ABSTRACT BACKGROUND: Manual polysomnography is considered as the gold-standard for diagnosis of sleep disorders. However, it is a labor intensive and time-consuming task and is being replaced by automated sleep scoring systems. METHODS: This cross-sectional study was conducted at Sleep Medicine and Research Center, King Abdulaziz University Hospital, Jeddah, Saudi Arabia, between January 2022 and July 2024, the correlation and agreement between various parameters recorded through automated and manual sleep scoring. RESULTS: Of a total of 442 participants, 234 (53.4%) were male and 206 (46.6%) were female. The mean age of participants was 48 ± 18.5 years. A moderate degree of agreement was found between apnea–hypopnea index (AHI) (κ = 0.510) and respiratory disturbance index supine (κ = 0.474) recorded through automated and manual scoring ( P < 0.001). A fair degree of agreement (κ = 0.308) was recorded for AHI-rapid eye movement ( P < 0.001) ( P < 0.001). Intra-class correlation showed a good agreement between total sleep time (TST) recorded through both systems (ICC = 0.727, P < 0.001). AHI events recorded through both systems showed good agreement (ICC = 0.753, P < 0.001), whereas an excellent agreement was observed for TST supine (ICC = 0.902, P < 0.001). CONCLUSION: There appears to be a marked variation in the accuracy of various sleep parameters by automated sleep recording. Further studies that utilize advance machine learning algorithms can help better understanding of the role of automated sleep scoring in screening and diagnosis of sleep disorders.
Faris Alhejaili (Wed,) studied this question.