Abstract
AbstractObstructive sleep apnea (OSA) is a chronic condition affecting up to 1 billion people, globally. Despite this spread, OSA is still thought to be underdiagnosed. Lack of diagnosis is largely attributed to the high cost, resource-intensive, and time-consuming nature of existing diagnostic technologies during sleep. As individuals with OSA do not show many symptoms other than daytime sleepiness, predicting OSA while the individual is awake (wakefulness) is quite challenging. However, research especially in the last decade has shown promising results for quick and accurate methodologies to predict OSA during wakefulness. Furthermore, advances in machine learning algorithms offer new ways to analyze the measured data with more precision. With a widening research outlook, the present review compares methodologies for OSA screening during wakefulness, and recommendations are made for avenues of future research and study designs.
Graphical abstract
Publisher
Springer Science and Business Media LLC
Reference101 articles.
1. Colten HR, Altevogt BM (2006) Sleep disorders and sleep deprivation: an unmet public health problem. The national academies press, Washington, D.C. https://doi.org/10.17226/11617
2. Kushida CA, Littner MR, Morgenthaler T et al (2005) Practice parameters for the indications for polysomnography and related procedures: an update for 2005. Sleep 28:499–523. https://doi.org/10.1093/SLEEP/28.4.499
3. American Academy of Sleep Medicine (2005) International classification of sleep disorders: diagnostic & coding manual, 2nd ed. American Academy of Sleep Medicine, Westchester, IL
4. Berry RB, Brooks R, Gamaldo CE et al (2012) The AASM manual for the scoring of sleep and associated events. Rules Terminology Techn Specifications Darien Illinois Am Acad Sleep Med 176:2012
5. Bradley TD, Floras JS (2013) Sleep apnea: implications in cardiovascular and cerebrovascular disease, 2nd edn. CRC Press, Boca Raton