Residual-Based Monitoring of Human Health

• US 20070149862A1
• Filed: 11/29/2006
• Published: 06/28/2007
• Est. Priority Date: 11/29/2005
• Status: Active Grant

• Current Assignee: Prolaio, Inc.
• Original Assignee: Venture Gain LLC
• Inventors: Pipke, Robert
• Granted Patent: US 8,795,170 B2
• Time in Patent Office: Days
• US Class Current: 600/301
• 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 5/02055 Simultaneously evaluating both cardiovascular condition and temperature
• CPC Class Codes: A61B 5/021 Measuring pressure in heart or blood vessels A61B5/0205 takes precedence
• CPC Class Codes: A61B 5/02455 provided with high/low alarm devices
• CPC Class Codes: A61B 5/0816 Measuring devices for examining respiratory frequency measuring frequency of electric signals G01R23/00
• CPC Class Codes: A61B 5/11 Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb for measuring pulse A61B5/02 ;A61B5/1038 takes precedence; motion detection to correct for motion artifacts in physiological signals A61B5/721
• CPC Class Codes: A61B 5/145 Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissuemeasuring of blood pressure or blood flow A61B5/02; non-radiation detecting or locating of foreign bodies in blood A61B5/06
• CPC Class Codes: A61B 5/14532 for measuring glucose, e.g. by tissue impedance measurement
• 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 5/352 Detecting R peaks, e.g. for synchronising diagnostic apparatus; Estimating R-R interval
• CPC Class Codes: A61B 5/7264 Classification of physiological signals or data, e.g. using neural networks, statistical classifiers, expert systems or fuzzy systems neural networks per se G06N3/00; expert systems per se G06N5/00
• CPC Class Codes: A61B 5/7267 involving training the classification device
• CPC Class Codes: G06F 18/22 Matching criteria, e.g. proximity measures
• CPC Class Codes: G06F 9/545 where tasks reside in different layers, e.g. user- and kernel-space
• CPC Class Codes: G16B 5/00 ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic networks
• CPC Class Codes: G16B 5/30 Dynamic-time models
• CPC Class Codes: G16B 99/00 Subject matter not provided for in other groups of this subclass
• CPC Class Codes: G16H 50/20 for computer-aided diagnosis, e.g. based on medical expert systems
• CPC Class Codes: G16H 50/30 for calculating health indices; for individual health risk assessment
• CPC Class Codes: G16H 50/50 : for simulation or modelling of medical disorders
• CPC Class Codes: G16H 50/70 : for mining of medical data, e.g. analysing previous cases of other patients

Abstract

Improved human health monitoring is provided in the context of sensor measurements of typical vital signs and other biological parameters, by a system and method using an empirical model of the parameters and disposed to estimate values of the parameters in response to actual measurements. Residuals resulting from the difference between the estimates and actual measurements are analyzed for robust indications of incipient health issues. Residual analysis is both more robust and more sensitive than conventional univariate range checking on vital signs.

Claims

1. A method for monitoring the health status of a human, comprising the steps of:

obtaining an observation of multiple vital signs from the human;

providing a kernel-based model characteristic of the behavior of at least some of said multiple vital signs under known health conditions;

generating an estimate with said kernel-based model of at least one of said multiple vital signs, based on said observation;

comparing said estimate to the corresponding measured value of the at least one of said multiple vital signs in said observation; and

making a determination of the health status of the human from said comparison.

2. A method according to claim 1, wherein said kernel-based model comprises a set of exemplary observations of said multiple vital signs, and generates said estimate as a linear combination of at least some of the exemplary observations.

3. A method according to claim 2, wherein said observation of multiple vital signs is used to localize said kernel-based model so that a subset of said exemplary observations is used to generate said estimate.

4. A method according to claim 1, wherein said comparison yields a residual value which is compared to a threshold in order to make said determination of the health status of the human.

5. A method according to claim 4, wherein the determination of the health status of the human is made based on a sequence of comparisons of said residual value to said threshold for successive said observations of multiple vital signs.

6. A method according to claim 5, wherein the sequence of comparisons is analyzed by counting how many times the residual exceeded the threshold.

7. A method according to claim 2, wherein said kernel-based model is a similarity-based model.

8. A method according to claim 2, wherein said kernel-based model is a kernel regression estimator.

9. A method according to claim 2, wherein said kernel-based model is a radial basis function network.

10. A method according to claim 2, wherein the at least one of said multiple vital signs for which said estimate is generated is not provided as an input to said kernel-based model, but is present in said exemplary observations.

14. A system according to claim 1, wherein said residual analysis software module compares said residual to a threshold in order to make said determination of the health status of the human.

11. A computer software system for monitoring the health status of a human, comprising:

a data feed for providing observations of multiple vital signs from the human being monitored;

a kernel-based modeling software module characteristic of the behavior of at least some of said multiple vital signs under known health conditions, for receiving a said observation from said data feed, and generating an estimate of at least one of said multiple vital signs, based on said received observation; and

a residual analysis software module disposed to compare said estimate to the corresponding measured value of the at least one of said multiple vital signs in said received observation to generate a residual, and make a determination of the health status of the human therefrom.

12. A system according to claim 11, wherein said kernel-based modeling software module comprises a set of exemplary observations of said multiple vital signs, and generates said estimate as a linear combination of at least some of the exemplary observations.

13. A system according to claim 12, wherein said observation of multiple vital signs is used for localization by said kernel-based modeling software module so that a subset of said exemplary observations is used to generate said estimate.

15. A system according to claim 12, wherein said kernel-based modeling software module implements a similarity-based model.

16. A system according to claim 12, wherein said kernel-based modeling software module implements a kernel regression estimator.

17. A system according to claim 12, wherein said kernel-based modeling software module implements a radial basis function network.