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Obstructive sleep apnea detection during wakefulness: a comprehensive methodological review
https://orcid.org/0000-0001-9202-949X
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
Obstructive sleep apnea detection during wakefulness: a comprehensive methodological review
https://orcid.org/0000-0001-9202-949X
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
Obstructive sleep apnea detection during wakefulness: a comprehensive methodological review
https://orcid.org/0000-0001-9202-949X
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
Obstructive sleep apnea detection during wakefulness: a comprehensive methodological review
Med Biol Eng Comput
Alqudah, Ali Mohammad (author) / Elwali, Ahmed (author) / Kupiak, Brendan (author) / Hajipour, Farahnaz (author) / Jacobson, Natasha (author) / Moussavi, Zahra (author)
Medical & Biological Engineering & Computing ; 62 ; 1277-1311
2024-05-01
Article (Journal)
Electronic Resource
English
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