IP Library › Granted Patent US 12,298,757
Granted Patent B2
US 12,298,757 · App. 17/286,303 · Granted May 13, 2025

Method and apparatus for event prediction for a physical system based on data solely collected from the physical system

Inventors: Asim Roy (Phoenix, AZ); Phillip Schulte (Rochester, MN); Lyle J. Olson (Rochester, MN); Charles J. Bruce (Ponte Vedra Beach, FL)
Assignee: Arizona Board of Regents on behalf of Arizona State University
G05B23/0283G06F11/3013G06F11/3452
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Quick Facts
Patent No.
US 12,298,757
App. No.
17/286,303
Granted
May 13, 2025
Kind
B2
Abstract

A physical system receives and records measurements from a plurality of sensors for the physical system over a period of time and creates therefrom a statistical model of normal behavior of the physical system. The statistical model is applied to monitor the physical system for events or significant changes. The method detects events or significant changes in the operation or behavior of the physical system responsive to the monitoring of the physical system for events or significant changes. Events or significant changes in the operation or behavior of the physical system may cause a notification or alert, which can be sent a supervisory system.

Claims (50)

1. A computer-implemented method performed by a monitoring system having at least a processor and a memory therein to execute instructions for detecting events about, or changes in, a decompensated heart failure (DHF) patient, the computer-implemented method comprising:

measuring, via a plurality of different types of sensors coupled in communication with the monitoring system and proximate the DHF patient, a corresponding plurality of different characteristics of the DHF patient, over a first period of time;

collecting at the monitoring system a continual stream of data from the plurality of different types of sensors comprising the measured corresponding plurality of different characteristics of the DHF patient over the first period of time;

creating in real-time at the monitoring system a unified statistical model of normal functioning of the DHF patient solely based on the collected continual stream of data from the plurality of different types of sensors comprising the measured corresponding plurality of different characteristics of the DHF patient over the first period of time;

monitoring via the monitoring system the DHF patient over a second, subsequent, period of time for events or changes outside the unified statistical model of normal functioning of the DHF patient by:

measuring via the plurality of different types of sensors a corresponding plurality of different characteristics of the DHF patient over the second period of time;

collecting at the monitoring system a continual stream of data from the plurality of different types of sensors comprising the measured corresponding plurality of different characteristics of the DHF patient over the second period of time; and

evaluating the DHF patient at the monitoring system solely based on:

the collected continual stream of data comprising the measured corresponding plurality of different characteristics of the DHF patient over the second period of time; and

the unified statistical model of normal functioning of the DHF patient;

detecting at the monitoring system events or changes outside the unified statistical model of normal functioning of the DHF patient responsive to monitoring the DHF patient over the second period of time for events or changes outside the unified statistical model of normal functioning of the DHF patient;

notifying via the monitoring system a supervisory system responsive to detecting at the monitoring system events or changes outside the unified statistical model of normal functioning of the DHF patient; and

medically responding to the DHF patient in response to the supervisory system receiving the notification.

2. The method of claim 1 , wherein collecting at the monitoring system the continual stream of data from the plurality of different types of sensors comprising the measured corresponding plurality of different characteristics of the DHF patient over the first period of time comprises collecting at the monitoring system a continual stream a respective plurality of time-series data comprising the measured corresponding plurality of different characteristics of the DHF patient over the first period of time.

3. The method of claim 2 , wherein creating in real-time at the monitoring system the unified statistical model of normal functioning of the DHF patient solely based on the collected continual stream of data from the plurality of different types of sensors comprising the measured corresponding plurality of different characteristics of the DHF patient over the first period of time comprises creating in real-time at the monitoring system the unified statistical model of normal functioning of the DHF patient solely based on the collected continual stream of the plurality of time-series data;

wherein collecting at the monitoring system a continual stream of data from the plurality of different types of sensors comprising the measured corresponding plurality of different characteristics of the DHF patient over the second period of time comprises collecting at the monitoring system a continual stream of a respective plurality time-series data from the plurality of different types of sensors comprising the measured corresponding plurality of different characteristics of the DHF patient over the second period of time; and

wherein evaluating the DHF patient at the monitoring system solely based on the collected continual stream of data comprising the measured corresponding plurality of different characteristics of the DHF patient over the second period of time, comprises evaluating the DHF patient at the monitoring system solely based on the collected continual stream of the plurality of time-series data comprising the measured corresponding plurality of different characteristics of the DHF patient over the second period of time.

4. The method of claim 3 , wherein collecting at the monitoring system the respective plurality of time-series data comprising the measured corresponding plurality of different characteristics of the DHF patient over the first period of time comprises receiving a total number, N, of time-series data comprising the measured corresponding plurality of different characteristics of the DHF patient over the first period of time.

5. The method of claim 4 , wherein creating in real-time at the monitoring system the unified statistical model of normal functioning of the DHF patient solely based on the collected continual stream of the plurality of time-series data comprises:

determining a duration, P;

recording average values of the plurality of time-series data, for the duration P, for a total, T, of such durations P, from the collected continual stream of data; and

creating a clustering model to record a distribution of the plurality of time-series data over for the period T times P using the T number of N time-series.

6. The method of claim 5 , wherein the clustering model comprises a Kohonen self-organizing map.

7. The method of claim 5 , wherein evaluating the DHF patient at the monitoring system solely based on the collected continual stream of the plurality of time-series data comprising the measured corresponding plurality of different characteristics of the DHF patient over the second period of time comprises applying the clustering model to monitor the DHF patient in subsequent time periods of length T times P using the N time-series data.

8. The method of claim 7 , wherein notifying via the monitoring system the supervisory system responsive to detecting at the monitoring system events or changes outside the unified statistical model of normal functioning of the DHF patient comprises notifying via the monitoring system the supervisory system responsive to detecting at the monitoring system deviations in the distribution of the time-series data.

9. A non-transitory computer readable storage media having instructions stored thereon that, when executed by a processor of a system, cause the system to perform operations comprising:

measuring via a plurality of different types of sensors coupled in communication with system and proximate a decompensated heart failure (DHF) patient, a corresponding plurality of different characteristics of the DHF patient over a first period of time;

collecting at the system a continual stream of data from the plurality of different types of sensors comprising the measured corresponding plurality of different characteristics of the DHF patient over the first period of time;

creating in real-time at the system a unified statistical model of normal functioning of the DHF patient solely based on the collected continual stream of data from the plurality of different types of sensors comprising the measured corresponding plurality of different characteristics of the DHF patient over the first period of time;

monitoring via the system the DHF patient over a second, subsequent, period of time, for events or changes outside the unified statistical model of normal functioning of the DHF patient by:

measuring via the plurality of different types of sensors a corresponding plurality of different characteristics of the DHF patient over the second period of time;

collecting at the monitoring system a continual stream of data from the plurality of different types of sensors comprising the measured corresponding plurality of different characteristics of the DHF patient over the second period of time; and

evaluating the DHF patient at the monitoring system solely based on:

the collected continual stream of data comprising the measured corresponding plurality of different characteristics of the DHF patient over the second period of time; and

the unified statistical model of normal functioning of the DHF patient;

detecting at the system events or changes outside the unified statistical model of normal functioning of the DHF patient responsive to monitoring the DHF patient over the second period of time for events or changes outside the statistical model of normal functioning of the DHF patient; and

notifying via the system a supervisory system responsive to detecting at the system events or significant outside the unified statistical model of normal functioning of the DHF patient; and

medically responding to the DHF patient in response to the supervisory system receiving the notification.

10. The non-transitory computer readable storage media of claim 9 , wherein collecting at the system the continual stream of data from the plurality of different types of sensors comprising the measured corresponding plurality of different characteristics of the DHF patient over the first period of time comprises collecting at the system a continual stream of a respective plurality of time-series data comprising the measured corresponding plurality of different characteristics of the DHF patient over the first period of time.

11. The non-transitory computer readable storage media of claim 10 , wherein creating in real-time at the system the unified statistical model of normal functioning of the DHF patient solely based on the collected continual stream of data from the plurality of different types of sensors comprising the measured corresponding plurality of different characteristics of the DHF patient over the first period of time comprises creating in real-time at the system the unified statistical model of normal functioning of the DHF patient solely based on the collected continual stream of the plurality of time-series data;

wherein collecting at the system a continual stream of data from the plurality of different types of sensors comprising the measured corresponding plurality of different characteristics of the DHF patient over the second period of time comprises collecting at the system a continual stream of a respective plurality time-series data from the plurality of different types of sensors comprising the measured corresponding plurality of different characteristics of the DHF patient over the second period of time; and

wherein evaluating the DHF patient at the system solely based on the collected continual stream of data comprising the measured corresponding plurality of different characteristics of the DHF patient over the second period of time, comprises evaluating the DHF patient at the system solely based on the collected continual stream of the plurality of time-series data comprising the measured corresponding plurality of different characteristics of the DHF patient over the second period of time.

12. The non-transitory computer readable storage media of claim 11 , wherein collecting at the system the respective plurality of time-series data comprising the measured corresponding plurality of different characteristics of the DHF patient over the first period of time comprises receiving a total number, N, of time-series data comprising the measured corresponding plurality of different characteristics of the DHF patient over the first period of time.

13. The non-transitory computer readable storage media of claim 12 , wherein creating in real-time at the system the unified statistical model of normal physical functioning of the DHF patient solely based on the collected continual stream of the plurality of time-series data comprises:

determining a duration, P;

recording average values of the plurality of time-series data, for the duration P, for a total, T, of such durations P, from the collected continual stream of data; and

creating a clustering model to record a distribution of the time-series data over for the period T times P using the T number of N time-series.

14. The non-transitory computer readable storage media of claim 13 , wherein creating a clustering model to record a distribution of the time-series data over for the period T times P using the T number of N time-series comprises creating a Kohonen self-organizing map to record a distribution of the time-series data over for the period T times P using the T number of N time-series.

15. The non-transitory computer readable storage media of claim 13 , wherein evaluating the DHF patient at the monitoring system solely based on the collected continual stream of the plurality of time-series data comprising the measured corresponding plurality of different characteristics of the DHF patient over the second period of time comprises applying the clustering model to monitor the DHF patient in subsequent time periods of length T times P using the N time-series data.

16. The non-transitory computer readable storage media of claim 15 , wherein notifying via the system the supervisory system responsive to detecting at the system events or changes outside the unified statistical model of normal functioning of the DHF patient comprises notifying via the system the supervisory system responsive to detecting at the system deviations in the distribution of the time-series data.

Assignments (2)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Aug 7, 2024
From: ROY, ASIM
To: ARIZONA BOARD OF REGENTS ON BEHALF OF ARIZONA STATE UNIVERSITY
Reel/Frame 068215/0424 →
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Aug 7, 2024
From: SCHULTE, PHILLIP; OLSON, LYLE J.; BRUCE, CHARLES J.
To: MAYO FOUNDATION FOR MEDICAL EDUCATION AND RESEARCH
Reel/Frame 068215/0454 →
Continuity (2)
Provisional Application 62747585 · Oct 18, 2018
Related Publication 20210382472A1 · Dec 9, 2021
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