AUTHOR=Kang Chaewon , An Sora , Kim Hyeon Jin , Devi Maithreyee , Cho Aram , Hwang Sungeun , Lee Hyang Woon 

TITLE=Age-integrated artificial intelligence framework for sleep stage classification and obstructive sleep apnea screening

JOURNAL=Frontiers in Neuroscience

VOLUME=Volume 17 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2023.1059186

DOI=10.3389/fnins.2023.1059186

ISSN=1662-453X

ABSTRACT=Sleep is an essential function to sustain a healthy life, and sleep dysfunction can cause various physical and mental issues. In particular, obstructive sleep apnea (OSA) is one of the most common sleep disorders and, if not treated in a timely manner, OSA can lead to critical problems such as high blood pressure or heart disease. The first crucial step in evaluating individuals’ quality of sleep and diagnosing sleep disorders is to classify sleep stages using polysomnographic (PSG) data including electroencephalography (EEG). To date, such sleep stage scoring has been mainly performed manually via visual inspection by experts, which is not only a time-consuming and laborious process but also may yield subjective results. Therefore, we have developed a computational framework that enables automatic sleep stage classification utilizing the power spectral density (PSD) features of sleep EEG. In particular, we propose an integrated artificial intelligence (AI) framework to further inform the risk of OSA based on the characteristics in automatically scored sleep stages. Given that previous findings that the characteristics of sleep EEG differ by age group, we followed a strategy of training age-specific models (younger and older groups) and a general model and comparing their performance. The results demonstrated that the performance of the younger age-specific group model was similar to that of the general model (and even higher at certain stages), but the performance of the older age-specific group model was rather low, suggesting that bias in individual variables, such as age bias, should be considered during model training. Our integrated model yielded an accuracy of 72% in sleep stage classification, and 71% in OSA screening based on the PSD features in automatically classified rapid eye movement (REM) and non-REM stages (N1, N2, N3). The current outcomes demonstrate the feasibility of AI-based computational studies that could contribute to personalized medicine by not only assessing an individual’s sleep status conveniently at home but also alerting them to the risk of sleep disorders and enabling early intervention, combined with advances in wearable devices and relevant technologies.