Advances in unobtrusive monitoring of sleep apnea using machine learning (2021)
Abstract / truncated to 115 words
Obstructive sleep apnea (OSA) is among the most prevalent sleep disorders, which is estimated to affect 6 %−19 % of women and 13 %−33 % of men. Besides daytime sleepiness, impaired cognitive functioning and an increased risk for accidents, OSA may lead to obesity, diabetes and cardiovascular diseases (CVD) on the long term. Its prevalence is only expected to rise, as it is linked to aging and excessive body fat. Nevertheless, many patients remain undiagnosed and untreated due to the cumbersome clinical diagnostic procedures. For this, the patient is required to sleep with an extensive set of body attached sensors. In addition, the recordings only provide a single night perspective on the patient in an ... toggle 2 keywordssleep apnea – machine learning
- Huysmans, Dorien
- KU Leuven
- Publication Year
- Upload Date
- Feb. 13, 2023
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