Blood pressure-cerebral oxygen coupling model: A new approach for stroke risk prediction

Stroke is a major cause of death and disability worldwide, but predicting its risk remains challenging. This study aimed to evaluate the cerebral blood flow autoregulation function of subjects with different stroke risk levels and predict their stroke risk. The coupling strength between cerebral oxygen and blood pressure signals was calculated by wavelet analysis and dynamic Bayesian inference and used as a quantitative index of cerebral blood flow autoregulation. A stroke prediction model based on the extreme random tree was constructed using the coupling strength and other data as input features. The results showed that the coupling strength was significantly higher in the high-risk group than the other groups. Moreover, the prediction model achieved an average accuracy of 0.80 across the three groups. The coupling strength of cerebral oxygen and blood pressure can be used as an objective index to predict stroke risk, which has implications for stroke prevention and intervention.