Sensor fusion and probabilistic parameter estimation method and apparatus

• US 8,494,829 B2
• Filed: 07/12/2011
• Issued: 07/23/2013
• Est. Priority Date: 07/21/2010
• Status: Active Grant

• Current Assignee: Vital Metrix Inc.
• Original Assignee: Streamline Automation LLC
• Inventors: Teixeira, Rodrigo E.
• Primary Examiner(s): BORIN, MICHAEL L
• Application Number:
  US13/181,027
• Publication Number:
  US 20120022350A1
• Time in Patent Office: 742 Days
• Field of Search: None
• US Class Current: 703/11
• 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 using data from multiple instruments 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 from multiple instruments 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 on fused data using a physical model, where the data originates from a mechanical system or a medical meter or instrument, such as an electrocardiogram or pulse oximeter to generate new parameter information and/or enhanced parameter information.

Claims

1. A method for processing sensor data representative of a body, comprising the steps of:

using a physical model, representative of function of a body constituent, coded into a digital signal processor of an analyzer, wherein said physical model comprises the step of;

using a fitting constant related to at least one of age and gender;

generating a prior probability distribution function using said physical model;

repetitively fusing input data originating from at least a first medical instrument and a second medical instrument with the prior probability distribution function to generate a posterior probability distribution function; and

processing the posterior probability distribution function with said processor to generate an output of least one of;

a heart attack prediction; and

a heart stroke volume, wherein said step of processing uses at least one equation relating heart stroke volume to the posterior probability distribution function.

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

determining a noise artifact event through analysis of the input data from said first instrument; and

using said noise artifact event to filter the input data from said second instrument.

3. The method of claim 1, wherein said estimated parameter value comprises:

a metric to a parameter not output by either said first instrument or said second instrument.

4. The method of claim 3, wherein said metric comprises a permittivity.

5. The method of claim 1, wherein the input data from said first instrument comprises hemodynamic data outside of a heart of the body.

6. The method of claim 5, wherein the input data from said second instrument comprises electrodynamic data representative of an electrodynamic signal originating in the heart of the body.

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

using both the hemodynamic data and the electrodynamic signal to generate an estimate of the autonomic nervous system tone.

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

combining the input signal from said first instrument and said second instrument, said step of combining resulting in at least one of;

a generated measure of heart rate; and

a heart rate variability measure,

wherein the input data from said first instrument comprises a hemodynamic measure.