| Title |
Sleep Stage Classification using Deep Learning Techniques: A Review |
| Authors |
(Jiyong Park) ; (Jaewon An) ; (Sang Ho Choi) |
| DOI |
https://doi.org/10.5573/IEIESPC.2023.12.1.30 |
| Keywords |
Sleep stage classification; Deep learning; Biosignals; Non-contact sensing |
| Abstract |
Sleep is an essential time for body recovery and healthy living. Therefore, sleep monitoring for health management is important. The gold-standard method for evaluating sleep is polysomnography (PSG), and physicians score the sleep stages using night PSG recording data.
However, scoring sleep stages requires considerable time and labor. Hence, more accessible and efficient sleep-scoring methods are required. Because sleep stage information provides significant information for healthcare, studies of automatic sleep scoring have been conducted to overcome the limitations of PSG. This study reviews the progress and challenges of single- and multi-biosignalbased deep learning approaches to classify the sleep stages. In addition, non-contact sensor-based methods are reviewed for long-term monitoring at home. |