Cited by 555Obstructive Sleep Apnea ResearchSleep and related disordersSleep and Wakefulness Research

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Polysomnography

Jessica Vensel Rundo, Ralph Downey·2019·Handbook of clinical neurology

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Abstract

Polysomnography refers to a systematic process used to collect physiologic parameters during sleep. A polysomnogram (PSG) is a procedure that utilizes electroencephalogram, electro-oculogram, electromyogram, electrocardiogram, and pulse oximetry, as well as airflow and respiratory effort, to evaluate for underlying causes of sleep disturbances. PSG is considered to be the gold standard for diagnosing sleep-related breathing disorders, which include obstructive sleep apnea (OSA), central sleep apnea, and sleep-related hypoventilation/hypoxia. PSG can also be utilized to evaluate for other sleep disorders, including nocturnal seizures, narcolepsy, periodic limb movement disorder, and rapid eye movement sleep behavior disorder. With recent technological developments, home sleep apnea testing can be done to confirm a diagnosis in patients with a high risk for moderate to severe OSA in the absence of comorbid medical conditions or other suspected sleep disorders.

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Über das Elektrenkephalogramm des Menschen
Berger, H. (1929) · European Archives of Psychiatry and Clinical Neuroscience · doi:10.1007/bf01797193 · Cited by 4,266
Clinical Practice Guideline for Diagnostic Testing for Adult Obstructive Sleep Apnea: An American Academy of Sleep Medicine Clinical Practice Guideline
Kapur, V. K., Auckley, D. H., Chowdhuri, S., Kuhlmann, D. C., Mehra, R., Ramar, K., & Harrod, C. G. (2017) · Journal of Clinical Sleep Medicine · doi:10.5664/jcsm.6506 · Cited by 3,253
REVERSAL OF OBSTRUCTIVE SLEEP APNOEA BY CONTINUOUS POSITIVE AIRWAY PRESSURE APPLIED THROUGH THE NARES
Sullivan, C., Berthon-Jones, M., Issa, F., & Eves, L. (1981) · The Lancet · doi:10.1016/s0140-6736(81)92140-1 · Cited by 2,731
Regularly Occurring Periods of Eye Motility, and Concomitant Phenomena, During Sleep
Aserinsky, E., & Kleitman, N. (1953) · Science · doi:10.1126/science.118.3062.273 · Cited by 2,488
Practice Parameters for the Indications for Polysomnography and Related Procedures: An Update for 2005
Kushida, C. A., Littner, M. R., Morgenthaler, T., Alessi, C. A., Bailey, D., Coleman, J., Friedman, L., Hirshkowitz, M., Kapen, S., Kramer, M., et al. (2005) · Sleep · doi:10.1093/sleep/28.4.499 · Cited by 2,054
Cyclic variations in EEG during sleep and their relation to eye movements, body motility, and dreaming
Dement, W., & Kleitman, N. (1957) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/0013-4694(57)90088-3 · Cited by 1,876
Clinical Guidelines for the Use of Unattended Portable Monitors in the Diagnosis of Obstructive Sleep Apnea in Adult Patients
Collop, N. A., Anderson, W. M., Boehlecke, B., Claman, D., Goldberg, R., Gottlieb, D. J., Hudgel, D., Sateia, M., Schwab, R., & Portable Monitoring Task Force of the American Academy of Sleep Medicine (2007) · Journal of Clinical Sleep Medicine · doi:10.5664/jcsm.27032 · Cited by 1,550
Surgical Correction of Anatomic Abnormalities in Obstructive Sleep Apnea Syndrome: Uvulopalatopharyngoplasty
Fujita, S., Conway, W. A., Zorick, F. J., & Roth, T. G. (1981) · Otolaryngology · doi:10.1177/019459988108900609 · Cited by 1,230
Normal Human Sleep: An Overview
Carskadon, M. A., & Dement, W. C. (2005) · Elsevier eBooks · doi:10.1016/b0-72-160797-7/50009-4 · Cited by 1,191
The visual scoring of sleep in adults
Silber, M. H., Ancoli-Israel, S., Bonnet, M. H., Chokroverty, S., Grigg-Damberger, M. M., Hirshkowitz, M., Kapen, S., Keenan, S. A., Kryger, M. H., Penzel, T., et al. (2007) · Journal of Clinical Sleep Medicine · doi:10.5664/jcsm.26814 · Cited by 1,064

Show all 42 references

Research works printed but not linked: 5

Bramwell, 1909, Excessive sleepiness, Clini Stud Edinb, 8, 276
Caton, 1875, The electric current of the brain, Br Med J, 2, 278
Caton, 1889, Case of narcolepsy, Clin Soc Trans, 22, 133
Jasper, 1958, The 10–20 electrode system of the International Federation, Electroencephalogr Clin Neurophysiol, 10, 370
Jouvet, 1959, Sur un stade d’activite electrique cerebrale rapide au cours du sommeil physiologique [On a stage of rapid cerebral electrical activity in the course of physiological sleep], CR Seances Soc Biol Fil, 153, 1024

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Background
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“Polysomnography (PSG) is capable of collecting many physiological parameters during sleep and is considered by sleep experts to be the gold standard for measuring sleep quality and sleep disorders (Rundo and Downey, 2019; Malekzadeh et al., 2022).”
Background
Association of nocturnal sleep duration and nocturnal sleep changes with instrumental activities of daily living disability among middle-aged and elderly Chinese
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Authors: Rundo, J. V., & Downey, R.
Affiliations: Cleveland Clinic (US)
Corresponding author: Jessica Vensel Rundo
Venue: Handbook of clinical neurology
Publisher: Elsevier
Published: 2019
Volume: 160
Pages: 381-392
ISSN: 0072-9752 (ISSN-L)
DOI: 10.1016/b978-0-444-64032-1.00025-4
OpenAlex: W4231438709
Cited by: 555
References: 42
Citations by year: 2026: 73 · 2025: 135 · 2024: 130 · 2023: 106 · 2022: 61 · 2021: 35 · 2020: 15
FWCI: 85.09
Type: book chapter
Language: en
Open access: closed
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
Locations: Handbook of clinical neurology (publishedVersion)
PubMed (publishedVersion)
Topics: Health Sciences › Medicine › Physiology (medical) › Obstructive Sleep Apnea Research
Social Sciences › Psychology › Experimental and Cognitive Psychology › Sleep and related disorders
Life Sciences › Neuroscience › Cognitive Neuroscience › Sleep and Wakefulness Research
Keywords: polysomnography, sleep disorders, obstructive sleep apnea, central sleep apnea, nocturnal hypoventilation, home sleep apnea testing, EEG, electrooculogram, electromyography, pulse oximetry, nocturnal seizures, narcolepsy, periodic limb movement disorder, REM sleep behavior disorder
MeSH terms: Electroencephalography, Electromyography, Humans, Sleep, Sleep Wake Disorders, Polysomnography

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