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- W4304481538 abstract "The overnight polysomnographyPolysomnography shows a range of drawbacks to diagnose obstructive sleep apnea (OSA)Obstructive sleep apnea (OSA) that have led to the search for artificial intelligence-based alternatives. Many classic machine learningMachine learning methods have been already evaluated for this purpose. In this chapter, we show the main approaches found in the scientific literature along with the most used data to develop the models, useful and large easily available databases, and suitable methods to assess performances. In addition, a range of results from selected studies are presented as examples of these methods. Very high diagnostic performances are reported in these results regardless of the approaches taken. This leads us to conclude that conventional machine learningMachine learning methods are useful techniques to develop new OSAObstructive sleep apnea (OSA) diagnosisDiagnoses simplification proposals and to act as benchmark for other more recent methods such as deep learningDeep learning." @default.
- W4304481538 created "2022-10-12" @default.
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- W4304481538 date "2022-01-01" @default.
- W4304481538 modified "2023-10-16" @default.
- W4304481538 title "Conventional Machine Learning Methods Applied to the Automatic Diagnosis of Sleep Apnea" @default.
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- W4304481538 doi "https://doi.org/10.1007/978-3-031-06413-5_8" @default.
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