Apparatus for processing physiological sensor data using a physiological model and method of operation therefor
OwnerRodrigo E. Teixeirainventor
Life of this patent
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Abstract
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.
Claims20
An apparatus for generation of a physiological estimate of a physiological process of an individual from input data, comprising:
- a biomedical monitoring device comprising a data processor configured to run a dual estimation algorithm, said biomedical monitoring device configured to produce the input data, wherein the input data comprises at least one of:
- a photoplethysmogram;
- and
- an electrocardiogram,
- said dual estimation algorithm configured to use a dynamic state-space model to operate on the input data using both an iterative state estimator and an iterative model parameter estimator in generation of said physiological estimate, said dynamic state-space model configured to:
- mathematically represent probabilities of physiological processes that generate said physiological estimate and mathematically represent probabilities of physical processes that affect collection of the input data.
A method for generating a physiological estimate from input data, comprising the steps of:
- generating input data using a biomedical monitoring device comprising a data processor configured to run a dual estimation algorithm, wherein the input data comprises at least one of:
- a photoplethysmogram;
- and
- an electrocardiogram,
- processing said input data using said dual estimation algorithm, said dual estimation algorithm configured to use a dynamic state-space model to operate on the input data using both an iterative state estimator and an iterative model parameter estimator in generation of said physiological estimate, said dynamic state-space model configured to:
- mathematically represent probabilities of physiological processes that generate said physiological estimate and mathematically represent probabilities of physical processes that affect collection of the input data.
Dependencies are inferred from the claim text.