GRAPH-BASED ANALYSIS FOR BIO-SIGNAL EVENT SENSING

• US 20170169591A1
• Filed: 12/11/2015
• Published: 06/15/2017
• Est. Priority Date: 12/11/2015
• Status: Active Application

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Chen, Chia-Yu, Hsueh, Pei-Yun S., Lai, Jui-Hsin, Xia, Yinglong
• Granted Patent: US 10,282,875 B2
• Time in Patent Office: Days
• CPC Class Codes: A61B 2503/12 Healthy persons not otherwise provided for, e.g. subjects of a marketing survey
• CPC Class Codes: A61B 2505/07 Home care
• CPC Class Codes: A61B 5/00 Measuring for diagnostic purposes radiation diagnosis A61B6/00; diagnosis by ultrasonic, sonic or infrasonic waves A61B8/00; Identification of persons
• CPC Class Codes: A61B 5/0022 Monitoring a patient using a global network, e.g. telephone networks, internet
• 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 5/024 Detecting, measuring or recording pulse rate or heart rate A61B5/0205, A61B5/021 take precedence
• CPC Class Codes: A61B 5/14542 for measuring blood gases A61B5/14551 takes precedence
• CPC Class Codes: A61B 5/316 Modalities, i.e. specific diagnostic methods
• CPC Class Codes: A61B 5/7221 Determining signal validity, reliability or quality A61B5/7207 and A61B5/7217 take precedence
• CPC Class Codes: A61B 5/7246 using correlation, e.g. template matching or determination of similarity
• 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 5/746 Alarms related to a physiological condition, e.g. details of setting alarm thresholds or avoiding false alarms alarms per se G08B
• CPC Class Codes: G06T 11/206 Drawing of charts or graphs

Abstract

One or more biological signals are obtained. The one or more biological signals are converted to one or more graph structures. Correlation between two or more of the biological signals are determined using the one or more graph structures. One or more changes in the one or more graph structures within a time window are recorded. A signal graph model is generated based on the recorded changes.

Claims

1. A method, comprising the steps of:

obtaining one or more biological signals;

converting the one or more biological signals into one or more graph structures;

determining correlation between two or more of the biological signals using the one or more graph structures;

recording one or more changes in the one or more graph structures within a time window; and

generating a signal graph model based on the recorded changes;

wherein the steps are performed by at least one processor device coupled to a memory.

2. The method of claim 1, further comprising comparing the one or more recorded changes to one or more patterns stored in a database.

3. The method of claim 2, further comprising generating at least one of a message and an alert in response to a match between the one or more recorded changes and a pattern for a medical condition stored in the database.

4. The method of claim 1, wherein the one or more biological signals are obtained in real time from one or more sensors attached to a subject.

5. The method of claim 4, wherein the one or more sensors are comprised within a wearable device.

6. The method of claim 4, wherein the one or more biological signals are converted into one or more graph structures in real time.

7. The method of claim 1, wherein the one or more biological signals are obtained from sensors attached to a plurality of subjects.

8. The method of claim 1, wherein each node of a given graph structure corresponds to one of the one or more biological signals.

9. The method of claim 1, wherein the correlation is an appearance correlation.

10. The method of claim 1, wherein the correlation is a temporal correlation.

11. The method of claim 1, wherein the correlation is a spatial correlation.

12. The method of claim 1, further comprising determining a confidence level for the correlation between the biological signals.

13. The method of claim 12, further comprising storing one or more signals comprising a correlation confidence level exceeding a specified correlation confidence threshold.

14. The method of claim 12, further comprising discarding one or more signals comprising a correlation confidence level below a specified correlation confidence threshold.

15. The method of claim 1, wherein recording one or more changes in the one or more graph structures comprises recording one or more changes in correlation between the one or more biological signals.

16. The method of claim 1, wherein the signal graph model comprises one or more dependency graphs.

17. The method of claim 16, further comprising displaying the signal graph model on one or more devices.

18. The method of claim 1, wherein the one or more graph structures are more compressed than the one or more biological signals.

19. A device comprising:

a memory and a processor operatively coupled to the memory and configured to implement the steps of;

obtaining one or more biological signals;

converting the one or more biological signals to one or more graph structures;

determining correlation between two or more of the biological signals using the one or more graph structures;

recording one or more changes in the one or more graph structures within a time window; and

generating a signal graph model based on the recorded changes.

20. A computer program product comprising a computer readable storage medium for storing computer readable program code which, when executed, causes a computer to:

obtain one or more biological signals;

convert the one or more biological signals to one or more graph structures;

determine correlation between two or more of the biological signals using the one or more graph structures;

record one or more changes in the one or more graph structures within a time window; and

generate a signal graph model based on the recorded changes.