Apparatus for processing physiological sensor data using a physiological model and method of operation therefor

• US 9,060,722 B2
• Filed: 06/08/2010
• Issued: 06/23/2015
• Est. Priority Date: 04/22/2009
• Status: Active Grant

• Current Assignee: Vital Metrix Inc.
• Original Assignee: Streamline Automation LLC
• Inventors: Teixeira, Rodrigo E.
• Primary Examiner(s): Kahelin, Michael
• Assistant Examiner(s): Alter, Mitchell E
• Application Number:
  US12/796,512
• Publication Number:
  US 20110301436A1
• Time in Patent Office: 1,841 Days
• US Class Current: 1/1
• CPC Class Codes: A61B 5/14552 Details of sensors specially adapted therefor
• CPC Class Codes: A61B 5/318 Heart-related electrical modalities, e.g. electrocardiography [ECG]
• CPC Class Codes: A61B 5/7267 involving training the classification device

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.

Claims

1. 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;

said state estimator iteratively;

operating on an initial input probability distribution function or a re-sampled probability distribution function using said dynamic state-space model to generate a second probability distribution function;

applying Bayes rule to said second probability distribution function and the input data to form a third probability distribution function; and

re-sampling said third probability distribution function as said re-sampled probability distribution function;

generating said physiological estimate using said model parameter estimator, said physiological estimate comprising;

a blood oxygen saturation; and

a left-ventricular stroke volume; and

warning of a hemorrhage using said physiological estimate.

2. The method of claim 1, further comprising the step of:

said dynamic state-space model processing said second probability distribution function using a sequential Monte Carlo algorithm.

3. The method of claim 2, further comprising the steps of said model parameter estimator iteratively:

providing an initial model parameter or providing an updated model parameter as an input to said dynamic state-space model to generate a current value for said physiological estimate;

using said third probability function and said current model parameter to form an expectation model; and

combining said expectation model with the input data using unsupervised machine learning to generate said updated model parameter.

4. The method of claim 3, further providing an age input and a gender input in generation of said expectation model.

5. The method of claim 3, wherein said expectation model comprises a confidence interval representing an accuracy probability of said current model parameter.

6. The method of claim 1, further comprising the step of:

combining into the input data at least two of;

said plethysmogram;

said electrocardiogram; and

blood pressure monitor data.