We develop models and fusion rules for oximeters that detect the onset of hypoxia. Hypoxia is a medical condition affecting portions of the body that are deprived of oxygen supply. Prolonged exposure to cerebral oxygen deficiency can lead to unconsciousness or even death. The onset of hypoxia in humans is of concern for those operating in high altitudes, and in military flights characterized by high-acceleration maneuvers. Using oximeters for measuring blood oxygen saturation levels is a common means to detect hypoxia in real time. Many types of oximeters can be used for this task but all are prone to complicated noise characteristics and bias inaccuracies. It may therefore be advisable to collect and combine data streams from multiple oximeters for more reliable Hypoxia/No Hypoxia decisions (compared to decisions made by a single oximeter). Here we develop statistical noise models for three popular types of oximeters (Respironics Novametrix 515B, Nonin forehead pulse oximeter 9847, and Masimo Rad-87). We also combine data streams from these oximeters using a Kalman filter. The result is a smooth and reliable estimate of blood oxygen saturation level which can be used to detect the onset of Hypoxia.