (OR) and P values: BTBD9 rs3923809(A) OR = 1.65, P = 1.5×10(-8); TOX3/BC034767 rs3104788(T) OR = 1.35, P = 9.0 × 10(-5); MEIS1 rs12469063(G) OR = 1.38, P = 2.0 × 10(-4); MAP2K5/SKOR1 rs6494696(G) OR = 1.24, P = 1.3×10(-2); and PTPRD(A) rs1975197 OR = 1.31, P = 6.3×10(-3). Linear regression models also revealed significant PLM effects for BTBD9, TOX3/BC034767, and MEIS1. Co-varying for RLS symptoms only modestly reduced the genetic associations.
Single nucleotide polymorphisms demonstrated to increase risk of RLS are strongly linked to increased PLM as well, although some loci may have more effects on one versus the other phenotype.
Highlights • Periodic leg movements and low serum ferritin are associated in the Wisconsin Sleep Cohort. • The finding was evident only after controlling for age, sex, inflammation, and genetic ...factors. • This suggests an association between mild iron deficiency and periodic leg movement index (PLMI) in the general population.
Abstract Background Provision of fortified juices may provide a convenient method to maintain and increase blood fat-soluble vitamins. Objective To determine whether children consuming orange juice ...fortified with calcium and combinations of vitamins D, E, and A could increase serum 25-hydroxyvitamin D 25(OH)D, α-tocopherol, and retinol levels. Design A 12-week randomized, double-blind, controlled trial. Participants/setting One hundred eighty participants (aged 8.04±1.42 years) were recruited at Tufts (n=70) and Boston University (n=110) during 2005-2006. Of those recruited, 176 children were randomized into three groups: CaD (700 mg calcium+200 IU vitamin D), CaDEA (700 mg calcium+200 IU vitamin D+12 IU vitamin E+2,000 IU vitamin A as beta carotene), or Ca (700 mg calcium). Children consumed two 240-mL glasses of CaD, CaDEA, or Ca fortified orange juice daily for 12 weeks. Main outcome measures Serum 25(OH)D, α-tocopherol, and retinol concentrations. Statistical analyses Changes in 25(OH)D, α-tocopherol, retinol, and parathyroid hormone concentrations were examined. Covariates included sex, age, race/ethnicity, body mass index, and baseline 25(OH)D, α-tocopherol, retinol, or parathyroid hormone levels. Multivariate models and repeated measures analysis of variance tested for group differences with pre–post measures (n=141). Results Baseline 25(OH)D was 68.4±27.7 nmol/L (27.4±11.10 ng/mL) ), with 21.7% of participants having inadequate 25(OH)D (<50 nmol/L 20.03 ng/mL). The CaD group's 25(OH)D increase was greater than that of the Ca group (12.7 nmol/L 5.09 ng/mL, 95% CI 1.3 to 24.1; P =0.029). The CaDEA group's increase in α-tocopherol concentration was greater than that in the Ca or CaD groups (3.79 μmol/L 0.16 μg/mL, 95% CI 2.5 to 5.1 and 3.09 μmol/L 0.13 μg/mL, 95% CI −1.8 to 4.3), respectively ( P <0.0001). Retinol levels did not change, and body weight remained as expected for growth. Conclusions Daily consumption of orange juice providing 200 IU vitamin D and 12 IU vitamin E increased 25(OH)D and α-tocopherol concentrations in young children within 12 weeks.
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