부산대학교 도서관

In order to diagnose sleep problems, it is critical to correctly identify sleep stages which is a labor-intensive task. Due to rising data volumes, advanced algorithms, and improvements in computational power and storage, artificial intelligence has been more popular in recent years. Automated sleep staging through cardiac rhythm is one of the active research areas that has gained attention over the last decade. In this study, we review four recent state-of-the-art deep learning methods for automated sleep staging, datasets developed in recent years, and discuss their performance evaluations.