Residual-based monitoring of human health

• US 9,743,888 B2
• Filed: 06/20/2014
• Issued: 08/29/2017
• Est. Priority Date: 11/29/2005
• Status: Active Grant

• Current Assignee: Prolaio, Inc.
• Original Assignee: Venture Gain LLC
• Inventors: Pipke, Robert Matthew
• Primary Examiner(s): Thomson, William
• Assistant Examiner(s): JIAN, SHIRLEY XUEYING
• Application Number:
  US14/310,867
• Publication Number:
  US 20140303457A1
• Time in Patent Office: 1,166 Days
• Field of Search: 600300-301
• 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 computer-automated monitoring system for early detection of incipient health problems in a monitored patient, comprising:

a computer-accessible memory for storing a set of exemplary data observations of multiple vital signs obtained from said monitored patient comprising a personalized model of said patient;

a data feed for receiving new data observations of said multiple vital signs from said monitored patient;

a computer configured by a kernel-based modeling software module to generate an estimate of the value of at least one of said multiple vital signs, as a weighted combination of a subset of said exemplary data observations where said weights are determined from a kernel function comparison of said subset of exemplary data observations and a said received observation;

said computer being further configured by a localization software module to select said subset of exemplary data observations based on at least some of the vital signs in said received observation;

said computer being further configured by a residual analysis software module to compare said estimate to the corresponding measured value of the at least one of said multiple vital signs in said received data observation to generate a residual; and

said computer being further configured by an alerting module to trigger an alert when said generated residual exceeds a threshold, and present said alert in a web-based interface usable by a medical practitioner for monitoring said patient for incipient health problems.

2. A system according to claim 1, wherein said kernel-based modeling software module implements a similarity-based model.

3. A system according to claim 1, wherein said kernel-based modeling software module implements a kernel regression estimator.

4. A system according to claim 1, wherein said kernel-based modeling software module implements a radial basis function network.

5. A computer automated monitoring system according to claim 1, wherein said alert module generate an alert only when said residual exceeds said threshold for a preselected number of data observations in a preselected window.

6. A computer automated monitoring system according to claim 1, wherein said computer is further configured by an adaptation module disposed to add a received data observation to said set of exemplary data observations comprising said personalized model upon selection by a clinical user of an option to adapt said model responsive to an alert.