Residual-based monitoring of human health

• US 8,597,185 B2
• Filed: 01/31/2011
• Issued: 12/03/2013
• Est. Priority Date: 11/29/2005
• Status: Active Grant

• Current Assignee: Prolaio, Inc.
• Original Assignee: Ventura GAIN LLC
• Inventors: Pipke, Robert Matthew
• Primary Examiner(s): Thomson, Bill
• Assistant Examiner(s): JIAN, SHIRLEY XUEYING
• Application Number:
  US13/017,239
• Publication Number:
  US 20110124982A1
• Time in Patent Office: 1,037 Days
• Field of Search: 600300-301, 706/15, 706/26
• 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 system for monitoring the health status of a human, comprising:

an ambulatory device disposed to receive multiple biological parameters from a plurality of sensors configured to be attached to a monitored human;

a computer disposed to obtain monitored observations of multiple biological parameters from said ambulatory device; and

a computer-accessible memory for storing a set of exemplary observations of said multiple biological parameters characteristic of the interrelationships between said multiple biological parameters under normal health conditions for the monitored human;

said computer being configured to generate an estimate of at least one of said multiple biological parameters in a monitored observation obtained from said ambulatory device, as a linear combination of at least some of said set of exemplary observations, determined from a kernel-based comparison of the monitored observation and at least some exemplary observations; and

said computer being further configured to compare said estimate to the corresponding value of the at least one of said multiple biological parameters in said monitored observation, to generate a residual, and make a determination of the health status of the human there from.

2. The system according to claim 1, wherein said ambulatory device communicates with said sensors by means of local wireless communications.

3. The system according to claim 2, wherein said local wireless communications is IEEE 802.15.1.

4. The system according to claim 1, wherein said plurality of sensors includes an oximeter.

5. The system according to claim 1, wherein said plurality of sensors includes an optical blood glucose meter.

6. The system according to claim 1, wherein said plurality of sensors includes an accelerometer.

7. The system according to claim 1, wherein said plurality of sensors includes at least a pair of temperature sensors disposed to measure ambient temperature and a temperature of the monitored human.

8. The system according to claim 1, wherein said ambulatory device comprises a cell radio and said observations of multiple biological parameters are communicated to said computer at least in part over a cellular communications network.

9. The system according to claim 1, wherein said ambulatory device records said multiple biological parameters and transfers them periodically upon being attached by a cable to an internet-connected appliance, and said appliance is configured to upload said multiple biological parameters to said computer over an internet connection.

10. The system according to claim 1, wherein said set of exemplary observations of said multiple biological parameters comprises at least some observations previously obtained from the monitored human under normal health conditions using such an ambulatory device.

11. The system according to claim 1, wherein said computer is further configured to use said monitored observation for localization so that a subset of said exemplary observations is used to generate said estimate.

12. The system according to claim 11, wherein said computer is further configured to determine said localized subset by performing a kernel-based comparison of said monitored observation with said exemplary observations.

13. The system according to claim 11, wherein said computer is further configured to determine said localized subset by comparing said monitored observation with predetermined clusters of said exemplary observations.

14. The system according to claim 1, wherein said computer is further configured with the capability to adapt said stored set of exemplary observations of said multiple biological parameters to include a monitored observation.

15. The system according to claim 14, wherein said computer is further configured to test residuals and provide notifications based thereon to a web page interface that enables a user of the system to identify any notification as corresponding to data from which at least one monitored observation should be used to adapt said stored set of exemplary observations.

16. The system according to claim 1, wherein said computer is further configured to test residuals with at least one threshold and generate notifications of health status based thereon.

17. The system according to claim 1, wherein said computer is further configured to test sequences of residuals to determine if a threshold is exceeded by a residual at least a selected number of times over said sequence and generate notifications of health status based thereon.

18. The system according to claim 1, wherein said computer is further configured to test sequences of residuals with a sequential probability ratio test and generate notifications of health status based thereon.

19. The system according to claim 1, wherein said computer is further configured to test for patterns of residuals with thresholds and generate notifications of health status based thereon.

20. The system according to claim 1, wherein said computer is further configured to test residuals and provide notifications based thereon to a web page interface that enables a user of the system to annotate any notification as corresponding to one of the set of statuses selected from the group consisting of (a) the notification is being monitored and investigated;

(b) the notification is overruled; and

(c) the notification is confirmed.

21. The system according to claim 1, wherein said monitored observation is first tested for being characteristic of at least one activity state before an estimate is generated.

22. The system according to claim 21, wherein the at least one activity state includes a state of resting.

23. The system according to claim 21, wherein the at least one activity state includes a state of sleeping.

24. The system according to claim 1, wherein said monitored observation is excluded from use in monitoring the health status of the human if the observation coincides with an interval which exhibits a disrupted respiratory pattern.

25. The system according to claim 1, wherein said kernel-based comparison of the monitored observation and at least some exemplary observations uses a kernel that determines its output value as a function of a norm of the vector difference between its multivariate input observations treated as vectors.

26. The system according to claim 1, wherein said kernel-based comparison of the monitored observation and at least some exemplary observations uses a kernel that determines its output value as a function of respective differences between like elements of its multivariate input observations.

27. The system according to claim 1, wherein said multiple biological parameters include a measure of heart rate variability.

28. The system according to claim 1, wherein said multiple biological parameters include a measure of a time lag between a respiratory activity and resulting oxygenation changes in the blood.

29. The system according to claim 1, wherein said multiple biological parameters include a measure of a time lag between a QRS event and a subsequent blood pressure event.

30. The system according to claim 1, wherein said multiple biological parameters include a measure of the difference between the ambient air temperature and the temperature of a location of the body.

31. The system according to claim 1, wherein said multiple biological parameters include to measure of breathing depth.

32. The system according to claim 1, wherein said observations of multiple biological parameters are made every 1 minute, and the multiple biological parameters comprise at least a measure of average heart rate, a measure of average respiration rate, and a measure of average blood pressure.