Sleep Stage

Sleep stage classification, the process of identifying different sleep stages (e.g., wake, REM, N1-N3) from physiological signals, aims to improve sleep disorder diagnosis and monitor sleep quality. Current research heavily utilizes deep learning, employing architectures like convolutional neural networks (CNNs), transformers, and recurrent neural networks (RNNs), often combined with techniques such as self-supervised learning and multimodal data integration to enhance accuracy and efficiency. These advancements are driving the development of more accessible and reliable sleep monitoring tools, potentially impacting both clinical practice and personalized sleep health management.