Sleep Stage Classification

Sleep stage classification aims to automatically identify different sleep stages (e.g., REM, NREM) from physiological signals, primarily EEG, but increasingly incorporating ECG, respiratory, and even video data. Current research emphasizes developing robust and accurate classification models using various deep learning architectures like convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, often incorporating multimodal data fusion and self-supervised learning techniques to address data scarcity and improve generalizability. This automated approach offers significant potential to improve the efficiency and objectivity of sleep studies, facilitating better diagnosis and treatment of sleep disorders and advancing our understanding of sleep's role in health.