Sleep Staging Model

Sleep staging, the process of classifying different sleep stages (e.g., REM, NREM), is crucial for diagnosing sleep disorders and assessing sleep quality. Current research focuses on improving the accuracy and efficiency of automated sleep staging models, particularly using readily available data sources like photoplethysmography (PPG) and single-channel EEG, and employing advanced architectures such as recurrent neural networks (RNNs) and graph neural networks to capture both temporal and spatial features within sleep data. These advancements aim to create more accessible and reliable sleep analysis tools, ultimately improving the diagnosis and treatment of sleep-related conditions. The development of robust models that account for long-term temporal dependencies within sleep cycles is a key area of ongoing investigation.