PROCESSING PHYSIOLOGICAL SENSOR DATA USING A PHYSIOLOGICAL MODEL COMBINED WITH A PROBABILISTIC PROCESSOR

摘要

A pulse oximeter system comprises a data processor configured to perform a method that combines a sigma point Kalman filter (SPKF) or sequential Monte Carlo (SMC) algorithm with Bayesian statistics and a mathematical model comprising a cardiovascular model and a plethysmography model to remove contaminating noise and artifacts from the pulse oximeter sensor output and measure blood oxygen saturation, heart rate, left-ventricular stroke volume, aortic pressure and systemic pressures.