Multivariate Residual-Based Health Index for Human Health Monitoring

• US 20110172504A1
• Filed: 01/04/2011
• Published: 07/14/2011
• Est. Priority Date: 01/14/2010
• Status: Active Grant

• Current Assignee: Prolaio, Inc.
• Original Assignee: Venture Gain LLC
• Inventors: Wegerich, Stephan W.
• Granted Patent: US 8,620,591 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 5/021 Measuring pressure in heart or blood vessels A61B5/0205 takes precedence
• CPC Class Codes: A61B 5/024 Detecting, measuring or recording pulse rate or heart rate A61B5/0205, A61B5/021 take precedence
• CPC Class Codes: A61B 5/0809 by impedance pneumography
• CPC Class Codes: A61B 5/0816 Measuring devices for examining respiratory frequency measuring frequency of electric signals G01R23/00
• CPC Class Codes: A61B 5/091 Measuring volume of inspired or expired gases, e.g. to determine lung capacity
• CPC Class Codes: A61B 5/14551 for measuring blood gases
• CPC Class Codes: A61B 5/412 Detecting or monitoring sepsis
• CPC Class Codes: A61B 5/6898 Portable consumer electronic devices, e.g. music players, telephones, tablet computers
• 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 5/7275 Determining trends in physiological measurement data; Predicting development of a medical condition based on physiological measurements, e.g. determining a risk factor
• CPC Class Codes: A61B 5/7278 Artificial waveform generation or derivation, e.g. synthesising signals from measured signals A61B5/327 takes precedence
• CPC Class Codes: A61B 5/746 Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms alarms per se G08B
• 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: G16Z 99/00 Subject matter not provided for in other main groups of this subclass

Abstract

Ambulatory or in-hospital monitoring of patients is provided with early warning and prioritization, enabling proactive intervention and amelioration of both costs and risks of health care. Multivariate physiological parameters are estimated by empirical model to remove normal variation. Residuals are tested using a multivariate probability density function to provide a multivariate health index for prioritizing medical effort.

Claims

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

receiving sensor data from a human;

generating with a programmed microprocessor a plurality of features from said sensor data, characteristic of physiological health of said human;

estimating with a programmed microprocessor values for said features characteristic of normal human physiology using a multivariate model, based on the values of said generated plurality of features;

differencing with a programmed microprocessor the estimated values and the generated features to provide residuals for the features;

determining with a programmed microprocessor a likelihood that said residuals are representative of a pattern of normal residuals using a Gaussian mixture model based on a set of normal residual reference patterns; and

applying with a programmed microprocessor a test to said likelihood to render a decision whether the generated features are characteristic of normal physiological behavior to provide an early indication of deviation of the physiological health of said human from normal.

2. A method according to claim 1, wherein said step of applying a test comprises deriving an index of said likelihood as a logarithm of the inverse of said likelihood, and comparing the index to a threshold.

3. A method according to claim 1, comprising the further step of testing with a programmed microprocessor a series of said rendered decisions for persistence of decisions that the features are not characteristic of normal physiological behavior.

4. A method according to claim 1, wherein said step of estimating values further comprises making a kernel-based comparison of a feature vector, comprising the values of said feature signals, to at least some of a library of exemplary vectors, each comprising values representative of said feature signals in a known health state, in order to generate said estimate as a linear combination of those exemplary vectors, weighted in relation to said comparisons.

5. A method according to claim 4, wherein said feature vector is compared to said exemplary vectors comprising said library in order to select a subset of said exemplary vectors to use in said kernel-based comparison for generating said estimate.

6. A method according to claim 4, wherein said estimate is generated as a linear combination of said exemplary vectors, weighted in relation to said comparisons according to:

7. A method according to claim 4, wherein said estimate is generated as a linear combination of said exemplary vectors, weighted in relation to said comparisons according to:

8. A method according to claim 4, wherein said kernel-based comparison is of the form:

9. A method according to claim 4, wherein said kernel-based comparison is of the form:

10. A method according to claim 4, wherein said kernel-based comparison is of the form:

11. A method according to claim 4, wherein said kernel-based comparison is of the form:

12. A method according to claim 4, wherein said kernel-based comparison is of the form:

13. A method according to claim 4, wherein said kernel-based comparison is of the form:

14. A method according to claim 1, further comprising obtaining sensor data by making measurements of sensors embedded inside the monitored human in connection with an implanted cardiac device.

15. A method according to claim 1, wherein said step of receiving sensor data comprises receiving wireless transmissions via extremely local radio protocol of measurements of sensors attached to the monitored human.

16. A method according to claim 1, wherein said step of receiving sensor data comprises receiving data from a ventilator.

17. A method for monitoring the health of a human, comprising:

receiving sensor data from a human;

generating with a programmed microprocessor a plurality of features from said sensor data, characteristic of physiological health of said human;

estimating with a programmed microprocessor values for said features characteristic of normal human physiology using a multivariate model, based on the values of said generated plurality of features;

differencing with a programmed microprocessor the estimated values and the generated features to provide residuals for the features;

determining with a programmed microprocessor for each of a plurality of known health states, a likelihood that said residuals are representative of a pattern of residuals characteristic of that known health state, using a Gaussian mixture model based on a set of residual reference patterns for the known health state; and

applying with a programmed microprocessor a test to the plurality of likelihoods to render a ranking of which of said plurality of known health states the generated features are most characteristic of.

18. A method according to claim 17, wherein said step of estimating values further comprises making a kernel-based comparison of a feature vector, comprising the values of said feature signals, to at least some of a library of exemplary vectors, each comprising values representative of said feature signals in a known health state, in order to generate said estimate as a linear combination of those exemplary vectors, weighted in relation to said comparisons.

19. A method according to claim 18, wherein said feature vector is compared to said exemplary vectors comprising said library in order to select a subset of said exemplary vectors to use in said kernel-based comparison for generating said estimate.

20. A method according to claim 18, wherein said estimate is generated as a linear combination of said exemplary vectors, weighted in relation to said comparisons according to:

21. A method according to claim 18, wherein said estimate is generated as a linear combination of said exemplary vectors, weighted in relation to said comparisons according to:

22. A system for monitoring the health of a human, comprising:

a device disposed to receive multiple physiological signals from a plurality of sensors arranged to capture physiological signals from a monitored human, having microprocessor hardware programmed to derive multiple physiological features from said signals;

a computer configured to receive monitored observations of said multiple physiological features from said device, to generate estimates of said features in the monitored observations using a model embodying behavior of said multiple physiological features under normal health conditions, and to generate residuals of said features by differencing the monitored observations with the estimates thereof; and

a computer-accessible memory for storing a set of exemplary observations of residuals of said multiple physiological features characteristic of a known health state, generated using said model;

said computer being specially configured to determine a likelihood that said residuals are representative of a pattern of residuals for said known health state, using a Gaussian mixture model based on said set of exemplary observations of residuals.

23. A system according to claim 22, wherein said model is a kernel regression estimator.

24. A system according to claim 23, wherein said the monitored observation of features is used to localize said model.

25. A system according to claim 22, wherein said model is a similarity-based model.

26. A system according to claim 25, wherein said the monitored observation of features is used to localize said model.

27. A system according to claim 22, wherein said computer is further specially configured to test said likelihood to render a decision whether the monitored observation of said multiple features is characteristic of said known health state.

28. A system according to claim 27, wherein said computer is further specially configured to test a series of said rendered decisions for persistence of like decisions regarding whether the features are characteristic of said known health state or not.

29. A system according to claim 22, wherein said computer is further specially configured to generate an index of said likelihood as a logarithm of the inverse of said likelihood, and compare the index to a threshold.

30. A system according to claim 22, wherein said device is a cell phone.

31. A system according to claim 22, wherein said device is a hospital bedside vital signs monitor.

32. A system according to claim 22, wherein said known health state is the state of normal health for the monitored human.