Eroğul, Osman2024-04-062024-04-062021https://sciendo.com/article/10.2478/ebtj-2021-0029https://hdl.handle.net/20.500.11851/11272The most basic definition of the sleep is related with organism’s response to environmental stimuli. Sleep can be defined as a reversible, lessthan-awake response to the environment, which recurs on a daily cycle. All problems that affect sleep quality, duration and subject’s daily life are named as sleep disorder. Polysomnography is the gold standard for the evaluation of sleep signals. A polysomnography device records electroencephalography, electrooculography, and electromyography activities at the same time. A polysomnographic measurement can be extended with recording additiona lphysiological signals, like electrocardiography and body position, oxygen level in the blood, and snoring. These additional signals are important for the diagnosis of sleep disorders. The diagnosis of sleep disorder is done with analyzing laborious overnight polysomnography recording. Nowadays, to reduce duration and increase accuracy of diagnosis, artificial intelligence applications are used. These applications developed by using feature extraction-based machine learning or deep learning algorithms that generally apply to one or two polysomnography signals. By using artificial intelligence in sleep studies, duration of diagnosis reduces from hours to minutes and increases accuracy of diagnosis to over 90%. This study gives examples about artificial intelligence applications used in sleep studies.eninfo:eu-repo/semantics/openAccessSleepArtificial intelligencePolysomnographyPhysiological signalsConference Object