BIOMEDICAL PARAMETER PROBABILISTIC ESTIMATION METHOD AND APPARATUS

• US 20120022384A1
• Filed: 04/28/2011
• Published: 01/26/2012
• Est. Priority Date: 04/22/2009
• Status: Active Grant

• Current Assignee: Vital Metrix Inc.
• Original Assignee: Streamline Automation LLC
• Inventors: Teixeira, Rodrigo
• Granted Patent: US 9,649,036 B2
• Time in Patent Office: Days
• US Class Current: 600/509
• CPC Class Codes: A61B 5/0205 Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
• CPC Class Codes: A61B 5/14551 for measuring blood gases
• CPC Class Codes: A61B 5/318 Heart-related electrical modalities, e.g. electrocardiography [ECG]
• CPC Class Codes: A61B 5/7203 for noise prevention, reduction or removal
• CPC Class Codes: A61B 5/7214 using signal cancellation, e.g. based on input of two identical physiological sensors spaced apart, or based on two signals derived from the same sensor, for different optical wavelengths
• CPC Class Codes: A61B 5/7278 Artificial waveform generation or derivation, e.g. synthesising signals from measured signals A61B5/327 takes precedence

Abstract

A probabilistic digital signal processor is described. Initial probability distribution functions are input to a dynamic state-space model, which operates on state and/or model probability distribution functions to generate a prior probability distribution function, which is input to a probabilistic updater. The probabilistic updater integrates sensor data with the prior to generate a posterior probability distribution function passed (1) to a probabilistic sampler, which estimates one or more parameters using the posterior, which is output or re-sampled in an iterative algorithm or (2) iteratively to the dynamic state-space model. For example, the probabilistic processor operates using a physical model on data from a mechanical system or a medical meter or instrument, such as an electrocardiogram. Output of the physical model yields an enhanced output of the original data, an output to a second physical parameter not output by the medical meter, or a prediction, such as an arrhythmia warning.

Claims

1. An apparatus for electrodynamic analysis of a body, comprising:

a digital signal processor integrated into a biomedical device, said digital signal processor configured to iteratively use;

an electrodynamics dynamic state-space model configured to provide a prior probability distribution function;

a probabilistic processor configured to produce a posterior probability distribution function using both;

(1) the prior probability distribution function and (2) electrodynamic signal originating in the body, wherein said electrodynamics dynamic state-space model comprises;

a process model, wherein said process model comprises a model describing electric charge transfer in the body; and

an observation model, wherein said observation model models variation in the electrodynamic signal resultant from sensor movement with a probability distribution function.

2. The apparatus of claim 1, said process model further comprising:

a heart electrodynamics model.

3. The apparatus of claim 1, wherein the electrodynamic signal comprises output of an electrocardiograph device.

4. The apparatus of claim 1, said digital signal processor configured to generate an output, wherein the output comprises a prognosis of a heart attack.

5. The apparatus of claim 1, said digital signal processor configured to generate an output, wherein the output comprises an arrhythmia detection based upon a model measuring contractility.

6. The apparatus of claim 1, said probabilistic processor configured to process the electrodynamic signal using both a sequential Monte Carlo algorithm and Bayes rule.

7. The apparatus of claim 1, said process model further comprising:

a probabilistic model.

8. The apparatus of claim 1, wherein the electrodynamic signal comprises a deterministic state reading of the body, said probabilistic processor configured to convert the deterministic state reading into a probability distribution function, said system processor configured to provide an output probability distribution function representative of state of health of the body.

9. The apparatus of claim 1, wherein the electrodynamic signal comprises at least one of:

raw sensor data related to a first physical parameter; and

an output related to a provided physical parameter,

wherein output correlated with the posterior probability distribution function relates to a second physical parameter calculated using said process model, wherein the second physical parameter is to a physical parameter other than the first physical parameter and the provided physical parameter.

10. The apparatus of claim 1, said process model of said dynamic state-space model further comprising:

a physics based model configured to represent movement of the electrodynamic signal in at least a chest of the body.

17. The method of claim 1, wherein the electrodynamic signal comprises at least one of:

raw sensor data related to a first physical parameter; and

an output related to a provided first physical parameter,

wherein output correlated with the posterior probability distribution function relates to a second physical parameter calculated using said process model, wherein the second physical parameter is to a physical parameter other than the first physical parameter and the provided physical parameter.

18. The method of claim 17, wherein said prior probability distribution function comprises a distribution of at least one of:

voltage values;

current values;

electrical impedances; and

electrical resistance values.

11. A method for electrodynamic analysis of a body, comprising the steps of:

providing a digital signal processor integrated into a biomedical device, said digital signal processor configured with;

an electrodynamics dynamic state-space model; and

a probabilistic processor;

generating a prior probability distribution function using said electrodynamics dynamic state-space model; and

using said probabilistic processor to produce a posterior probability distribution function, said probabilistic processor provided with both;

the prior probability distribution function; and

electrodynamic signal originating in the body,

wherein said electrodynamics dynamic state-space model comprises;

a process model configured to describe electric charge transfer in the body; and

an observation model configured to model variation in the electrodynamic signal, resultant from sensor movement, with a probability distribution function.

12. The method of claim 11, further comprising the step of:

fusing the electrodynamics signal generated with a first sensor with a second data stream generated using a second sensor, wherein the second data stream relates to a hemodynamics process.

13. The method of claim 11, said electrodynamics dynamic state-space model further comprising the step of:

an electrical wave propagation model modeling an electric charge moving at least in a chest of the body.

14. The method of claim 11, said process model further comprising the step of:

using a physical model to relate the electrodynamics signal with any of;

a heart atrium; and

a heart ventricle.

15. The method of claim 11, said process model further comprising the step of:

using said process model to represent at least a portion of a heart action potential.

16. The method of claim 11, wherein said process model comprises use of a model relating duration of a heart action potential to a heart beat cycle length.

19. An apparatus for electrodynamic analysis of a body, comprising:

a digital signal processor integrated into a biomedical device, said digital signal processor configured to;

iteratively provide a prior probability distribution function using a physical model representative of at least a component of the body;

produce a posterior probability distribution function using both;

(1) the prior probability distribution function and (2) electrodynamic signal originating in the body,

wherein said physical model comprises;

a process model configured to model electric charge transfer in the body; and

an observation model configured to model variation in the electrodynamic signal, resultant from sensor movement, with a probability distribution function.

20. The apparatus of claim 19, said biomedical device configured to provide a probabilistic output representative of state of a heart of the body.

21. The apparatus of claim 19, said biomedical device configured to provide an output, said output comprising a measure of heart rate variability.