A systematic review on sleep stage classification and sleep disorder detection using artificial intelligence
Tayab Uddin Wara, Ababil Hossain Fahad, Adri Shankar Das, Md. Mehedi Hasan Shawon
Abstract
Sleep is vital for our physical and mental health, and sound sleep can help us to focus on daily activities. Therefore, a sleep study that includes sleep patterns and disorders is crucial to enhancing our knowledge about individual 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. Artificial Intelligence has made the evaluation process of sleep stages and sleep disorders classification more efficient. Many studies have focused on analyzing various datasets using advanced techniques and algorithms to improve computational ease and accuracy. This review aims to comprehensively analyze recent literature on different approaches and outcomes in sleep studies, specifically on ‘sleep stages classification' and ‘sleep disorder detection' using Artificial Intelligence. Initially, 185 articles were selected from top journals, and eventually, 81 of them were reviewed in detail, covering the period from 2016 to 2023. Brain waves are the most commonly used body signals for studying sleep patterns and disorders. Almost 36 % of the research exclusively used brain activity signals, and 80 % combined them with other body parameters in sleep staging. The Neural Network algorithms are the most popular, having 47 % of the total usage. At the same time, Long Short-Term Memory, Ensemble Learning, Support Vector Machine, and Random Forest accounted for 15 %, 12 %, 7 %, and 6 % of usage, respectively. For evaluating AI model performance, accuracy or precision is used in 86.42 % of cases, followed by an F1 score of 46.91 %, Kappa of 39.51 %, Specificity of 30.86 %, Sensitivity of 29.63 %, along with other metrics. This article would help physicians and researchers get the gist of Artificial Intelligence's contribution to sleep studies and the feasibility of their intended work.