Artificial intelligence in sleep medicine: Background and implications for clinicians.

None Polysomnography (PSG) remains the cornerstone of objective testing in sleep medicine and results in massive amounts of electrophysiological data, which is well-suited for analysis with artificial intelligence (AI)-based tools. Combined with other sources of health data, AI is expected to provide new insights to inform the clinical care of sleep disorders and advance our understanding of the integral role sleep plays in human health. Additionally, AI has the potential to streamline day-to-day operations and therefore optimize direct patient care by the sleep disorders team. However, clinicians, scientists, and other stakeholders must develop best practices to integrate this rapidly evolving technology into our daily work while maintaining the highest degree of quality and transparency in health care and research. Ultimately, when harnessed appropriately in conjunction with human expertise, AI will improve the practice of sleep medicine and further sleep science for the health and well-being of our patients.

[1]  S. Drate.  Adolescent , 2020, Definitions.

[2]  Mark Hoogendoorn,et al.  Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy , 2020, Intensive Care Medicine.

[3]  Daniel B. Forger,et al.  Sleep stage prediction with raw acceleration and photoplethysmography heart rate data derived from a consumer wearable device , 2019, Sleep.

[4]  Daniel B. Forger,et al.  0326 Sleep Stage Prediction With Raw Acceleration And Photoplethysmography Heart Rate Data Derived From A Consumer Wearable Device , 2019, Sleep.

[5]  I. Colrain,et al.  The Sleep of the Ring: Comparison of the ŌURA Sleep Tracker Against Polysomnography , 2019, Behavioral sleep medicine.

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

[7]  H. Adeli,et al.  Automated seizure prediction , 2018, Epilepsy & Behavior.

[8]  A. Ng,et al.  Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists , 2018, PLoS medicine.

[9]  Elizabeth C. Lorenzi,et al.  Development and validation of machine learning models to identify high-risk surgical patients using automatically curated electronic health record data (Pythia): A retrospective, single-site study , 2018, PLoS medicine.

[10]  Peter Washington,et al.  Mobile detection of autism through machine learning on home video: A development and prospective validation study , 2018, PLoS medicine.

[11]  Sara Fontanella,et al.  Machine learning to identify pairwise interactions between specific IgE antibodies and their association with asthma: A cross-sectional analysis within a population-based birth cohort , 2018, PLoS medicine.

[12]  I. Hirsch,et al.  Advances in Glucose Monitoring and Automated Insulin Delivery: Supplement to Endocrine Society Clinical Practice Guidelines , 2018, Journal of the Endocrine Society.

[13]  B. Edwards,et al.  Identifying obstructive sleep apnoea patients responsive to supplemental oxygen therapy , 2018, European Respiratory Journal.

[14]  Geraint Rees,et al.  Clinically applicable deep learning for diagnosis and referral in retinal disease , 2018, Nature Medicine.

[15]  G. Jean-Louis,et al.  Sleep Duration and Physical Activity Profiles Associated With Self-Reported Stroke in the United States: Application of Bayesian Belief Network Modeling Techniques , 2018, Front. Neurol..

[16]  Joseph Cheung,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.

[17]  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.

[18]  Ju Lynn Ong,et al.  An end-to-end framework for real-time automatic sleep stage classification , 2018, Sleep.

[19]  N. Shah,et al.  Implementing Machine Learning in Health Care - Addressing Ethical Challenges. , 2018, The New England journal of medicine.

[20]  G. C. Gutiérrez-Tobal,et al.  Assessment of oximetry-based statistical classifiers as simplified screening tools in the management of childhood obstructive sleep apnea , 2018, Sleep and Breathing.

[21]  G. C. Gutiérrez-Tobal,et al.  Nocturnal Oximetry‐based Evaluation of Habitually Snoring Children , 2017, American journal of respiratory and critical care medicine.

[22]  Andrew H. Beck,et al.  Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer , 2017, JAMA.

[23]  Navdeep Jaitly,et al.  Speech recognition for medical conversations , 2017, INTERSPEECH.

[24]  Dimitri Perrin,et al.  Neural network analysis of sleep stages enables efficient diagnosis of narcolepsy , 2017, Nature Communications.

[25]  B. Racette,et al.  A predictive model to identify Parkinson disease from administrative claims data , 2017, Neurology.

[26]  Olga Sourina,et al.  Large-Scale Automated Sleep Staging , 2017, Sleep.

[27]  J. Concato,et al.  Polysomnographic phenotypes and their cardiovascular implications in obstructive sleep apnoea , 2017, Thorax.

[28]  Aisha T. Langford,et al.  Differential and Combined Effects of Physical Activity Profiles and Prohealth Behaviors on Diabetes Prevalence among Blacks and Whites in the US Population: A Novel Bayesian Belief Network Machine Learning Analysis , 2017, Journal of diabetes research.

[29]  Andrew Y. Ng,et al.  Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks , 2017, ArXiv.

[30]  Ronald M. Aarts,et al.  Validation of Photoplethysmography-Based Sleep Staging Compared With Polysomnography in Healthy Middle-Aged Adults , 2017, Sleep.

[31]  Jonathan H. Chen,et al.  Machine Learning and Prediction in Medicine - Beyond the Peak of Inflated Expectations. , 2017, The New England journal of medicine.

[32]  R. Thomas,et al.  Urgent Need to Improve PAP Management: The Devil Is in Two (Fixable) Details. , 2017, Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine.

[33]  E. Mignot,et al.  Diagnostic value of sleep stage dissociation as visualized on a 2-dimensional sleep state space in human narcolepsy , 2017, Journal of Neuroscience Methods.

[34]  David W. Bates,et al.  Screening for medication errors using an outlier detection system , 2017, J. Am. Medical Informatics Assoc..

[35]  Sebastian Thrun,et al.  Dermatologist-level classification of skin cancer with deep neural networks , 2017, Nature.

[36]  R. Chervin,et al.  The Past Is Prologue: The Future of Sleep Medicine. , 2017, Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine.

[37]  Subhashini Venugopalan,et al.  Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. , 2016, JAMA.

[38]  E. Topol,et al.  Adapting to Artificial Intelligence: Radiologists and Pathologists as Information Specialists. , 2016, JAMA.

[39]  B. Edwards,et al.  Upper-Airway Collapsibility and Loop Gain Predict the Response to Oral Appliance Therapy in Patients with Obstructive Sleep Apnea. , 2016, American journal of respiratory and critical care medicine.

[40]  P. Hanly,et al.  Minimizing Interrater Variability in Staging Sleep by Use of Computer-Derived Features. , 2016, Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine.

[41]  A. Hassan,et al.  A decision support system for automatic sleep staging from EEG signals using tunable Q-factor wavelet transform and spectral features , 2016, Journal of Neuroscience Methods.

[42]  J. Butler,et al.  Zopiclone Increases the Arousal Threshold without Impairing Genioglossus Activity in Obstructive Sleep Apnea. , 2016, Sleep.

[43]  Georg Dorffner,et al.  Computer-Assisted Automated Scoring of Polysomnograms Using the Somnolyzer System. , 2015, Sleep.

[44]  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.

[45]  Suchi Saria,et al.  Subtyping: What It is and Its Role in Precision Medicine , 2015, IEEE Intelligent Systems.

[46]  B. Boeve,et al.  Antidepressants Increase REM Sleep Muscle Tone in Patients with and without REM Sleep Behavior Disorder. , 2015, Sleep.

[47]  P. Hanly,et al.  Odds ratio product of sleep EEG as a continuous measure of sleep state. , 2015, Sleep.

[48]  Werner Poewe,et al.  Validation of an integrated software for the detection of rapid eye movement sleep behavior disorder. , 2014, Sleep.

[49]  Erik K St Louis,et al.  Diagnostic thresholds for quantitative REM sleep phasic burst duration, phasic and tonic muscle activity, and REM atonia index in REM sleep behavior disorder with and without comorbid obstructive sleep apnea. , 2014, Sleep.

[50]  Francesco Rundo,et al.  Comparison between an automatic and a visual scoring method of the chin muscle tone during rapid eye movement sleep. , 2014, Sleep medicine.

[51]  Atul Malhotra,et al.  Trazodone increases the respiratory arousal threshold in patients with obstructive sleep apnea and a low arousal threshold. , 2014, Sleep.

[52]  J. Molinuevo,et al.  Neurodegenerative Disorder Risk in Idiopathic REM Sleep Behavior Disorder: Study in 174 Patients , 2014, PloS one.

[53]  Daniel J Buysse.  Sleep health: can we define it? Does it matter? , 2014, Sleep.

[54]  A. Malhotra,et al.  Defining phenotypic causes of obstructive sleep apnea. Identification of novel therapeutic targets. , 2013, American journal of respiratory and critical care medicine.

[55]  D. Rye,et al.  Test-retest reliability of the multiple sleep latency test in narcolepsy without cataplexy and idiopathic hypersomnia. , 2013, Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine.

[56]  Carlos H Schenck,et al.  Delayed emergence of a parkinsonian disorder or dementia in 81% of older men initially diagnosed with idiopathic rapid eye movement sleep behavior disorder: a 16-year update on a previously reported series. , 2013, Sleep medicine.

[57]  A. Pack,et al.  Performance of an automated polysomnography scoring system versus computer-assisted manual scoring. , 2013, Sleep.

[58]  S. Nemati,et al.  A simplified method for determining phenotypic traits in patients with obstructive sleep apnea. , 2013, Journal of applied physiology.

[59]  R. Millman,et al.  Subjective sleepiness and daytime functioning in bariatric patients with obstructive sleep apnea , 2013, Sleep and Breathing.

[60]  R. Chervin,et al.  Respiratory cycle-related EEG changes: response to CPAP. , 2012, Sleep.

[61]  Luca Citi,et al.  Instantaneous monitoring of sleep fragmentation by point process heart rate variability and respiratory dynamics , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[62]  Sheng-Fu Liang,et al.  A rule-based automatic sleep staging method , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[63]  Andrew H. Beck,et al.  Systematic Analysis of Breast Cancer Morphology Uncovers Stromal Features Associated with Survival , 2011, Science Translational Medicine.

[64]  A. Malhotra,et al.  Eszopiclone increases the respiratory arousal threshold and lowers the apnoea/hypopnoea index in obstructive sleep apnoea patients with a low arousal threshold. , 2011, Clinical science.

[65]  P. Anderer,et al.  Computer-Assisted Sleep Classification according to the Standard of the American Academy of Sleep Medicine : Validation Study of the AASM Version of the Somnolyzer 24 ! 7 , 2010 .

[66]  F. Cappuccio,et al.  Sleep duration and all-cause mortality: a systematic review and meta-analysis of prospective studies. , 2010, Sleep.

[67]  Markus Svensén,et al.  Beyond atopy: multiple patterns of sensitization in relation to asthma in a birth cohort study. , 2010, American journal of respiratory and critical care medicine.

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

[69]  D. Gozal,et al.  Daytime sleepiness and polysomnography in obstructive sleep apnea patients. , 2008, Sleep medicine.

[70]  G. Plazzi,et al.  A quantitative statistical analysis of the submentalis muscle EMG amplitude during sleep in normal controls and patients with REM sleep behavior disorder , 2008, Journal of sleep research.

[71]  Thomas Penzel,et al.  Quantification of Tonic and Phasic Muscle Activity in REM Sleep Behavior Disorder , 2008, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[72]  Chung-Kang Peng,et al.  Differentiating obstructive from central and complex sleep apnea using an automated electrocardiogram-based method. , 2007, Sleep.

[73]  A. Schlögl,et al.  An E-Health Solution for Automatic Sleep Classification according to Rechtschaffen and Kales: Validation Study of the Somnolyzer 24 × 7 Utilizing the Siesta Database , 2005, Neuropsychobiology.

[74]  R. Chervin,et al.  Electroencephalographic changes during respiratory cycles predict sleepiness in sleep apnea. , 2005, American journal of respiratory and critical care medicine.

[75]  T. Young,et al.  Epidemiology of daytime sleepiness: definitions, symptomatology, and prevalence. , 2004, The Journal of clinical psychiatry.

[76]  A. Malhotra,et al.  Assessment of automated scoring of polysomnographic recordings in a population with suspected sleep-disordered breathing. , 2004, Sleep.

[77]  N M Punjabi,et al.  Sleep disorders in regional sleep centers: a national cooperative study. Coleman II Study Investigators. , 2000, Sleep.

[78]  M. Carskadon,et al.  Adolescent sleep patterns, circadian timing, and sleepiness at a transition to early school days. , 1998, Sleep.

[79]  M. Cole,et al.  Correlations among Epworth Sleepiness Scale scores, multiple sleep latency tests and psychological symptoms , 1998, Journal of sleep research.

[80]  S. Redline,et al.  Relationship between sleepiness and general health status. , 1996, Sleep.

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

[82]  W C Dement,et al.  Determinants of daytime sleepiness in obstructive sleep apnea. , 1988, Chest.

[83]  Tell Me,et al.  Which? , 1882, The Homoeopathic physician.

[84]  Daniel J Buysse,et al.  Which Sleep Health Characteristics Predict All-Cause Mortality in Older Men? An Application of Flexible Multivariable Approaches , 2018, Sleep.

[85]  H. Dickhaus,et al.  Classification of Sleep Stages Using Multi-wavelet Time Frequency Entropy and LDA , 2010, Methods of Information in Medicine.

[86]  A. Chesson,et al.  The American Academy of Sleep Medicine (AASM) Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specifications , 2007 .

[87]  Todd,et al.  Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning , 2002, Nature Medicine.

[88]  F. J. Nieto,et al.  Relation of sleepiness to respiratory disturbance index: the Sleep Heart Health Study. , 1999, American journal of respiratory and critical care medicine.

[89]  A. Muzet,et al.  Sleep stage scoring using the neural network model: comparison between visual and automatic analysis in normal subjects and patients. , 1996, Sleep.

[90]  K. Jenpanich,et al.  [Drug administration]. , 1976, Thai journal of nursing.