System, Method and Computer Program Product for Detection of Changes in Health Status and Risk of Imminent Illness

• US 20100324436A1
• Filed: 02/04/2009
• Published: 12/23/2010
• Est. Priority Date: 02/04/2008
• Status: Active Grant

• Current Assignee: University of Virginia Patent Foundation (University of Virginia)
• Original Assignee: University of Virginia Patent Foundation (University of Virginia)
• Inventors: Moorman, J. Randall, Lake, Douglas E.
• Granted Patent: US 8,588,908 B2
• Time in Patent Office: Days
• US Class Current: 600/515
• CPC Class Codes: A61B 5/0006 ECG or EEG signals
• CPC Class Codes: A61B 5/02405 Determining heart rate variability
• CPC Class Codes: A61B 5/0245 by using sensing means generating electric signals, i.e. ECG signals
• CPC Class Codes: A61B 5/316 Modalities, i.e. specific diagnostic methods
• CPC Class Codes: A61B 5/339 Displays specially adapted therefor
• CPC Class Codes: A61B 5/349 Detecting specific parameters of the electrocardiograph cycle
• CPC Class Codes: A61B 5/364 Detecting abnormal ECG interval, e.g. extrasystoles, ectopic heartbeats
• CPC Class Codes: A61B 5/412 Detecting or monitoring sepsis

Abstract

A method for analysis of cardiac rhythms and the clinical status of a patient, based on calculations of entropy and moments of time series intervals. An optimal determination is made of segments of data that demonstrate statistical homogeneity, specifically with regard to moments and entropy. The invention also involves calculating moments and entropy on each interval segments with the goal of diagnosis of cardiac rhythm. More specifically, an absolute entropy measurement is calculated, providing dynamic information of fundamental importance in diagnosis and analysis.

Claims

1. A method of detecting abnormal cardiac rhythms and clinical status of a patient comprising:

obtaining physiological data from a subject comprising one or more series of intervals;

calculating entropy data based the series of intervals; and

generating a diagnostic output based on the entropy data.

2. The method of claim 1, wherein the entropy data based on the series of intervals comprises numbers of matching intervals.

3. The method of claim 2, wherein generating the diagnostic output comprises using a regression model to combine the numbers of matching intervals.

4. The method of claim 1, wherein the physiological data comprises two or more series of intervals obtained simultaneously from different physiological signals.

5. The method of claim 1 further comprising separating the series of intervals into a plurality of subsets of the series of intervals.

6. The method of claim 5, wherein calculating the entropy data further comprises averaging entropy data from each of the plurality of the subsets of the series of intervals.

7. The method of claim 5, wherein the subsets of the series of intervals comprise less than 13 intervals.

8. The method of claim 1, wherein generating the diagnostic result further comprises using the coefficient of sample entropy (COSEn).

9. The method of claim 1 further comprising producing an output to a medical care provider to diagnose abnormal cardiac rhythms and clinical status of the subject.

10. The method of claim 1, wherein the interval data comprises a number of samples during one period of a cardiac rhythm.

11. The method of claim 1, wherein calculating entropy data based on the numbers of matching intervals further comprises a tolerance value (r) for determining whether two or more intervals match.

12. The method of claim 1, wherein the data from the cardiac rhythm of a patient comprises samples from an EKG waveform.

13. A method of detecting abnormal cardiac rhythms and clinical status of a patient comprising:

obtaining physiological data from a subject;

separating the physiological data into intervals;

grouping the intervals into one or more sets of segments of intervals;

determining numbers of matching intervals within each segment; and

generating a diagnostic output based on the numbers of matching intervals.

14. The method of claim 13, wherein the diagnostic output determines whether the subject has an abnormal cardiac rhythm and the subject'"'"'s clinical status.

15. The method of claim 13, wherein generating the diagnostic output further comprises running a regression model on the numbers of matching intervals.

16. The method of claim 13 further comprising calculating an absolute entropy measurement.

17. The method of claim 16, wherein the absolute entropy measurement is a coefficient of sample entropy (COSEn).

18. An apparatus for detecting abnormal cardiac rhythms and clinical status of a patient comprising:

a sampling device for obtaining physiological data from a subject comprising a one or more series of intervals;

a computer processing device configured for processing the physiological data from the subject into entropy data and producing a diagnostic result; and

an output device for rendering the diagnostic output.

19. The apparatus of claim 18, wherein the sampling device comprises an EKG machine.

20. The apparatus of claim 18, wherein producing the diagnostic output comprises running a regression model on the entropy data.

21. The apparatus according to claim 18, wherein the physiological data is representative of a cardiac rhythm and clinical status of the subject.