Sleep Behavior

Research on sleep behavior focuses on understanding its multifaceted nature and impact on health, particularly concerning mental well-being and the prediction of conditions like psychotic relapses. Current studies employ machine learning techniques, including neural networks and graph-based models like attention-enhanced networks, to analyze diverse data sources such as physiological signals (e.g., heart rate, electrocorticograms), mobile phone usage, and social network interactions to predict sleep patterns and identify individuals at risk. These advancements offer the potential for improved sleep management strategies and earlier detection of mental health issues through objective, non-invasive methods. The development of personalized sleep recommendations based on individual behavioral data also represents a significant area of progress.