The Experts below are selected from a list of 70833 Experts worldwide ranked by ideXlab platform
Clete A Kushida - One of the best experts on this subject based on the ideXlab platform.
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Joint Consensus Statement of the American Academy of Sleep Medicine and Sleep Research Society on the Recommended Amount of Sleep for a Healthy Adult: Methodology and Discussion.
Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine, 2015Co-Authors: Nathaniel F Watson, Gregory Belenky, Donald L Bliwise, Orfeu M Buxton, Daniel J Buysse, David F Dinges, James E Gangwisch, Michael A Grandner, M. Safwan Badr, Clete A KushidaAbstract:The American Academy of Sleep Medicine and Sleep Research Society recently released a Consensus Statement regarding the recommended amount of Sleep to promote optimal health in adults. This paper d...
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Joint consensus statement of the American Academy of Sleep Medicine and Sleep Research Society on the recommended amount of Sleep for a healthy adult: Methodology and discussion
Sleep, 2015Co-Authors: Nathaniel F Watson, Gregory Belenky, Donald L Bliwise, Orfeu M Buxton, Daniel J Buysse, David F Dinges, James E Gangwisch, Michael A Grandner, M. Safwan Badr, Clete A KushidaAbstract:The American Academy of Sleep Medicine and Sleep Research Society recently released a Consensus Statement regarding the recommended amount of Sleep to promote optimal health in adults. This paper describes the methodology, background literature, voting process, and voting results for the consensus statement. In addition, we address important assumptions and challenges encountered during the consensus process. Finally, we outline future directions that will advance our understanding of Sleep need and place Sleep duration in the broader context of Sleep health.
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recommended amount of Sleep for a healthy adult a joint consensus statement of the american academy of Sleep medicine and Sleep Research society
Journal of Clinical Sleep Medicine, 2015Co-Authors: Nathaniel F Watson, Safwan M Badr, Gregory Belenky, Donald L Bliwise, Orfeu M Buxton, Daniel J Buysse, David F Dinges, James E Gangwisch, Michael A Grandner, Clete A KushidaAbstract:Sleep is essential for optimal health. The American Academy of Sleep Medicine (AASM) and Sleep Research Society (SRS) developed a consensus recommendation for the amount of Sleep needed to promote optimal health in adults, using a modified RAND Appropriateness Method process. The recommendation is summarized here. A manuscript detailing the conference proceedings and evidence supporting the final recommendation statement will be published in Sleep and the Journal of Clinical Sleep Medicine. Citation: Watson NF, Badr MS, Belenky G, Bliwise DL, Buxton OM, Buysse D, Dinges DF, Gangwisch J, Grandner MA, Kushida C, Malhotra RK, Martin JL, Patel SR, Quan SF, Tasali E. Recommended amount of Sleep for a healthy adult: a joint consensus statement of the American Academy of Sleep Medicine and Sleep Research Society. J Clin Sleep Med 2015;11(6):591–592.
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Recommended Amount of Sleep for a Healthy Adult: A Joint Consensus Statement of the American Academy of Sleep Medicine and Sleep Research Society.
Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine, 2015Co-Authors: Nathaniel F Watson, Gregory Belenky, Donald L Bliwise, Orfeu M Buxton, Daniel J Buysse, David F Dinges, James E Gangwisch, Michael A Grandner, M. Safwan Badr, Clete A KushidaAbstract:Sleep is essential for optimal health. The American Academy of Sleep Medicine (AASM) and Sleep Research Society (SRS) developed a consensus recommendation for the amount of Sleep needed to promote optimal health in adults, using a modified RAND Appropriateness Method process. The recommendation is summarized here. A manuscript detailing the conference proceedings and evidence supporting the final recommendation statement will be published in Sleep and the Journal of Clinical Sleep Medicine.
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Recommended Amount of Sleep for a Healthy Adult: A Joint Consensus Statement of the American Academy of Sleep Medicine and Sleep Research Society.
Sleep, 2015Co-Authors: Nathaniel F Watson, Gregory Belenky, Donald L Bliwise, Orfeu M Buxton, Daniel J Buysse, David F Dinges, James E Gangwisch, Michael A Grandner, M. Safwan Badr, Clete A KushidaAbstract:Sleep is essential for optimal health. The American Academy of Sleep Medicine (AASM) and Sleep Research Society (SRS) developed a consensus recommendation for the amount of Sleep needed to promote optimal health in adults, using a modified RAND Appropriateness Method process. The recommendation is summarized here. A manuscript detailing the conference proceedings and evidence supporting the final recommendation statement will be published in Sleep and the Journal of Clinical Sleep Medicine. Citation: Watson NF, Badr MS, Belenky G, Bliwise DL, Buxton OM, Buysse D, Dinges DF, Gangwisch J, Grandner MA, Kushida C, Malhotra RK, Martin JL, Patel SR, Quan SF, Tasali E. Recommended amount of Sleep for a healthy adult: a joint consensus statement of the American Academy of Sleep Medicine and Sleep Research Society. Sleep 2015;38(6):843–844.
Nathaniel F Watson - One of the best experts on this subject based on the ideXlab platform.
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Joint Consensus Statement of the American Academy of Sleep Medicine and Sleep Research Society on the Recommended Amount of Sleep for a Healthy Adult: Methodology and Discussion.
Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine, 2015Co-Authors: Nathaniel F Watson, Gregory Belenky, Donald L Bliwise, Orfeu M Buxton, Daniel J Buysse, David F Dinges, James E Gangwisch, Michael A Grandner, M. Safwan Badr, Clete A KushidaAbstract:The American Academy of Sleep Medicine and Sleep Research Society recently released a Consensus Statement regarding the recommended amount of Sleep to promote optimal health in adults. This paper d...
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Joint consensus statement of the American Academy of Sleep Medicine and Sleep Research Society on the recommended amount of Sleep for a healthy adult: Methodology and discussion
Sleep, 2015Co-Authors: Nathaniel F Watson, Gregory Belenky, Donald L Bliwise, Orfeu M Buxton, Daniel J Buysse, David F Dinges, James E Gangwisch, Michael A Grandner, M. Safwan Badr, Clete A KushidaAbstract:The American Academy of Sleep Medicine and Sleep Research Society recently released a Consensus Statement regarding the recommended amount of Sleep to promote optimal health in adults. This paper describes the methodology, background literature, voting process, and voting results for the consensus statement. In addition, we address important assumptions and challenges encountered during the consensus process. Finally, we outline future directions that will advance our understanding of Sleep need and place Sleep duration in the broader context of Sleep health.
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recommended amount of Sleep for a healthy adult a joint consensus statement of the american academy of Sleep medicine and Sleep Research society
Journal of Clinical Sleep Medicine, 2015Co-Authors: Nathaniel F Watson, Safwan M Badr, Gregory Belenky, Donald L Bliwise, Orfeu M Buxton, Daniel J Buysse, David F Dinges, James E Gangwisch, Michael A Grandner, Clete A KushidaAbstract:Sleep is essential for optimal health. The American Academy of Sleep Medicine (AASM) and Sleep Research Society (SRS) developed a consensus recommendation for the amount of Sleep needed to promote optimal health in adults, using a modified RAND Appropriateness Method process. The recommendation is summarized here. A manuscript detailing the conference proceedings and evidence supporting the final recommendation statement will be published in Sleep and the Journal of Clinical Sleep Medicine. Citation: Watson NF, Badr MS, Belenky G, Bliwise DL, Buxton OM, Buysse D, Dinges DF, Gangwisch J, Grandner MA, Kushida C, Malhotra RK, Martin JL, Patel SR, Quan SF, Tasali E. Recommended amount of Sleep for a healthy adult: a joint consensus statement of the American Academy of Sleep Medicine and Sleep Research Society. J Clin Sleep Med 2015;11(6):591–592.
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Recommended Amount of Sleep for a Healthy Adult: A Joint Consensus Statement of the American Academy of Sleep Medicine and Sleep Research Society.
Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine, 2015Co-Authors: Nathaniel F Watson, Gregory Belenky, Donald L Bliwise, Orfeu M Buxton, Daniel J Buysse, David F Dinges, James E Gangwisch, Michael A Grandner, M. Safwan Badr, Clete A KushidaAbstract:Sleep is essential for optimal health. The American Academy of Sleep Medicine (AASM) and Sleep Research Society (SRS) developed a consensus recommendation for the amount of Sleep needed to promote optimal health in adults, using a modified RAND Appropriateness Method process. The recommendation is summarized here. A manuscript detailing the conference proceedings and evidence supporting the final recommendation statement will be published in Sleep and the Journal of Clinical Sleep Medicine.
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Recommended Amount of Sleep for a Healthy Adult: A Joint Consensus Statement of the American Academy of Sleep Medicine and Sleep Research Society.
Sleep, 2015Co-Authors: Nathaniel F Watson, Gregory Belenky, Donald L Bliwise, Orfeu M Buxton, Daniel J Buysse, David F Dinges, James E Gangwisch, Michael A Grandner, M. Safwan Badr, Clete A KushidaAbstract:Sleep is essential for optimal health. The American Academy of Sleep Medicine (AASM) and Sleep Research Society (SRS) developed a consensus recommendation for the amount of Sleep needed to promote optimal health in adults, using a modified RAND Appropriateness Method process. The recommendation is summarized here. A manuscript detailing the conference proceedings and evidence supporting the final recommendation statement will be published in Sleep and the Journal of Clinical Sleep Medicine. Citation: Watson NF, Badr MS, Belenky G, Bliwise DL, Buxton OM, Buysse D, Dinges DF, Gangwisch J, Grandner MA, Kushida C, Malhotra RK, Martin JL, Patel SR, Quan SF, Tasali E. Recommended amount of Sleep for a healthy adult: a joint consensus statement of the American Academy of Sleep Medicine and Sleep Research Society. Sleep 2015;38(6):843–844.
Susan Redline - One of the best experts on this subject based on the ideXlab platform.
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x search an open access interface for cross cohort exploration of the national Sleep Research resource
BMC Medical Informatics and Decision Making, 2018Co-Authors: Licong Cui, Remo Mueller, Matthew Kim, Susan Redline, Ningzhou Zeng, Emily R Hankosky, Guo-qiang ZhangAbstract:The National Sleep Research Resource (NSRR) is a large-scale, openly shared, data repository of de-identified, highly curated clinical Sleep data from multiple NIH-funded epidemiological studies. Although many data repositories allow users to browse their content, few support fine-grained, cross-cohort query and exploration at study-subject level. We introduce a cross-cohort query and exploration system, called X-search, to enable Researchers to query patient cohort counts across a growing number of completed, NIH-funded studies in NSRR and explore the feasibility or likelihood of reusing the data for Research studies. X-search has been designed as a general framework with two loosely-coupled components: semantically annotated data repository and cross-cohort exploration engine. The semantically annotated data repository is comprised of a canonical data dictionary, data sources with a data dictionary, and mappings between each individual data dictionary and the canonical data dictionary. The cross-cohort exploration engine consists of five modules: query builder, graphical exploration, case-control exploration, query translation, and query execution. The canonical data dictionary serves as the unified metadata to drive the visual exploration interfaces and facilitate query translation through the mappings. X-search is publicly available at https://www.x-search.net/ with nine NSRR datasets consisting of over 26,000 unique subjects. The canonical data dictionary contains over 900 common data elements across the datasets. X-search has received over 1800 cross-cohort queries by users from 16 countries. X-search provides a powerful cross-cohort exploration interface for querying and exploring heterogeneous datasets in the NSRR data repository, so as to enable Researchers to evaluate the feasibility of potential Research studies and generate potential hypotheses using the NSRR data.
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BCB - The National Sleep Research Resource: Towards a Sleep Data Commons
Proceedings of the 2018 ACM International Conference on Bioinformatics Computational Biology and Health Informatics, 2018Co-Authors: Guo-qiang Zhang, Remo Mueller, Matthew Kim, Michael Rueschman, Daniel Mobley, Licong Cui, Shiqiang Tao, Sara Mariani, Susan RedlineAbstract:Objective: The gold standard for diagnosing Sleep disorders is polysomnography, which generates extensive data about biophysical changes occurring during Sleep. We developed the National Sleep Research Resource (NSRR), a comprehensive system for sharing Sleep data. The NSRR embodies elements of a data commons aimed at accelerating Research to address critical questions about the impact of Sleep disorders on important health outcomes. Approach: We used a metadata-guided approach, with a set of common Sleep-specific terms enforcing uniform semantic interpretation of data elements across three main components: (1) annotated datasets; (2) user interfaces for accessing data; and (3) computational tools for the analysis of polysomnography recordings. We incorporated the process for managing dataset-specific data use agreements, evidence of Institutional Review Board review, and the corresponding access control in the NSRR web portal. The metadata-guided approach facilitates structural and semantic interoperability, ultimately leading to enhanced data reusability and scientific rigor. Results: The authors curated and deposited retrospective data from 10 large, NIH-funded Sleep cohort studies, including several from the Trans-Omics for Precision Medicine (TOPMed) program, into the NSRR. The NSRR currently contains data on 26,808 subjects and 31,166 signal files in European Data Format. Launched in April 2014, over 3000 registered users have downloaded over 130 terabytes of data. Conclusions: The NSRR offers a use case and an example for creating a full-fledged data commons. It provides a single point of access to analysis-ready physiological signals from polysomnography obtained from multiple sources, and a wide variety of clinical data to facilitate Sleep Research. The NIH Data Commons (or Commons) is an ambitious vision for a shared virtual space to allow digital objects to be stored and computed upon by the scientific community. The Commons would allow investigators to find, manage, share, use and reuse data, software, metadata and workflows. It imagines an ecosystem that makes digital objects Findable, Accessible, Interoperable and Reusable (FAIR). Four components are considered integral parts of the Commons: a computing resource for accessing and processing of digital objects; a "digital object compliance model" that describes the properties of digital objects that enable them to be FAIR; datasets that adhere to the digital object compliance model; and software and services to facilitate access to and use of data. This paper describes the contributions of NSRR along several aspects of the Commons vision: metadata for Sleep Research digital objects; a collection of annotated Sleep data sets; and interfaces and tools for accessing and analyzing such data. More importantly, the NSRR provides the design of a functional architecture for implementing a Sleep Data Commons. The NSRR also reveals complexities and challenges involved in making clinical Sleep data conform to the FAIR principles. Future directions: Shared resources offered by emerging resources such as cloud instances provide promising platforms for the Data Commons. However, simply expanding storage or adding compute power may not allow us to cope with the rapidly expanding volume and increasing complexity of biomedical data. Concurrent efforts must be spent to address digital object organization challenges. To make our approach future-proof, we need to continue advancing Research in data representation and interfaces for human-data interaction. A possible next phase of NSRR is the creation of a universal self-descriptive sequential data format. The idea is to break large, unstructured, sequential data files into minimal, semantically meaningful, fragments. Such fragments can be indexed, assembled, retrieved, rendered, or repackaged on-the-fly, for multitudes of application scenarios. Data points in such a fragment will be locally embedded with relevant metadata labels, governed by terminology and ontology. Potential benefits of such an approach may include precise levels of data access, increased analysis readiness with on-the-fly data conversion, multi-level data discovery and support for effective web-based visualization of contents in large sequential files.
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the national Sleep Research resource towards a Sleep data commons
International Conference on Bioinformatics, 2018Co-Authors: Guo-qiang Zhang, Remo Mueller, Matthew Kim, Michael Rueschman, Daniel Mobley, Licong Cui, Shiqiang Tao, Sara Mariani, Susan RedlineAbstract:Objective: The gold standard for diagnosing Sleep disorders is polysomnography, which generates extensive data about biophysical changes occurring during Sleep. We developed the National Sleep Research Resource (NSRR), a comprehensive system for sharing Sleep data. The NSRR embodies elements of a data commons aimed at accelerating Research to address critical questions about the impact of Sleep disorders on important health outcomes. Approach: We used a metadata-guided approach, with a set of common Sleep-specific terms enforcing uniform semantic interpretation of data elements across three main components: (1) annotated datasets; (2) user interfaces for accessing data; and (3) computational tools for the analysis of polysomnography recordings. We incorporated the process for managing dataset-specific data use agreements, evidence of Institutional Review Board review, and the corresponding access control in the NSRR web portal. The metadata-guided approach facilitates structural and semantic interoperability, ultimately leading to enhanced data reusability and scientific rigor. Results: The authors curated and deposited retrospective data from 10 large, NIH-funded Sleep cohort studies, including several from the Trans-Omics for Precision Medicine (TOPMed) program, into the NSRR. The NSRR currently contains data on 26,808 subjects and 31,166 signal files in European Data Format. Launched in April 2014, over 3000 registered users have downloaded over 130 terabytes of data. Conclusions: The NSRR offers a use case and an example for creating a full-fledged data commons. It provides a single point of access to analysis-ready physiological signals from polysomnography obtained from multiple sources, and a wide variety of clinical data to facilitate Sleep Research. The NIH Data Commons (or Commons) is an ambitious vision for a shared virtual space to allow digital objects to be stored and computed upon by the scientific community. The Commons would allow investigators to find, manage, share, use and reuse data, software, metadata and workflows. It imagines an ecosystem that makes digital objects Findable, Accessible, Interoperable and Reusable (FAIR). Four components are considered integral parts of the Commons: a computing resource for accessing and processing of digital objects; a "digital object compliance model" that describes the properties of digital objects that enable them to be FAIR; datasets that adhere to the digital object compliance model; and software and services to facilitate access to and use of data. This paper describes the contributions of NSRR along several aspects of the Commons vision: metadata for Sleep Research digital objects; a collection of annotated Sleep data sets; and interfaces and tools for accessing and analyzing such data. More importantly, the NSRR provides the design of a functional architecture for implementing a Sleep Data Commons. The NSRR also reveals complexities and challenges involved in making clinical Sleep data conform to the FAIR principles. Future directions: Shared resources offered by emerging resources such as cloud instances provide promising platforms for the Data Commons. However, simply expanding storage or adding compute power may not allow us to cope with the rapidly expanding volume and increasing complexity of biomedical data. Concurrent efforts must be spent to address digital object organization challenges. To make our approach future-proof, we need to continue advancing Research in data representation and interfaces for human-data interaction. A possible next phase of NSRR is the creation of a universal self-descriptive sequential data format. The idea is to break large, unstructured, sequential data files into minimal, semantically meaningful, fragments. Such fragments can be indexed, assembled, retrieved, rendered, or repackaged on-the-fly, for multitudes of application scenarios. Data points in such a fragment will be locally embedded with relevant metadata labels, governed by terminology and ontology. Potential benefits of such an approach may include precise levels of data access, increased analysis readiness with on-the-fly data conversion, multi-level data discovery and support for effective web-based visualization of contents in large sequential files.
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The National Sleep Research Resource: towards a Sleep data commons
Journal of the American Medical Informatics Association : JAMIA, 2018Co-Authors: Guo-qiang Zhang, Remo Mueller, Matthew Kim, Michael Rueschman, Daniel Mobley, Licong Cui, Shiqiang Tao, Sara Mariani, Susan RedlineAbstract:Objective The gold standard for diagnosing Sleep disorders is polysomnography, which generates extensive data about biophysical changes occurring during Sleep. We developed the National Sleep Research Resource (NSRR), a comprehensive system for sharing Sleep data. The NSRR embodies elements of a data commons aimed at accelerating Research to address critical questions about the impact of Sleep disorders on important health outcomes. Approach We used a metadata-guided approach, with a set of common Sleep-specific terms enforcing uniform semantic interpretation of data elements across three main components: (1) annotated datasets; (2) user interfaces for accessing data; and (3) computational tools for the analysis of polysomnography recordings. We incorporated the process for managing dataset-specific data use agreements, evidence of Institutional Review Board review, and the corresponding access control in the NSRR web portal. The metadata-guided approach facilitates structural and semantic interoperability, ultimately leading to enhanced data reusability and scientific rigor. Results The authors curated and deposited retrospective data from 10 large, NIH-funded Sleep cohort studies, including several from the Trans-Omics for Precision Medicine (TOPMed) program, into the NSRR. The NSRR currently contains data on 26 808 subjects and 31 166 signal files in European Data Format. Launched in April 2014, over 3000 registered users have downloaded over 130 terabytes of data. Conclusions The NSRR offers a use case and an example for creating a full-fledged data commons. It provides a single point of access to analysis-ready physiological signals from polysomnography obtained from multiple sources, and a wide variety of clinical data to facilitate Sleep Research.
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Strategic Opportunities in Sleep and Circadian Research: Report of the Joint Task Force of the Sleep Research Society and American Academy of Sleep Medicine
Sleep, 2014Co-Authors: Phyllis C. Zee, Susan Redline, Clete A Kushida, M. Safwan Badr, Janet Mullington, Allan I. Pack, Sairam Parthasarathy, Ronald Szymusiak, James K. Walsh, Nathaniel F WatsonAbstract:EXECUTIVE SUMMARY Sleep and circadian timing are fundamental biological imperatives in animals and humans, throughout the lifespan. These biological systems can be challenged by pathology, individual choices, and social/societal pressures, often resulting in Sleep loss or circadian disruption (i.e., “Sleep deficiency”), and ultimately adverse health and safety outcomes. Advances in the scientific knowledge generated during the last decade indicate the transformative potential of Sleep and circadian health for improving the health of the American people, including the development of novel, personalized, preventative and therapeutic strategies for multiple chronic diseases. The American Academy of Sleep Medicine (AASM) and the Sleep Research Society (SRS) created a Task Force with a mandate to engage the Sleep and circadian scientific community, the National Institutes of Health (NIH) and other key stakeholders to help catalyze the implementation of the most time-sensitive Research priorities identified in the 2011 NIH Sleep Disorders Research Plan. Given the mounting evidence of the importance of Sleep health to overall physical health, behavioral health and safety, together with the rapid advances in basic Sleep and circadian science, we need to seize on this opportunity to accelerate translational and clinical Research in Sleep and circadian rhythms. This white paper represents the proceedings and consensus development at the Joint Task Force on Sleep and Circadian Research Conference held in 2013 in Bethesda, MD. It is directed toward all invested in Sleep and circadian Research for their consideration, including Researchers, educators, patients, professional societies, industry partners, funding-decision and policy makers. This documentation is timely and comes on the heels of a compelling call for an international effort in this area. 1 The four major Research goals and specific recommen dations for each of these goals were identified. These recom mendations can be adapted and directed to prioritize Research in various populations and clinical settings.
Michael G. Morrical - One of the best experts on this subject based on the ideXlab platform.
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0763 automated pipeline for spectral analysis of eeg data the national Sleep Research resource tool
Sleep, 2017Co-Authors: Sara Mariani, Michael G. Morrical, Leila Tarokh, Ina Djonlagic, Brian E Cade, K Yaffe, Katie L Stone, Kenneth A Loparo, Shaun Purcell, Daniel AeschbachAbstract:Introduction: The National Sleep Research Resource (NSRR, www.Sleepdata.org) features thousands of polysomnograms (PSGs) that can be analyzed for further understanding how variations in physiological signals associate with health outcomes. Quantitative EEG analysis may help characterize physiological variation. However, analysis of large datasets collected in uncontrolled settings requires a robust pipeline including artifact detectors. To promote community-wide use of PSG data, we developed an open-source, automated pipeline for spectral analysis of Sleep EEGs and tested the level of agreement with traditional analysis. Methods: We used data from the C3-A2 EEG lead in a sample of PSGs from 161 women participating in the Study of Osteoporotic Fractures. The traditional approach used manual artifact removal on 4-s basis and application of commercial spectral analysis software. Automated analysis included spectral power-based artifact detection on 30-s basis and generation of summary figures for adjudication. We compared automatic and manual artifact detection epoch-by-epoch and then compared the average EEG spectral power density in six frequency bands obtained with the two approaches using correlation analysis, Bland-Altman plots and Wilcoxon test. Results: The automated artifact detection algorithm had high specificity (96.8 to 99.4% in NREM, 96.9 to 99.1% in REM depending on the criterion for comparing 4-s with 30-s epochs) but lower sensitivity (26.7 to 38.1% in NREM, 9.1 to 27.4% in REM). However, we found no clinically or statistically significant differences in power density values, and results were highly correlated (Spearman’s r>0.99). Large artifacts (total power >99th percentile) were removed with sensitivity up to 90.9% in NREM, 87.7% in REM, specificity 96.6% and 96.9%. Conclusion: The automated pipeline generated similar results to those obtained with standard approach, while reducing analysis time 100-fold. This Matlab toolset, publicly available on the NSRR website, can be used to analyze thousands of recordings, allowing for its application in genetics and epidemiological Research.
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Scaling Up Scientific Discovery in Sleep Medicine: The National Sleep Research Resource.
Sleep, 2016Co-Authors: Dennis A. Dean, Ary L. Goldberger, Remo Mueller, Matthew Kim, Michael Rueschman, Daniel Mobley, Satya S. Sahoo, Catherine P. Jayapandian, Licong Cui, Michael G. MorricalAbstract:Professional Sleep societies have identified a need for strategic Research in multiple areas that may benefit from access to and aggregation of large, multidimensional datasets. Technological advances provide opportunities to extract and analyze physiological signals and other biomedical information from datasets of unprecedented size, heterogeneity, and complexity. The National Institutes of Health has implemented a Big Data to Knowledge (BD2K) initiative that aims to develop and disseminate state of the art big data access tools and analytical methods. The National Sleep Research Resource (NSRR) is a new National Heart, Lung, and Blood Institute resource designed to provide big data resources to the Sleep Research community. The NSRR is a web-based data portal that aggregates, harmonizes, and organizes Sleep and clinical data from thousands of individuals studied as part of cohort studies or clinical trials and provides the user a suite of tools to facilitate data exploration and data visualization. Each deidentified study record minimally includes the summary results of an overnight Sleep study; annotation files with scored events; the raw physiological signals from the Sleep record; and available clinical and physiological data. NSRR is designed to be interoperable with other public data resources such as the Biologic Specimen and Data Repository Information Coordinating Center Demographics (BioLINCC) data and analyzed with methods provided by the Research Resource for Complex Physiological Signals (PhysioNet). This article reviews the key objectives, challenges and operational solutions to addressing big data opportunities for Sleep Research in the context of the national Sleep Research agenda. It provides information to facilitate further interactions of the user community with NSRR, a community resource.
Thomas Fenzl - One of the best experts on this subject based on the ideXlab platform.
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Sleep scoring made easy-Semi-automated Sleep analysis software and manual rescoring tools for basic Sleep Research in mice.
MethodsX, 2015Co-Authors: Matthias Kreuzer, S. Polta, J. Gapp, C. Schuler, Eberhard Kochs, Thomas FenzlAbstract:Studying Sleep behavior in animal models demands clear separation of vigilance states. Pure manual scoring is time-consuming and commercial scoring software is costly. We present a LabVIEW-based, semi-automated scoring routine using recorded EEG and EMG signals. This scoring routine is • designed to reliably assign the vigilance/Sleep states wakefulness (WAKE), non-rapid eye movement Sleep (NREMS) and rapid eye movement Sleep (REMS) to defined EEG/EMG episodes. • straightforward to use even for beginners in the field of Sleep Research. • freely available upon request. Chronic recordings from mice were used to design and evaluate the scoring routine consisting of an artifact-removal, a scoring- and a rescoring routine. The scoring routine processes EMG and different EEG frequency bands. Amplitude-based thresholds for EEG and EMG parameters trigger a decision tree assigning each EEG episode to a defined vigilance/Sleep state automatically. Using the rescoring routine individual episodes or particular state transitions can be re-evaluated manually. High agreements between auto-scored and manual Sleep scoring could be shown for experienced scorers and for beginners quickly and reliably. With small modifications to the software, it can be easily adapted for Sleep analysis in other animal models.
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Fully automated Sleep deprivation in mice as a tool in Sleep Research.
Journal of neuroscience methods, 2007Co-Authors: Thomas Fenzl, Christoph P.n. Romanowski, Cornelia Flachskamm, Karlheinz Honsberg, Erwin Boll, Arnold Hoehne, Mayumi KimuraAbstract:Although total Sleep deprivation is frequently used in Sleep Research, the techniques used such as gentle handling are labor consuming and not standardized (and boring). In order to minimize these limitations, we developed a fully automated setup, which can be used for total Sleep deprivation. A shortfall of individually adjustable thresholds of electromyogram (EMG) signals from Sleep deprived animals was used online to switch running wheels incorporated into the home cages. Randomized direction of rotations, adaptable rotational speed and automatic deactivation of the running wheels during quiet waking of the animals provided robust and standardized Sleep deprivation without increased stress, when compared to gentle handling. The setup can easily be introduced to a variety of home cages and is individually adaptable to each animal to be Sleep deprived.
