This study aims to explore the relationship between the circadian rhythms of critically ill patients and the incidence of Status Epilepticus (SE), and to develop a predictive model based on circadian rhythm indicators and clinical factors. We conducted a diurnal rhythm analysis of vital sign data from 4413 patients, discovering significant differences in the circadian rhythms of body temperature, blood oxygen saturation, and heart rate between the SE and non-SE groups, which were correlated with the incidence of SE. We also employed various machine learning algorithms to identify the ten most significant variables and developed a predictive model with strong performance and clinical applicability. Our research provides a new perspective and methodology for the study of biological rhythms in critically ill patients, offering new evidence and tools for the prevention and treatment of SE. Our findings are consistent or similar to some in the literature, while differing from or supplementing others. We observed significant differences in the vital signs of epileptic patients at different times of the day across various diagnostic time groups, reflecting the regulatory effects of circadian rhythms. We suggest heightened monitoring and intervention of vital signs in critically ill patients, especially during late night to early morning hours, to reduce the risk of SE and provide more personalized treatment plans.
Keywords: Circadian rhythms; diurnal rhythm analysis; machine learning; predictive modeling; status epilepticus.