A novel dynamic model to predict abnormal oxygen desaturations in blood

This paper presents a novel approach for modeling and predicting the dynamics of blood oxygenation signals (SpO₂). A performance metric based on a dynamically adjusted threshold is optimized to fit the SpO₂ predictive model by maximizing its ability to predict abnormal desaturation events. While we use a predictive model structure that is discrete time autoregressive, we formulate fitting this model structure as a mixed integer programming problem (MIP) and we optimize the models to maximize multi-step ahead predictions of abnormal events. Our results show that the proposed model is better able to capture the dynamic behavior for abnormal changes of SpO₂ compared to standard autoregressive models. This study provides a starting point for further needed investigation in prediction of physiological systems behavior.