  Sleep is vital for people's physical and mental health, and sound sleep can
help them focus on daily activities. Therefore, a sleep study that includes
sleep patterns and disorders is crucial to enhancing our knowledge about
individuals' health status. The findings on sleep stages and sleep disorders
relied on polysomnography and self-report measures, and then the study went
through clinical assessments by expert physicians. However, the evaluation
process of sleep stage classification and sleep disorder has become more
convenient with artificial intelligence applications and numerous
investigations focusing on various datasets with advanced algorithms and
techniques that offer improved computational ease and accuracy. This study aims
to provide a comprehensive, systematic review and meta-analysis of the recent
literature to analyze the different approaches and their outcomes in sleep
studies, which includes works on sleep stages classification and sleep disorder
detection using AI. In this review, 183 articles were initially selected from
different journals, among which 80 records were enlisted for explicit review,
ranging from 2016 to 2023. Brain waves were the most commonly employed body
parameters for sleep staging and disorder studies. The convolutional neural
network, the most widely used of the 34 distinct artificial intelligence
models, comprised 27%. The other models included the long short-term memory,
support vector machine, random forest, and recurrent neural network, which
consisted of 11%, 6%, 6%, and 5% sequentially. For performance metrics,
accuracy was widely used for a maximum of 83.75% of the cases, the F1 score of
45%, Kappa of 36.25%, Sensitivity of 31.25%, and Specificity of 30% of cases,
along with the other metrics. This article would help physicians and
researchers get the gist of AI's contribution to sleep studies and the
feasibility of their intended work.
