The future of sleep health: a data-driven revolution in sleep science and medicine

[1]  Siân Steans Deprived , 2022, Journal of Class & Culture.

[2]  Danilo Mandic,et al.  Hearables: Automatic Overnight Sleep Monitoring With Standardized In-Ear EEG Sensor , 2020, IEEE Transactions on Biomedical Engineering.

[3]  Ronald M. Aarts,et al.  Sleep stage classification from heart-rate variability using long short-term memory neural networks , 2019, Scientific Reports.

[4]  Luis Gustavo Nonato,et al.  Multidimensional Projection for Visual Analytics: Linking Techniques with Distortions, Tasks, and Layout Enrichment , 2019, IEEE Transactions on Visualization and Computer Graphics.

[5]  G. Breen,et al.  Genetic predisposition to advanced biological ageing increases risk for childhood-onset recurrent major depressive disorder in a large UK sample , 2017, Journal of affective disorders.

[6]  Luis Fernandez-Luque,et al.  Benchmark on a large cohort for sleep-wake classification with machine learning techniques , 2019, npj Digital Medicine.

[7]  S. Ancoli-Israel,et al.  Sleep disturbance and cancer-related fatigue symptom cluster in breast cancer patients undergoing chemotherapy , 2019, Supportive Care in Cancer.

[8]  B. Mussa,et al.  Personalized intervention to improve stress and sleep patterns for glycemic control and weight management in obese Emirati patients with type 2 diabetes: a randomized controlled clinical trial , 2019, Diabetes, metabolic syndrome and obesity : targets and therapy.

[9]  Yi Wang,et al.  Ensemble learning algorithm based on multi-parameters for sleep staging , 2019, Medical & Biological Engineering & Computing.

[10]  Adam B. Cohen,et al.  Digital health: a path to validation , 2019, npj Digital Medicine.

[11]  C. Fung,et al.  SleepDB: A Clinical and Administrative Database Developed to Improve the Diagnosis, Management and Longitudinal Tracking of Sleep Disorders , 2019, A34. SCREENING, DIAGNOSIS, AND TREATMENT IN SLEEP DISORDERS.

[12]  Tarja Saaresranta,et al.  Sleep Parameter Assessment Accuracy of a Consumer Home Sleep Monitoring Ballistocardiograph Beddit Sleep Tracker: A Validation Study. , 2019, Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine.

[13]  Sean Khozin,et al.  Developing and adopting safe and effective digital biomarkers to improve patient outcomes , 2019, npj Digital Medicine.

[14]  S. Kesler,et al.  Secondary Outcomes of a Behavioral Sleep Intervention: A Randomized Clinical Trial , 2019, Health psychology : official journal of the Division of Health Psychology, American Psychological Association.

[15]  Tapani Ristaniemi,et al.  Multi-modality of polysomnography signals' fusion for automatic sleep scoring , 2019, Biomed. Signal Process. Control..

[16]  Edita Fino,et al.  Monitoring healthy and disturbed sleep through smartphone applications: a review of experimental evidence , 2019, Sleep and Breathing.

[17]  S. Steinhubl,et al.  Digitising the way to better sleep health , 2019, The Lancet.

[18]  Brian Caulfield,et al.  Not all sensors are created equal: a framework for evaluating human performance measurement technologies , 2019, npj Digital Medicine.

[19]  N. H. Rod,et al.  Sleep Duration and Sleep Disturbances as Predictors of Healthy and Chronic Disease–Free Life Expectancy Between Ages 50 and 75: A Pooled Analysis of Three Cohorts , 2019, The journals of gerontology. Series A, Biological sciences and medical sciences.

[20]  S. Lawler,et al.  The effectiveness of mHealth for self-management in improving pain, psychological distress, fatigue, and sleep in cancer survivors: a systematic review , 2019, Journal of Cancer Survivorship.

[21]  Hans-Jochen Heinze,et al.  Systematic comparison between a wireless EEG system with dry electrodes and a wired EEG system with wet electrodes , 2019, NeuroImage.

[22]  Eric J Topol,et al.  High-performance medicine: the convergence of human and artificial intelligence , 2019, Nature Medicine.

[23]  N. Bragazzi,et al.  SleepOMICS: How Big Data Can Revolutionize Sleep Science , 2019, International journal of environmental research and public health.

[24]  C. Poulter Why We Sleep: The New Science of Sleep and Dreams , 2019, Occupational Medicine.

[25]  Reto Huber,et al.  Capturing sleep–wake cycles by using day-to-day smartphone touchscreen interactions , 2018, bioRxiv.

[26]  Haoqi Sun,et al.  Expert-level sleep scoring with deep neural networks , 2018, J. Am. Medical Informatics Assoc..

[27]  Shafiq R. Joty,et al.  Adversarial Unsupervised Representation Learning for Activity Time-Series , 2018, AAAI.

[28]  Stefan Debener,et al.  Machine‐learning‐derived sleep–wake staging from around‐the‐ear electroencephalogram outperforms manual scoring and actigraphy , 2018, Journal of sleep research.

[29]  J. Buhmann,et al.  Automatic Human Sleep Stage Scoring Using Deep Neural Networks , 2018, Front. Neurosci..

[30]  Juan Miguel García-Gómez,et al.  Kinematics of Big Biomedical Data to characterize temporal variability and seasonality of data repositories: Functional Data Analysis of data temporal evolution over non-parametric statistical manifolds , 2018, Int. J. Medical Informatics.

[31]  George Demiris,et al.  Smartphone Applications to Support Sleep Self-Management: Review and Evaluation. , 2018, Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine.

[32]  M. Desai,et al.  Rationale and design of a large-scale, app-based study to identify cardiac arrhythmias using a smartwatch: The Apple Heart Study , 2018, American heart journal.

[33]  Krishna P. Kadiyala,et al.  All One Needs to Know about Fog Computing and Related Edge Computing Paradigms: A Complete Survey , 2018, J. Syst. Archit..

[34]  Alexandros T. Tzallas,et al.  EEG-Based Automatic Sleep Stage Classification , 2018, Biomedical Journal of Scientific & Technical Research.

[35]  Yi-Zeng Hsieh,et al.  Internet of Things Pillow Detecting Sleeping Quality , 2018, 2018 1st International Cognitive Cities Conference (IC3).

[36]  Shafiq R. Joty,et al.  A Structured Learning Approach with Neural Conditional Random Fields for Sleep Staging , 2018, 2018 IEEE International Conference on Big Data (Big Data).

[37]  Jennifer L Martin,et al.  Use of Actigraphy for the Evaluation of Sleep Disorders and Circadian Rhythm Sleep-Wake Disorders: An American Academy of Sleep Medicine Clinical Practice Guideline. , 2018, Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine.

[38]  S. Stranges,et al.  Sleep duration and multimorbidity in Luxembourg. Results from the European Health Examination Survey , 2018, Revue d'Épidémiologie et de Santé Publique.

[39]  Carlos E. Palau,et al.  A Smart System for Sleep Monitoring by Integrating IoT With Big Data Analytics , 2018, IEEE Access.

[40]  Emily A. Abel,et al.  Pediatric Videosomnography: Can Signal/Video Processing Distinguish Sleep and Wake States? , 2018, Front. Pediatr..

[41]  Susan Redline,et al.  Insomnia with objective short sleep duration and risk of incident cardiovascular disease and all-cause mortality: Sleep Heart Health Study , 2018, Sleep.

[42]  Saif S. Khairat,et al.  The Impact of Visualization Dashboards on Quality of Care and Clinician Satisfaction: Integrative Literature Review , 2018, JMIR human factors.

[43]  Zeljko Zilic,et al.  A Continuous Respiratory Monitoring System Using Ultrasound Piezo Transducer , 2018, 2018 IEEE International Symposium on Circuits and Systems (ISCAS).

[44]  Matthew Willetts,et al.  Statistical machine learning of sleep and physical activity phenotypes from sensor data in 96,220 UK Biobank participants , 2017, Scientific Reports.

[45]  Ilene M Rosen,et al.  Consumer Sleep Technology: An American Academy of Sleep Medicine Position Statement. , 2018, Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine.

[46]  Ronald M. Aarts,et al.  A comparison of probabilistic classifiers for sleep stage classification , 2018, Physiological measurement.

[47]  A. Forner-Cordero,et al.  Comparison of sleep quality assessed by actigraphy and questionnaires to healthy subjects , 2018, Sleep Science.

[48]  Wessel Kraaij,et al.  Evaluating an mHealth App for Health and Well-Being at Work: Mixed-Method Qualitative Study , 2018, JMIR mHealth and uHealth.

[49]  Gunnar Hartvigsen,et al.  Using Fitness Trackers and Smartwatches to Measure Physical Activity in Research: Analysis of Consumer Wrist-Worn Wearables , 2018, Journal of medical Internet research.

[50]  Qi Huang,et al.  Hidden Markov models for monitoring circadian rhythmicity in telemetric activity data , 2018, Journal of The Royal Society Interface.

[51]  Sabine Van Huffel,et al.  Automated EEG sleep staging in the term-age baby using a generative modelling approach , 2018, Journal of neural engineering.

[52]  S. Pandi‑Perumal Why We Sleep: The New Science of Sleep and Dreams by Matthew Walker, Ph.D. , 2018, Sleep and Vigilance.

[53]  David Taniar,et al.  Sensor data management in the cloud: Data storage, data ingestion, and data retrieval , 2018, Concurr. Comput. Pract. Exp..

[54]  Kaare B. Mikkelsen,et al.  Personalizing deep learning models for automatic sleep staging , 2018, 1801.02645.

[55]  Saul Neves de Jesus,et al.  Depression and quality of life in older adults: Mediation effect of sleep quality , 2017, International journal of clinical and health psychology : IJCHP.

[56]  Yu Zhang,et al.  A Comparison Study on Multidomain EEG Features for Sleep Stage Classification , 2017, Comput. Intell. Neurosci..

[57]  Xin Zhang,et al.  Sleep Stage Classification Based on Multi-level Feature Learning and Recurrent Neural Networks via Wearable Device , 2017, Comput. Biol. Medicine.

[58]  Zhibo Pang,et al.  Smart Homes for Elderly Healthcare—Recent Advances and Research Challenges , 2017, Sensors.

[59]  Dina Katabi,et al.  Zero-Effort In-Home Sleep and Insomnia Monitoring using Radio Signals , 2017, Proc. ACM Interact. Mob. Wearable Ubiquitous Technol..

[60]  Pengjiang Qian,et al.  Seizure Classification From EEG Signals Using Transfer Learning, Semi-Supervised Learning and TSK Fuzzy System , 2017, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[61]  D. Grigsby-Toussaint,et al.  Mobile Phone Interventions for Sleep Disorders and Sleep Quality: Systematic Review , 2017, JMIR mHealth and uHealth.

[62]  Bessam Abdulrazak,et al.  Novel Unobtrusive Approach for Sleep Monitoring Using Fiber Optics in an Ambient Assisted Living Platform , 2017, ICOST.

[63]  Morwenna Kirwan,et al.  Activity Trackers Implement Different Behavior Change Techniques for Activity, Sleep, and Sedentary Behaviors , 2017, Interactive journal of medical research.

[64]  Tommi S. Jaakkola,et al.  Learning Sleep Stages from Radio Signals: A Conditional Adversarial Architecture , 2017, ICML.

[65]  Jimeng Sun,et al.  SLEEPNET: Automated Sleep Staging System via Deep Learning , 2017, ArXiv.

[66]  Zhongxing Zhang,et al.  Recognizing Sleep Stages with Wearable Sensors in Everyday Settings , 2017, ICT4AgeingWell.

[67]  Charlene Gamaldo,et al.  The Accuracy, Night-to-Night Variability, and Stability of Frontopolar Sleep Electroencephalography Biomarkers. , 2017, Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine.

[68]  T. Penzel,et al.  The Need for a Reliable Sleep EEG Biomarker. , 2017, Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine.

[69]  Akane Sano,et al.  Irregular sleep/wake patterns are associated with poorer academic performance and delayed circadian and sleep/wake timing , 2017, Scientific Reports.

[70]  L. Hawk,et al.  Sleep Disturbance During Smoking Cessation: Withdrawal or Side Effect of Treatment? , 2017, Journal of smoking cessation.

[71]  Bing Liu,et al.  Lifelong machine learning: a paradigm for continuous learning , 2017, Frontiers of Computer Science.

[72]  Louis-Philippe Morency,et al.  Multimodal Machine Learning: A Survey and Taxonomy , 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[73]  Shwetak N. Patel,et al.  Making Sense of Sleep Sensors: How Sleep Sensing Technologies Support and Undermine Sleep Health , 2017, CHI.

[74]  M. Thorpy,et al.  International classification of sleep disorders , 2017 .

[75]  Wei Zhang,et al.  Cross-Subject EEG Feature Selection for Emotion Recognition Using Transfer Recursive Feature Elimination , 2017, Front. Neurorobot..

[76]  Z. Shinar,et al.  Validation of Contact-Free Sleep Monitoring Device with Comparison to Polysomnography. , 2017, Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine.

[77]  D. Grigsby-Toussaint,et al.  Sleep apps and behavioral constructs: A content analysis , 2017, Preventive medicine reports.

[78]  Rosalind W. Picard,et al.  Multimodal ambulatory sleep detection , 2017, 2017 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI).

[79]  M. Robles,et al.  Stability metrics for multi-source biomedical data based on simplicial projections from probability distribution distances , 2017, Statistical methods in medical research.

[80]  Andrew Krystal,et al.  National Sleep Foundation's sleep quality recommendations: first report☆ , 2017, Sleep health.

[81]  Sana Tmar-Ben Hamida,et al.  Deep Learning and Insomnia: Assisting Clinicians With Their Diagnosis , 2017, IEEE Journal of Biomedical and Health Informatics.

[82]  D. Mandic,et al.  A wearable in-ear encephalography sensor for monitoring sleep: preliminary observations from nap studies , 2016 .

[83]  Esa Mervaala,et al.  Assessment of the suitability of using a forehead EEG electrode set and chin EMG electrodes for sleep staging in polysomnography , 2016, Journal of sleep research.

[84]  Jeannie-Marie S. Leoutsakos,et al.  Validation of a Wireless, Self-Application, Ambulatory Electroencephalographic Sleep Monitoring Device in Healthy Volunteers. , 2016, Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine.

[85]  Bernd Bickel,et al.  FlexMolds , 2016, ACM Trans. Graph..

[86]  Zilu Liang,et al.  SleepExplorer: a visualization tool to make sense of correlations between personal sleep data and contextual factors , 2016, Personal and Ubiquitous Computing.

[87]  R. Martins,et al.  Exploring the bi-directional relationship between sleep and beta-amyloid , 2016, Current opinion in psychiatry.

[88]  Peter Richtárik,et al.  Federated Learning: Strategies for Improving Communication Efficiency , 2016, ArXiv.

[89]  Yike Guo,et al.  Automatic Sleep Stage Scoring with Single-Channel EEG Using Convolutional Neural Networks , 2016, ArXiv.

[90]  Ryan S McGinnis,et al.  Monitoring gait in multiple sclerosis with novel wearable motion sensors , 2016, PloS one.

[91]  D. Spiegel,et al.  Longitudinal Association of Poor Sleep Quality With Chemotherapy-Induced Nausea and Vomiting in Patients With Breast Cancer , 2016, Psychosomatic medicine.

[92]  M. St-Onge,et al.  Effects of Diet on Sleep Quality. , 2016, Advances in nutrition.

[93]  R. Chervin,et al.  Sleep and Cognitive Function in Multiple Sclerosis. , 2016, Sleep.

[94]  Alexander Tataraidze,et al.  Bioradiolocation-based sleep stage classification , 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[95]  Eric J. Topol,et al.  Stop the privatization of health data , 2016, Nature.

[96]  Dan Zhang,et al.  Novel semi-dry electrodes for brain–computer interface applications , 2016, Journal of neural engineering.

[97]  Tao Zhang,et al.  Fog and IoT: An Overview of Research Opportunities , 2016, IEEE Internet of Things Journal.

[98]  Catherine P. Jayapandian,et al.  Scaling Up Scientific Discovery in Sleep Medicine: The National Sleep Research Resource. , 2016, Sleep.

[99]  Yunyoung Nam,et al.  Sleep Monitoring Based on a Tri-Axial Accelerometer and a Pressure Sensor , 2016, Sensors.

[100]  Pascal B. Pfiffner,et al.  C3-PRO: Connecting ResearchKit to the Health System Using i2b2 and FHIR , 2016, PloS one.

[101]  Hirozumi Yamaguchi,et al.  Survey of Real-time Processing Technologies of IoT Data Streams , 2016, J. Inf. Process..

[102]  M. B. Gillespie,et al.  Overview of smartphone applications for sleep analysis , 2016, World journal of otorhinolaryngology - head and neck surgery.

[103]  Ivo D. Dinov,et al.  Methodological challenges and analytic opportunities for modeling and interpreting Big Healthcare Data , 2016, GigaScience.

[104]  L. Piwek,et al.  The Rise of Consumer Health Wearables: Promises and Barriers , 2016, PLoS medicine.

[105]  Virginia Gewin,et al.  Data sharing: An open mind on open data , 2016, Nature.

[106]  Mehrdad Nourani,et al.  Sleep state classification using pressure sensor mats , 2015, 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[107]  Weidong Wei,et al.  Night-shift work increases morbidity of breast cancer and all-cause mortality: a meta-analysis of 16 prospective cohort studies. , 2015, Sleep medicine.

[108]  Hannu Toivonen,et al.  Adaptive Heartbeat Modeling for Beat-to-Beat Heart Rate Measurement in Ballistocardiograms , 2015, IEEE Journal of Biomedical and Health Informatics.

[109]  Mohammed Imamul Hassan Bhuiyan,et al.  On the classification of sleep states by means of statistical and spectral features from single channel Electroencephalogram , 2015, 2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI).

[110]  Shwetak N. Patel,et al.  DoppleSleep: a contactless unobtrusive sleep sensing system using short-range Doppler radar , 2015, UbiComp.

[111]  Karim Jerbi,et al.  Learning machines and sleeping brains: Automatic sleep stage classification using decision-tree multi-class support vector machines , 2015, Journal of Neuroscience Methods.

[112]  Danielle E. Ramo,et al.  Mobile App-Delivered Cognitive Behavioral Therapy for Insomnia: Feasibility and Initial Efficacy Among Veterans With Cannabis Use Disorders , 2015, JMIR research protocols.

[113]  Sudhansu Chokroverty,et al.  Is There a Clinical Role For Smartphone Sleep Apps? Comparison of Sleep Cycle Detection by a Smartphone Application to Polysomnography. , 2015, Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine.

[114]  Chang-Tsun Li,et al.  On Reducing the Effect of Covariate Factors in Gait Recognition: A Classifier Ensemble Method , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[115]  Florian Michahelles,et al.  Communicating and interpreting wearable sensor data with health coaches , 2015, 2015 9th International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth).

[116]  Rob Miller,et al.  Smart Homes that Monitor Breathing and Heart Rate , 2015, CHI.

[117]  F. Hu,et al.  Sleep Duration and Risk of Type 2 Diabetes: A Meta-analysis of Prospective Studies , 2015, Diabetes Care.

[118]  Tamara Munzner,et al.  Visualization Analysis and Design , 2014, A.K. Peters visualization series.

[119]  Mannes Poel,et al.  Comparison of feature and classifier algorithms for online automatic sleep staging based on a single EEG signal , 2014, 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[120]  K. Yaffe,et al.  Impact of sleep on the risk of cognitive decline and dementia , 2014, Current opinion in psychiatry.

[121]  M. Sateia,et al.  International classification of sleep disorders-third edition: highlights and modifications. , 2014, Chest.

[122]  Allard J van der Beek,et al.  Evaluation of an mHealth intervention aiming to improve health-related behavior and sleep and reduce fatigue among airline pilots. , 2014, Scandinavian journal of work, environment & health.

[123]  C. Kline The Bidirectional Relationship Between Exercise and Sleep , 2014, American journal of lifestyle medicine.

[124]  Jean-Marc Ginoux,et al.  An Ultrasonic Contactless Sensor for Breathing Monitoring , 2014, Sensors.

[125]  Ming-Chun Huang,et al.  Unobtrusive Sleep Stage Identification Using a Pressure-Sensitive Bed Sheet , 2014, IEEE Sensors Journal.

[126]  Ju Teng Teo,et al.  Simultaneous measurement of breathing rate and heart rate using a microbend multimode fiber optic sensor , 2014, Journal of biomedical optics.

[127]  Eui-nam Huh,et al.  Fog Computing and Smart Gateway Based Communication for Cloud of Things , 2014, 2014 International Conference on Future Internet of Things and Cloud.

[128]  Ossi Kaltiokallio,et al.  Non-invasive respiration rate monitoring using a single COTS TX-RX pair , 2014, IPSN-14 Proceedings of the 13th International Symposium on Information Processing in Sensor Networks.

[129]  D. Holtzman,et al.  Sleep and Alzheimer disease pathology—a bidirectional relationship , 2014, Nature Reviews Neurology.

[130]  M. Holmqvist,et al.  Web- vs. telehealth-based delivery of cognitive behavioral therapy for insomnia: a randomized controlled trial. , 2014, Sleep medicine.

[131]  Guoliang Xing,et al.  iSleep: unobtrusive sleep quality monitoring using smartphones , 2013, SenSys '13.

[132]  K. Lichstein,et al.  Telehealth cognitive behavior therapy for co-occurring insomnia and depression symptoms in older adults. , 2013, Journal of clinical psychology.

[133]  Zhenping Huang,et al.  Association between Sleep Duration and Cancer Risk: A Meta-Analysis of Prospective Cohort Studies , 2013, PloS one.

[134]  Akane Sano,et al.  Recognition of sleep dependent memory consolidation with multi-modal sensor data , 2013, 2013 IEEE International Conference on Body Sensor Networks.

[135]  S. Surani,et al.  Gender and age influence the effects of slow-wave sleep on respiration in patients with obstructive sleep apnea , 2013, Sleep and Breathing.

[136]  R. Rosenberg,et al.  The American Academy of Sleep Medicine inter-scorer reliability program: sleep stage scoring. , 2013, Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine.

[137]  Erry Gunawan,et al.  An Impulse Radio Ultrawideband System for Contactless Noninvasive Respiratory Monitoring , 2013, IEEE Transactions on Biomedical Engineering.

[138]  B. Koley,et al.  An ensemble system for automatic sleep stage classification using single channel EEG signal , 2012, Comput. Biol. Medicine.

[139]  Urbano Nunes,et al.  Adaptive automatic sleep stage classification under covariate shift , 2012, 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[140]  Sunny Consolvo,et al.  Lullaby: a capture & access system for understanding the sleep environment , 2012, UbiComp.

[141]  Shing-Tai Pan,et al.  A transition-constrained discrete hidden Markov model for automatic sleep staging , 2012, Biomedical engineering online.

[142]  M. Dursun,et al.  Comparison of Artificial Immune Clustering with Fuzzy C-means Clustering in the sleep stage classification problem , 2012, 2012 International Symposium on Innovations in Intelligent Systems and Applications.

[143]  Sunny Consolvo,et al.  ShutEye: encouraging awareness of healthy sleep recommendations with a mobile, peripheral display , 2012, CHI.

[144]  S. Quan,et al.  Exercise is associated with a reduced incidence of sleep-disordered breathing. , 2012, The American journal of medicine.

[145]  Joonas Paalasmaa,et al.  Quantifying respiratory variation with force sensor measurements , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[146]  J. Born,et al.  Sleep and immune function , 2011, Pflügers Archiv - European Journal of Physiology.

[147]  A. Sadeh The role and validity of actigraphy in sleep medicine: an update. , 2011, Sleep medicine reviews.

[148]  Jennifer L Martin,et al.  Wrist actigraphy. , 2011, Chest.

[149]  Pasquale Strazzullo,et al.  Sleep duration predicts cardiovascular outcomes: a systematic review and meta-analysis of prospective studies. , 2011, European heart journal.

[150]  U. Rajendra Acharya,et al.  Analysis and Automatic Identification of Sleep Stages Using Higher Order Spectra , 2010, Int. J. Neural Syst..

[151]  D. Kripke,et al.  Wrist actigraphic scoring for sleep laboratory patients: algorithm development , 2010, Journal of sleep research.

[152]  Agustina Garcés Correa,et al.  An automatic detector of drowsiness based on spectral analysis and wavelet decomposition of EEG records , 2010, EMBC 2010.

[153]  M. H. Asyali,et al.  Sleep stage and obstructive apneaic epoch classification using single-lead ECG , 2010, Biomedical engineering online.

[154]  R. Foster,et al.  Sleep and circadian rhythm disruption in psychiatric and neurodegenerative disease , 2010, Nature Reviews Neuroscience.

[155]  Tarani Chandola,et al.  The effect of short sleep duration on coronary heart disease risk is greatest among those with sleep disturbance: a prospective study from the Whitehall II cohort. , 2010, Sleep.

[156]  Guan Yong,et al.  Research on k-means Clustering Algorithm: An Improved k-means Clustering Algorithm , 2010, 2010 Third International Symposium on Intelligent Information Technology and Security Informatics.

[157]  Kazuomi Kario,et al.  Sleep Duration as a Risk Factor for Cardiovascular Disease- a Review of the Recent Literature , 2009, Current cardiology reviews.

[158]  O. Boric-Lubecke,et al.  Signal-to-Noise Ratio in Doppler Radar System for Heart and Respiratory Rate Measurements , 2009, IEEE Transactions on Microwave Theory and Techniques.

[159]  D. Blask,et al.  Melatonin, sleep disturbance and cancer risk. , 2009, Sleep medicine reviews.

[160]  P. Anderer,et al.  Interrater reliability for sleep scoring according to the Rechtschaffen & Kales and the new AASM standard , 2009, Journal of sleep research.

[161]  M. Opp,et al.  How (and why) the immune system makes us sleep , 2009, Nature Reviews Neuroscience.

[162]  S. Sidney,et al.  Short sleep duration and incident coronary artery calcification. , 2008, JAMA.

[163]  T. Roth,et al.  Neurophysiology of Sleep and Wakefulness: Basic Science and Clinical Implications , 2008, Current neuropharmacology.

[164]  Yang Hao,et al.  Wireless body sensor networks for health-monitoring applications , 2008, Physiological measurement.

[165]  N. Marshall,et al.  Sleep apnea as an independent risk factor for all-cause mortality: the Busselton Health Study. , 2008, Sleep.

[166]  Anup V. Desai,et al.  Two randomized placebo-controlled trials to evaluate the efficacy and tolerability of mirtazapine for the treatment of obstructive sleep apnea. , 2008, Sleep.

[167]  Cathy Alessi,et al.  Practice parameters for the clinical evaluation and treatment of circadian rhythm sleep disorders. An American Academy of Sleep Medicine report. , 2007, Sleep.

[168]  Myoungho Lee,et al.  A study on a non-contacting respiration signal monitoring system using Doppler ultrasound , 2007, Medical & Biological Engineering & Computing.

[169]  E. van Cauter,et al.  The metabolic consequences of sleep deprivation. , 2007, Sleep medicine reviews.

[170]  N. Butkov,et al.  Digital analysis and technical specifications. , 2007, Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine.

[171]  Ronald J Ozminkowski,et al.  The direct and indirect costs of untreated insomnia in adults in the United States. , 2007, Sleep.

[172]  P. Achermann,et al.  Trait-like individual differences in the human sleep electroencephalogram , 2006, Neuroscience.

[173]  S. Taheri,et al.  The link between short sleep duration and obesity: we should recommend more sleep to prevent obesity , 2006, Archives of Disease in Childhood.

[174]  D. Hillman,et al.  The economic cost of sleep disorders. , 2006, Sleep.

[175]  Kwangsuk Park,et al.  Air mattress sensor system with balancing tube for unconstrained measurement of respiration and heart beat movements , 2005, Physiological measurement.

[176]  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.

[177]  C. Drake,et al.  Shift work sleep disorder: prevalence and consequences beyond that of symptomatic day workers. , 2004, Sleep.

[178]  F. Zijlstra,et al.  Sleep quantity, sleep difficulties and their perceived consequences in a representative sample of some 2000 British adults , 2004, Journal of sleep research.

[179]  Nadezhda Sazonova,et al.  Activity-based sleep–wake identification in infants , 2004, Physiological measurement.

[180]  J. Sheridan,et al.  Effect of Sleep Deprivation on Response to Immunizaton , 2002 .

[181]  M. Schneemann,et al.  Coin Rubbing and Camphor Intoxication—Reply , 2002 .

[182]  Stephanie Schuckers,et al.  Activity-based sleep-wake identification in infants , 2002, Computers in Cardiology.

[183]  Peter Achermann,et al.  Individual ‘Fingerprints’ in Human Sleep EEG Topography , 2001, Neuropsychopharmacology.

[184]  J. Abisheganaden,et al.  Respiratory monitoring using an air-mattress system , 2000, Physiological measurement.

[185]  Suresh R. Devasahayam,et al.  Signals and Systems in Biomedical Engineering: Signal Processing and Physiological Systems Modeling , 2000 .

[186]  Yoshua Bengio,et al.  Convolutional networks for images, speech, and time series , 1998 .

[187]  S. Hochreiter,et al.  Long Short-Term Memory , 1997, Neural Computation.

[188]  D. Dawson,et al.  Fatigue, alcohol and performance impairment , 1997, Nature.

[189]  G. Jean-Louis,et al.  Determination of sleep and wakefulness with the actigraph data analysis software (ADAS). , 1996, Sleep.

[190]  M. Bonnet,et al.  We are chronically sleep deprived. , 1995, Sleep.

[191]  A. Sadeh,et al.  Activity-based sleep-wake identification: an empirical test of methodological issues. , 1994, Sleep.

[192]  D. J. Mullaney,et al.  Automatic sleep/wake identification from wrist activity. , 1992, Sleep.

[193]  D. Greenblatt,et al.  The International Classification of Sleep Disorders , 1992 .

[194]  D. J. Mullaney,et al.  An activity-based sleep monitor system for ambulatory use. , 1982, Sleep.

[195]  R. Kanaan,et al.  An Ambulatory Polysomnography Study of the Post-traumatic Nightmares of Post-traumatic Stress Disorder , 2018, Sleep.

[196]  M. Hafner,et al.  Why Sleep Matters-The Economic Costs of Insufficient Sleep: A Cross-Country Comparative Analysis. , 2017, Rand health quarterly.

[197]  Mohammed Imamul Hassan Bhuiyan,et al.  Automatic sleep scoring using statistical features in the EMD domain and ensemble methods , 2016 .

[198]  Jaihyun Park,et al.  SVM based dynamic classifier for sleep disorder monitoring wearable device , 2016, 2016 IEEE International Conference on Consumer Electronics (ICCE).

[199]  Juan Miguel García-Gómez,et al.  Actigraphy pattern analysis for outpatient monitoring. , 2015, Methods in molecular biology.

[200]  Samu Sjövall,et al.  Coping with stress: Firstbeat Lifestyle Assessments for family workers , 2015 .

[201]  Adolfo Muñoz,et al.  The Usefulness of Activity Trackers in Elderly with Reduced Mobility: A Case Study , 2013, MedInfo.

[202]  L. Mâsse,et al.  Physical activity in the United States measured by accelerometer. , 2008, Medicine and science in sports and exercise.

[203]  W. Dement,et al.  The promise of sleep : a pioneer in sleep medicine explores the vital connection between health, happiness, and a good night's sleep : the national bestseller/ William C. Dement and Christopher Vaughan , 1999 .

[204]  Marius Fieschi,et al.  Artificial Intelligence in Medicine , 1990, Springer US.