[Advances in the detection of arousal in obstructive sleep apnea]

Abstract

Obstructive sleep apnea (OSA) is primarily characterized by intermittent nocturnal hypoxia and sleep fragmentation. Arousals interrupt sleep continuity and lead to sleep fragmentation, which can lead to cognitive dysfunction, excessive daytime sleepiness, and adverse cardiovascular outcome events, making arousals important for diagnosing OSA and reducing the risk of complications, including heart disease and cognitive impairment. Traditional arousal interpretation requires sleep specialists to manually score PSG recordings throughout the night, which is time consuming and has low inter-specialist agreement, so the search for simple, efficient, and reliable arousal detection methods can be a powerful tool to clinicians. In this paper, we systematically reviewed different methods for recognizing arousal in OSA patients, including autonomic markers (pulse conduction time, pulse wave amplitude, peripheral arterial tone, heart rate, etc.) and machine learning-based automated arousal detection systems, and found that autonomic markers may be more beneficial in certain subgroups, and that deep artificial networks will remain the main research method for automated arousal detection in the future.