SleepOMICS: How Big Data Can Revolutionize Sleep Science
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[1] W. Dement,et al. Introduction , 1998, Thorax.
[2] Mario Tudor,et al. [Hans Berger (1873-1941)--the history of electroencephalography]. , 2005, Acta medica Croatica : casopis Hravatske akademije medicinskih znanosti.
[3] Emmanuel Mignot,et al. History of the development of sleep medicine in the United States. , 2005, Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine.
[4] J. Hobson. Sleep is of the brain, by the brain and for the brain , 2005, Nature.
[5] J. Owens,et al. Classification and epidemiology of childhood sleep disorders. , 2008, Primary care.
[6] Marimuthu Palaniswami,et al. Support Vector Machines for Automated Recognition of Obstructive Sleep Apnea Syndrome From ECG Recordings , 2009, IEEE Transactions on Information Technology in Biomedicine.
[7] G. Eysenbach. Infodemiology and Infoveillance: Framework for an Emerging Set of Public Health Informatics Methods to Analyze Search, Communication and Publication Behavior on the Internet , 2009, Journal of medical Internet research.
[8] A. Sehgal,et al. Genetics of Sleep and Sleep Disorders , 2011, Cell.
[9] Chimezie Ogbuji,et al. MiDas: automatic extraction of a common domain of discourse in sleep medicine for multi-center data integration. , 2011, AMIA ... Annual Symposium proceedings. AMIA Symposium.
[10] Terrence D. Hill,et al. Religious Doubts and Sleep Quality: Findings from a Nationwide Study of Presbyterians #090709revised , 2011, Review of religious research.
[11] M. Pusz,et al. How Good Is Google? The Quality of Otolaryngology Information on the Internet , 2011 .
[12] A. Pack,et al. Sleep disorders, public health, and public safety. , 2011, JAMA.
[13] L. Aldabal,et al. Metabolic, Endocrine, and Immune Consequences of Sleep Deprivation , 2011, The open respiratory medicine journal.
[14] Ngianga-Bakwin Kandala,et al. Sleep problems: an emerging global epidemic? Findings from the INDEPTH WHO-SAGE study among more than 40,000 older adults from 8 countries across Africa and Asia. , 2012, Sleep.
[15] Enzo Tagliazucchi,et al. Automatic sleep staging using fMRI functional connectivity data , 2012, NeuroImage.
[16] How Good Is Google? , 2011, Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery.
[17] D. Ingram,et al. Seasonal trends in restless legs symptomatology: evidence from Internet search query data. , 2013, Sleep medicine.
[18] Satish T. S. Bukkapatnam,et al. Wireless Wearable Multisensory Suite and Real-Time Prediction of Obstructive Sleep Apnea Episodes , 2013, IEEE Journal of Translational Engineering in Health and Medicine.
[19] William D. Marslen-Wilson,et al. The Cambridge Centre for Ageing and Neuroscience (Cam-CAN) study protocol: a cross-sectional, lifespan, multidisciplinary examination of healthy cognitive ageing , 2014, BMC Neurology.
[20] John B. Hogenesch,et al. Machine Learning Helps Identify CHRONO as a Circadian Clock Component , 2014, PLoS biology.
[21] Richard A. Miller,et al. The COMET Sleep Research Platform , 2014, EGEMS.
[22] D. Ingram,et al. Seasonal trends in sleep-disordered breathing: evidence from Internet search engine query data , 2015, Sleep and Breathing.
[23] M. Carskadon,et al. A Longitudinal Assessment of Sleep Timing, Circadian Phase, and Phase Angle of Entrainment across Human Adolescence , 2014, PloS one.
[24] G. Jean-Louis,et al. Racial/ethnic disparities in sleep health and health care: importance of the sociocultural context. , 2015, Sleep health.
[25] Esther Rodríguez-Villegas,et al. An open-source toolbox for standardized use of PhysioNet Sleep EDF Expanded Database , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).
[26] Misha Pavel,et al. Behavioral Informatics and Computational Modeling in Support of Proactive Health Management and Care , 2015, IEEE Transactions on Biomedical Engineering.
[27] Laurent Hébert-Dufresne,et al. Enhancing disease surveillance with novel data streams: challenges and opportunities , 2015, EPJ Data Science.
[28] M. Breakspear,et al. The connectomics of brain disorders , 2015, Nature Reviews Neuroscience.
[29] Akane Sano,et al. Recognizing academic performance, sleep quality, stress level, and mental health using personality traits, wearable sensors and mobile phones , 2015, 2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks (BSN).
[30] Kayvan Najarian,et al. Big Data Analytics in Healthcare , 2015, BioMed research international.
[31] Fernando Martín-Sánchez,et al. The use of self-quantification systems for personal health information: big data management activities and prospects , 2015, Health Information Science and Systems.
[32] G. Poland,et al. Adversomics: a new paradigm for vaccine safety and design , 2015, Expert review of vaccines.
[33] H. Landolt,et al. Sleep Pharmacogenetics: Personalized Sleep-Wake Therapy. , 2016, Annual review of pharmacology and toxicology.
[34] H. Schiöth,et al. Epigenomics of Total Acute Sleep Deprivation in Relation to Genome-Wide DNA Methylation Profiles and RNA Expression , 2016, Omics : a journal of integrative biology.
[35] Lars T. Westlye,et al. The brain functional connectome is robustly altered by lack of sleep , 2016, NeuroImage.
[36] P. Lanteri,et al. Environmental Research and Public Health Co-morbidity, Mortality, Quality of Life and the Healthcare/welfare/social Costs of Disordered Sleep: a Rapid Review , 2022 .
[37] Nicola Luigi Bragazzi,et al. Leveraging Big Data for Exploring Occupational Diseases-Related Interest at the Level of Scientific Community, Media Coverage and Novel Data Streams: The Example of Silicosis as a Pilot Study , 2016, PloS one.
[38] S. Parthasarathy,et al. Big-Data or Slim-Data: Predictive Analytics Will Rule with World. , 2016, Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine.
[39] Catherine P. Jayapandian,et al. Scaling Up Scientific Discovery in Sleep Medicine: The National Sleep Research Resource. , 2016, Sleep.
[40] Rui Wang,et al. Using Smartphones to Collect Behavioral Data in Psychological Science , 2016, Perspectives on psychological science : a journal of the Association for Psychological Science.
[41] S. Redline,et al. The Role of Big Data in the Management of Sleep-Disordered Breathing. , 2016, Sleep medicine clinics.
[42] Trends of Public Interest in Sleep Disorders: Looking by Internet Searching Volume , 2017 .
[43] Meredith A. Shafto,et al. How are age-related differences in sleep quality associated with health outcomes? An epidemiological investigation in a UK cohort of 2406 adults , 2016, BMJ Open.
[44] Munmun De Choudhury,et al. Computational Approaches Toward Integrating Quantified Self Sensing and Social Media , 2017, CSCW.
[45] Haya S Alsubie,et al. Obstructive Sleep Apnoea: Children are not little Adults. , 2017, Paediatric respiratory reviews.
[46] M. Westover,et al. Big data in sleep medicine: prospects and pitfalls in phenotyping , 2017, Nature and science of sleep.
[47] W. Sannita,et al. Poor sleeping has underrepresented medical, healthcare, and social costs? , 2017, European journal of internal medicine.
[48] S. Abbott,et al. Orthosomnia: Are Some Patients Taking the Quantified Self Too Far? , 2017, Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine.
[49] N. Goel. Neurobehavioral Effects and Biomarkers of Sleep Loss in Healthy Adults , 2017, Current Neurology and Neuroscience Reports.
[50] R. Furrer,et al. The quantified self during travel: mapping health in a prospective cohort of travellers. , 2017, Journal of travel medicine.
[51] S. Garbarino,et al. Obstructive sleep apnea (OSA): healthcare and social costs. , 2017, La Medicina del lavoro.
[52] S. Pandi-Perumal,et al. The effect of intermittent fasting during Ramadan on sleep, sleepiness, cognitive function, and circadian rhythm , 2017, Sleep and Breathing.
[53] V. Chattu,et al. Racial/Ethnic and Social Inequities in Sleep Medicine: The Tip of the Iceberg? , 2017, Journal of the National Medical Association.
[54] O. Tzischinsky,et al. Comparative study shows differences in screen exposure, sleep patterns and sleep disturbances between Jewish and Muslim children in Israel , 2017, Acta paediatrica.
[55] Ming Chen,et al. Big Data Analytics in Medicine and Healthcare , 2018, J. Integr. Bioinform..
[56] P. Zee,et al. Circadian disruption and human health: A bidirectional relationship , 2020, The European journal of neuroscience.
[57] Till Roenneberg,et al. Dynamics and Ultradian Structure of Human Sleep in Real Life , 2018, Current Biology.
[58] Shawn Dolley,et al. Big Data’s Role in Precision Public Health , 2018, Front. Public Health.
[59] E. Rolls,et al. Functional Connectivities in the Brain That Mediate the Association Between Depressive Problems and Sleep Quality , 2018, JAMA psychiatry.
[60] J. Ficker,et al. Predictors of positive airway pressure therapy termination in the first year: analysis of big data from a German homecare provider , 2018, BMC Pulmonary Medicine.
[61] Elizabeth A. McDevitt,et al. Quantifying sleep architecture dynamics and individual differences using big data and Bayesian networks , 2018, PloS one.
[62] Terrence D. Hill,et al. Religious involvement as a social determinant of sleep: an initial review and conceptual model. , 2018, Sleep health.
[63] Vijay Kumar Chattu,et al. The Global Problem of Insufficient Sleep and Its Serious Public Health Implications , 2018, Healthcare.
[64] C. Lindgren,et al. GWAS identifies 14 loci for device-measured physical activity and sleep duration , 2018, Nature Communications.
[65] P. Bourke,et al. Facebook use and sleep quality: Light interacts with socially induced alertness , 2018, British journal of psychology.
[66] Yunfan Wu,et al. Abnormal Topology of the Structural Connectome in the Limbic Cortico-Basal-Ganglia Circuit and Default-Mode Network Among Primary Insomnia Patients , 2018, Front. Neurosci..
[67] M. Khoury,et al. HLBS-PopOmics: an online knowledge base to accelerate dissemination and implementation of research advances in population genomics to reduce the burden of heart, lung, blood, and sleep disorders , 2018, Genetics in Medicine.
[68] Boshra Hatef,et al. Performance comparison of machine learning techniques in sleep scoring based on wavelet features and neighboring component analysis , 2018, PeerJ.
[69] F. Bilotta,et al. Artificial neural networks can be effectively used to model changes of intracranial pressure (ICP) during spinal surgery using different non invasive ICP surrogate estimators. , 2018, Journal of neurosurgical sciences.
[70] P. Lanteri,et al. Obstructive Sleep Apnea With or Without Excessive Daytime Sleepiness: Clinical and Experimental Data-Driven Phenotyping , 2018, Front. Neurol..
[71] M. Behzadifar,et al. Vaccines Meet Big Data: State-of-the-Art and Future Prospects. From the Classical 3Is (“Isolate–Inactivate–Inject”) Vaccinology 1.0 to Vaccinology 3.0, Vaccinomics, and Beyond: A Historical Overview , 2018, Front. Public Health.
[72] Xin Zhang,et al. Sleep Stage Classification Based on Multi-level Feature Learning and Recurrent Neural Networks via Wearable Device , 2017, Comput. Biol. Medicine.
[73] M. Simmaco,et al. Genetics of Obstructive Sleep Apnea: Vitamin D Receptor Gene Variation Affects Both Vitamin D Serum Concentration and Disease Susceptibility. , 2019, Omics : a journal of integrative biology.